Mapping Paddy Rice Planting Area in Northeastern China Using Spatiotemporal Data Fusion and Phenology-Based Method

remote sensing

Article Mapping Paddy Rice Planting Area in Northeastern China Using Spatiotemporal Data Fusion and Phenology-Based Method

Qi Yin 1, Maolin Liu 1 , Junyi Cheng 1, Yinghai Ke 2,3,* and Xiuwan Chen 1,3

1 Institute of Remote Sensing and GIS, Peking University, No. 5 Yiheyuan Road, Haidian District, Beijing 100871, China 2 Beijing Laboratory of Water Security, Base of the State Key Laboratory of Urban Environment Process & Digital Modeling, Capital Normal University, Beijing 100089, China 3 Engineering Research Center of Earth Observation and Navigation (CEON), Ministry of Education of the People’s Republic of China, Beijing 100871, China * Correspondence: [email protected]; Tel.: +86-181-0108-1127

Received: 16 May 2019; Accepted: 16 July 2019; Published: 18 July 2019

Abstract: Accurate paddy rice mapping with fine spatial detail is significant for ensuring food security and maintaining sustainable environmental development. In northeastern China, rice is planted in fragmented and patchy fields and its production has reached over 10% of the total amount of rice production in China, which has brought the increasing need for updated paddy rice maps in the region. Existing methods for mapping paddy rice are often based on remote sensing techniques by using optical images. However, it is difficult to obtain high quality time series remote sensing data due to the frequent cloud cover in rice planting area and low temporal sampling frequency of satellite imagery. Therefore, paddy rice maps are often developed using few Landsat or time series MODIS images, which has limited the accuracy of paddy rice mapping. To overcome these limitations, we presented a new strategy by integrating a spatiotemporal fusion algorithm and phenology-based algorithm to map paddy rice fields. First, we applied the spatial and temporal adaptive reflectance fusion model (STARFM) to fuse the Landsat and MODIS data and obtain multi-temporal Landsat-like images. From the fused Landsat-like images and the original Landsat images, we derived time series vegetation indices (VIs) with high temporal and high spatial resolution. Then, the phenology-based algorithm, considering the unique physical features of paddy rice during the flooding and transplanting phases/open-canopy period, was used to map paddy rice fields. In order to prove the effectiveness of the proposed strategy, we compared our results with those from other three classification strategies: (1) phenology-based classification based on original Landsat images only, (2) phenology-based classification based on original MODIS images only and (3) random forest (RF) classification based on both Landsat and Landsat-like images. The validation experiments indicate that our fusion-and phenology-based strategy could improve the overall accuracy of classification by 6.07% (from 92.12% to 98.19%) compared to using Landsat data only, and 8.96% (from 89.23% to 98.19%) compared to using MODIS data, and 4.66% (from93.53% to 98.19%) compared to using the RF algorithm. The results show that our new strategy, by integrating the spatiotemporal fusion algorithm and phenology-based algorithm, can provide an effective and robust approach to map paddy rice fields in regions with limited available images, as well as the areas with patchy and fragmented fields.

Keywords: rice paddy; phenology-based algorithm; STARFM; time series; China

Remote Sens. 2019, 11, 1699; doi:10.3390/rs11141699 www.mdpi.com/journal/remotesensing Remote Sens. 2019, 11, 1699 2 of 24

1. Introduction Rice is one of the major staple foods worldwide and plays an essential role in supporting the growing population. Timely and efficient monitoring of paddy rice fields is a prerequisite for paddy rice growth monitoring, yield estimation, and agricultural resource management. In recent decades, paddy rice fields have expanded rapidly due to increasing population and food demand, especially in northeastern China [1,2], which is exerting pressures on cultivated lands, regional biodiversity and water resources. Monitoring the spatiotemporal dynamics of paddy rice fields is of great significance to food safety [3], water resources management [4], and ecosystem sustainability [5]. The rapid development of satellite remote sensing technology has greatly improved our ability to observe, monitor, and map paddy rice fields [6]. Compared to single time imagery used in traditional classification, time series imagery can provide more temporal information and help capture phenology information, thereby reducing the classification errors [7]. In recent years, phenology-based algorithms have been developed. For example, Zhang et al., 2017 successfully used MODIS LST product to define rice flooding/transplanting period, and then used MODIS-derived VI temporal profiles to analyze the spatiotemporal patterns of paddy rice fields from 2000 to 2015 [8]. Zhou et al., 2016 used the phenology-based method through analysis of Landsat and MODIS images to extract the paddy rice planting area from the rice-wetland coexistent area [9]. These algorithms extract phenology information and recognize the key phenology phase (e.g., flooding and transplanting phase, tillering) using spectral reflectance or vegetation indices at individual pixels from time series imagery. The most representative phenology-based paddy rice mapping method is the transplanting-based algorithm [8]. Paddy rice fields are a mixture of water and rice plants in the flooding and transplanting phase and thus have unique physical characteristics. This feature can be detected based on the relationship between the time series Land Surface Water Index (LSWI) [10] and Normalized Difference Vegetation Index (NDVI) [11] or Enhanced Vegetation Index (EVI) [12]. At present, the transplanting-based algorithm has been widely used for rice field mapping in many regions [7,13–15]. For example, Xiao et al. successfully applied the transplanting-based algorithm to paddy rice mapping and generated rice distribution maps in South China, South Asia and Southeast Asia based on MODIS data [13,16]. Zhang et al. obtained the paddy rice map of northeastern China in 2010 by using time series MODIS-derived VI data [17]. At present, MODIS data has been widely used in the transplanting-based algorithm to map paddy rice for its high revisit frequency [18]. However, its coarse spatial resolution (250 m–1 km) limits its suitability in monitoring small cropland patches with high spatial complexity. Especially in China where paddy rice is usually planted in small, patchy and fragmented fields, the low spatial resolution of MODIS imagery makes it extremely difficult to generate accurate paddy rice mapping [8]. The Landsat imagery is an alternative data source with 30 m spatial resolution [6]. However, because of its low temporal resolution (16 days), it is difficult to obtain enough cloud-free Landsat images for time series analysis. Therefore, neither the MODIS nor the Landsat alone is suitable for mapping fragmented paddy rice fields. One way to solve this problem is to use spatiotemporal fusion algorithms to generate high spatial-temporal-resolution Landsat-like imagery. Applying remote sensing spatiotemporal fusion algorithms is a flexible, inexpensive and effective way to generate images with both high temporal and high spatial resolutions. A typical fusion idea is to combine low-temporal, high-spatial resolution data (such as Landsat) with high-temporal, low-spatial resolution data (such as MODIS) to predict images. At present, many scholars have proposed various spatiotemporal fusion algorithms based on multi-source data [19–21], among which the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) proposed by Gao et al. [22] has been most widely used. Currently, spatiotemporal fusion predictions have demonstrated their ability and potential for crop-type and other land cover classifications [23–27]. For example, Zhu et al., 2017 fused Landsat and MODIS images and used SVM for crop type classification. They found that incorporating STARFM-predicted images for key dates helps reduce the classification error [23]. Li et al., 2015 fused Landsat TM and MODIS imagery using Remote Sens. 2019, 11, 1699 3 of 24

ESTARFM and used decision tree classification to map six crop types [28]. Wang et al., 2017 adopted similar strategy to map six energy crop types [24]. One recent study used several fused images on key dates to map paddy rice fields in Hunan, China by supervised random tree (RT) classifier [25]. However, it can be seen that most studies used image statistics-based approaches, e.g., the supervised classifiers like maximum likelihood classify (MLC) [27], support vector machine (SVM) [23,26], random forest [29], and unsupervised classifiers like iterative self-organizing data analysis technique (ISODATE) [30]. Those approaches usually rely on image statistics and/or training sample collection and/or visual interpretation, which is time-consuming, labor-intensive and region-dependent. The classification models trained for one region may not be suitable for other regions, which may make the classification results not always satisfying [9,24,31]. On the other hand, some studies used several predicted images only instead of constructing time series data throughout the growing season [25,32]. For paddy rice, clear quantitative representations of physical characteristics such as the flooding and transplanting signals of paddy rice and key phenology phases would be helpful [18]. However, the phenological characteristics or variations (e.g., from transplanting to tillering), and the temporal profile information cannot be well quantified without time series observations. Because of the unique phenological characteristics of paddy rice, time series Landsat and Landsat-like data reconstructed by spatiotemporal fusion method combined with phenology-based algorithm may have the potential to provide a new and feasible strategy for paddy rice mapping in areas with small, patchy and fragmented paddy rice fields. This study aims to present such a strategy. First, the time-series MODIS land surface temperature (LST) product was used to identify the plant growing season and key phenology phases in order to improve the selection of images at appropriate phenological stages. Second, time series Landsat and Landsat-like images during the growing season were constructed by the fusion of Landsat and MODIS imagery using STARFM. Third, an improved phenology-based algorithm with the aid of auxiliary information was proposed. Finally, the accuracy of the final paddy rice map was evaluated. To further evaluate the role of the strategy in rice mapping, the importance of fusion data in different time windows was also analyzed.

2. Study Area and Data

2.1. Study Area The Sanjiang Plain, located in Northeastern China, is an alluvial plain formed by the confluence of Heilongjiang River, Wusuli River and Songhua River. It has a humid and semi-humid continental climate region and belongs to the temperate zone. Annual precipitation is about 500–650 mm and mainly falls between June to October. The agriculture land is primarily cultivated with paddy rice, maize, bean and some vegetables. Rice seeds are usually sown in mid-April, and rice seedling plants are transplanted to paddy rice fields between mid-May and early June. Flooding is usually carried out about two weeks before rice transplanting, and it is a key practice in rice agriculture. Rice plants are mature in late September and harvested in early October. Corn and soybean are planted from mid-to-late May and are mature at almost the same time as rice. Other vegetation such as evergreen forests and wetlands usually have longer growing seasons than rice, corn and soybean. In recent years, the paddy rice planting area in the Sanjiang Plain has been increasing, resulting in a decrease in wetland area and a sharp increase in farmland [33]. It was reported that the rice production in Sanjiang Plain has reached 13% of the total amount of rice production in China [34]. We chose a part of the Sanjiang Plain as a pilot study area (Landsat Path/Row: 114/27). The study area is located between 46◦05033.7000–48◦02026.1200N, 132◦00039.1300–133◦08042.0300E, and has large areas of croplands, water bodies, buildings, forests, marshes (wetlands) and other land cover types. The paddy rice fields in the study are distributed in a scattered manner (Figure1). Remote Sens. 2019, 11, 1699 4 of 24 Remote Sens. 2019, 11, x FOR PEER REVIEW 4 of 24

Figure 1. The location of the study area and its digital elevation model (DEM). The DEM is from Figure 1. The location of the study area and its digital elevation model (DEM). The DEM is from Shuttle Radar Topography Mission (SRTM) 90 m Digital Elevation Data Version 4, obtained from Shuttle Radar Topography Mission (SRTM) 90 m Digital Elevation Data Version 4, obtained from http://www.srtm.csi.cgiar.org. Main rivers, the county boundary and Landsat scene extent (path/row http://www.srtm.csi.cgiar.org. Main rivers, the county boundary and Landsat scene extent (path/row 114/27) are highlighted. 114/27) are highlighted. 2.2. Data and Pre-Processing 2.2. Data and Pre-Processing 2.2.1. Landsat Data and Pre-Processing 2.2.1. Landsat Data and Pre-Processing In order to examine the availability of the Landsat data, the Google Earth Engine platform (GEE, https:In//earthengine.google.org order to examine the availability) was used of to the count Landsat the number data, the of all Google available Earth Landsat Engine images platform (Landsat (GEE, ETMhttps://earthengine.google.org)+, Landsat OLI) over China was [35]. used Figure to2 showscount thethe annual number average of all numbers available of Landsat imagesimages during(Landsat 2013–2018 ETM+, (FigureLandsat2 a,c)OLI) and over the totalChina number [35]. Figure of 2018 (Figure2 shows2c,d) the with annual a cloud average cover ofnumbers less than of 10%Landsat respectively. images during As illustrated, 2013–2018 there (Figure are few 2a,c) Landsat and imagesthe total available number especially of 2018 (Figure in thesouthern 2c,d) with and a northeasterncloud cover of regions, less than while 10% those respectively. regions are As the illustrated, main rice productionthere are few areas Landsat in China images (most available less than 10especially images). in Due the southern to the long and revisit northeastern period (16 regions, days) andwhile the those impacts regions from are clouds the main and rice rain, production the actual availableareas in China data is (most too few less to than meet 10 the images). requirement Due to of the the long phenology-based revisit period algorithm. (16 days) and the impacts from clouds and rain, the actual available data is too few to meet the requirement of the phenology- based algorithm.

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Figure 2. Availability of time series cloudless Landsat images in China. The annual average observation Figure 2. Availability of time series cloudless Landsat images in China. The annual average numbers during 2000–2018 for (a) Landsat 8 and (b) Landsat 7; the total observation numbers in 2018 observation numbers during 2000–2018 for (a) Landsat 8 and (b) Landsat 7; the total observation for (c) Landsat 8 and (d) Landsat 7. The study area is marked with red polygons. numbers in 2018 for (c) Landsat 8 and (d) Landsat 7. The study area is marked with red polygons. In the study, all available Landsat 7 (ETM+) and Landsat 8 (OLI) surface reflectance data (path/row: 114/27)In ofthe the study, study all area available with cloud Landsat cover less7 (ETM+) than 10% and in Landsat 2018 were 8 obtained,(OLI) surface except reflectance for the images data with(path/row: large areas114/27) of of snow the study cover inarea winter. with cloud The total cover number less than of qualified10% in 2018 images were is obtained, seven. All except Landsat for theimages images used with in thelarge study areas were of snow standard cover terrain-corrected in winter. The total (L1T), number radiation-corrected, of qualified images and is geometric-seven. All Landsatcorrected images images, used and in had the beenstudy atmospherically were standard correctedterrain-corrected to surface (L1T), reflectance radiation-corrected, using LEDAPS and (Landsatgeometric-corrected ETM+)[36 ]images, and Land and Surface had been Reflectance atmosphe Coderically (LaSRC) corrected (Landsat to surface OLI) [reflectance37]. All possible using LEDAPSbad observations (Landsat aETM+)ffected [36] by clouds,and Land cirrus Surface clouds Refl orectance ice/snow Code with (LaSRC) confidence (Landsat levels OLI) of 67–100%[37]. All werepossible excluded bad observations from Landsat affected images by usingclouds, the cirrus quality clouds assessment or ice/snow band with (QA). confidence The SLC-o levelsff strips of 67– in 100%the Landsat were excluded ETM + imagesfrom Landsat were classifiedimages using as “no the value”quality [assessment38]. Then, weband cropped (QA). The all imagesSLC-off tostrips the ingeographic the Landsat extent ETM of + theimages study were area. classified Finally, as Landsat “no value” images [38]. after Then, preprocessing we cropped were all images usedfor to the geographicSTARFM and extent phenology-based of the study area. classification Finally, (FigureLandsat3). images after preprocessing were used for the STARFM and phenology-based classification (Figure 3).

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Figure 3. Landsat images used in study after preprocessing (NIR, red and green bands) on the (a) April Figure 3. Landsat images used in study after preprocessing (NIR, red and green bands) on the (a) 1st, (b) April 25th, (c) May 19th, (d) May 27th, (e) July 6th, (f) August 7th, (g) September 16th, and h) April 1st, (b) April 25th, (c) May 19th, (d) May 27th, (e) July 6th, (f) August 7th, (g) September 16th, October 18th, and the cloud cover percentage of each image is shown in the figure. and h) October 18th, and the cloud cover percentage of each image is shown in the figure. 2.2.2. MODIS Data and Pre-Processing 2.2.2. MODIS Data and Pre-Processing The MOD09A1 Version 6 is an eight-day composite 500-m MODIS surface reflectance product. The MYD11A2The MOD09A1 Version Version 6 data can6 is providean eight-day eight-day compos averageite 500-m values MODIS of nighttime surface clear-sky reflectance LSTs. product. All the TheMODIS MYD11A2 products Version covering 6 data the studycan provide area were eight-day downloaded average from values the Unitedof nightti Statesme clear-sky Geological LSTs. Survey All (USGS)the MODIS and products re-projected covering to the the UTM-WGS84 study area Zonewere 53Ndownloaded projection. from Then, the allUnited images States were Geological cropped Surveyto keep (USGS) the same and size re-projected with Landsat to imagesthe UTM-WGS exactly.84 For Zone MOD09A1 53N projection. images, pixelsThen, contaminatedall images were by croppedclouds were to maskedkeep the based same on size the MODISwith Landsat quality flags.images The exactly. MYD11A2 For products MOD09A1 were images, used to definepixels contaminated by clouds were masked based on the MODIS quality flags. The MYD11A2 products phenology timing and crop calendar. The starting dates of temperatures above 0, 5 and 10 ◦C were were used to define phenology timing and crop calendar. The starting dates of temperatures above calculated and then the resultant maps of the starting date of stable temperatures above 0, 5 and 10 ◦C were0, 5 and generated 10 °C were (Figure calculated4). Finally, and allthen MOD09A1 the resultant images maps and of the the resultant starting date maps of were stable resampled temperatures to a abovespatial 0, resolution 5 and 10 °C of 30were m usinggenerated the nearest (Figure neighbor 4). Finally, interpolation all MOD09A1 to beimages spatially and consistentthe resultant with maps the wereused Landsatresampled images. to a spatial resolution of 30 m using the nearest neighbor interpolation to be spatially consistent with the used Landsat images. 2.3. Additional Datasets 2.3. Additional Datasets Ancillary datasets including elevation data and forest map were used to improve paddy rice classification.Ancillary Thedatasets elevation including data waselevation Shuttle data Radar and Topography forest map were Mission used (SRTM) to improve Digital paddy Elevation rice classification.Data Version 4 The from elevation the CGIAR-CSI data was SRTM Shuttle 90 m Radar Database Topography (http://srtm.csi.cgiar.org Mission (SRTM)) and Digital the forest Elevation map wasData obtained Version from4 from ALOS the /CGIAR-CSIPALSAR-based SRTM fine 90 resolution m Database (25-m) (http://srtm.csi.cgiar.org) global forest map (https: and//www.eorc. the forest mapjaxa.jp /wasALOS obtained/en/palsar_fnf from/data ALOS/PALSAR-based/index.htm)[39]. In addition, fine resolution the phenology (25-m) calendar global of majorforest plantsmap (https://www.eorc.jaxa.jp/ALOS/en/pin the study area acquired from the Ministryalsar_fnf/data/index.htm) of Agriculture and Rural[39]. AInffairs addition, of the People’s the phenology Republic calendarof China (ofhttp: major//www.moa.gov.cn plants in the study) was area used acquired to validate from the the accuracy Ministry ofof theAgriculture temperature-defined and Rural Affairs plant ofgrowing the People’s season Republic and flooding of China and (http://www.moa. transplanting periodgov.cn) of paddy was used rice. to validate the accuracy of the temperature-definedReference samples plant were growing visually season interpreted and flooding from (1)and high transplanting resolution period image of collections paddy rice. from GoogleReference Earth, andsamples (2) the were 311 visually geo-referenced interpreted field from photos (1) mostlyhigh resolution collected image in May collections 2017 from from the GoogleGlobal Geo-ReferencedEarth, and (2) the Field 311 Photo geo-referenced Library (http: fiel//dwww.eomf.ou.edu photos mostly collected/photos in/). May The geo-referenced2017 from the Globalfield photos Geo-Referenced were converted Field into Photo points Library of interest (http://www.eomf.ou.edu/photos/). (POIs) in kmz format for displaying The ingeo-referenced Google Earth. fieldIn those photos photos, were rice converted fields were into usually points farmlandof interest covered (POIs) byin kmz water. format A total for of displaying 711 vector in polygons Google (AreasEarth. In of Interest,those photos, AOIs) rice were fiel drawnds were around usually the farmland acquired POIscovered by referringby water. to A both total the of field 711 photosvector polygons (Areas of Interest, AOIs) were drawn around the acquired POIs by referring to both the field photos and the Google’s historical high-resolution images, including paddy rice (347 AOIs, 9997

Remote Sens. 2019, 11, 1699 7 of 24 and the Google’s historical high-resolution images, including paddy rice (347 AOIs, 9997 pixels) and Remote Sens. 2019,, 11,, xx FORFOR PEERPEER REVIEWREVIEW 7 of 24 non-paddy rice (332 AOIs, 9908 pixels) (Figure5). The area of paddy rice AOIs (900 ha) is close to non-paddypixels) rice and AOIs non-paddy (892 ha). rice (332 Those AOIs, AOIs 9908 contained pixels) (Figure only one5). The kind area of of land paddy cover rice andAOIs their (900 boundariesha) is kept aclose distance to non-paddy of over rice 30 AO mIs awayIs (892(892 ha). fromha). ThoseThose the AOIsAOIs boundaries containedcontained of onon otherlyly oneone land kindkindcover ofof landland types. covercover andand The theirtheir detailed non-paddyboundaries rice AOIskept a generation distance of over method 30 m was away as fr follows:om the boundaries the non-paddy of other rice land land cover cover types. types The were detailed non-paddy rice AOIs generation method was as follows: the non-paddy rice land cover types divideddetailed into ﬁvenon-paddy major rice types: AOIs upland generation crop, method forest, was wetland as follows:/swamp, the non-paddy water body,rice land and cover built-up types land, were divided into five major types: upland crop,, forest,forest, wetland/swamp,wetland/swamp, waterwater body,body, andand built-upbuilt-up according to the auxiliary data in Section 2.3 and the paddy rice map from Qin et al. (2015) [3]. Random land,land, accordingaccording toto thethe auxiliaryauxiliary datadata inin SectionSection 2.32.3 andand thethe paddypaddy ricerice mapmap fromfrom QinQin etet al.al. (2015)(2015) [3].[3]. pointsRandom in the study points areain the were study generated area were generated in each stratum in eacheach stratumstratum by referring byby referringreferring to the totohigh-resolution thethe high-resolutionhigh-resolution images from Googleimagesimages Earthfromfrom GoogleGoogle and ﬁeld EarthEarth photos, andand fifi andeld photos, then non-rice and then AOIs non-rice were AOIs generated were aroundgenerated the around random the points with therandom same points method with as the paddy same method rice AOIs. as paddy The rice Google AOIs. historical The Google images historical used images in the used AOI in the drawing processAOI were drawing mainly process in 2016–2018. were mainly in 2016–2018.

FigureFigure 4. The 4. Spatial-temporal The Spatial-temporal changes changes of nighttimeof nighttime LST LST derived derived from from MYD11A2MYD11A2 data data in in 2018. 2018. (a ())a ) The ≥ ℃ ≥ ℃ first dateThe withfirst date nighttime with nighttime LST 0 LST◦C; (≥b )00 the ℃;; first ((b)) thethe date firstfirst with datedate nighttime withwith nighttimenighttime LST LSTLST5 ◦ C;≥ 5 (5c )℃ the;; ((c)) first thethe firstfirst date with date with nighttime LST ≥≥ 10 ℃; (d) the end date with nighttime LST ≥ 0≥ ℃; (e) the end date with nighttimedate LSTwith nighttime10 ◦C; ( dLST) the ≥ end10 ℃ date; (d) with the end nighttime date with LST nighttime0 ◦C; LST (e) the≥ 0 end℃; ( datee) the with end date nighttime with LST ≥ ≥ ℃ ≥≥ ℃ 5 C;nighttime (f) the end LST date ≥ 55 with℃;; ((ff)) nighttime thethe endend datedate LST withwith nighttimenighttime10 C. LSTLST ≥ 1010 ℃.. ≥ ◦ ≥ ◦

Figure 5. Spatial distribution of ﬁeld photos in the study area is shown in the left panel and part of the validation area of interest (AOIs) in the right panel. Remote Sens. 2019, 11, x FOR PEER REVIEW 8 of 24

Figure 5. Spatial distribution of field photos in the study area is shown in the left panel and part of Remote Sens. 2019, 11, 1699 8 of 24 the validation area of interest (AOIs) in the right panel.

3.3. Method Method TheThe fusionfusion- and and phenology-based phenology-based paddy paddy rice rice mappi mappingng methodology methodology (Figure (Figure 66)) mainlymainly involved thethe followingfollowing steps: steps: (1) (1) STARFM STARFM prediction prediction by using by imagesusing afterimages pre-processing after pre-processing (Landsat ETM (Landsat+/OLI, ETM+/OLI,MODIS); (2) MODIS); Calculation (2) Calculation of time series of VIs,time namelyseries VIs, EVI, namely NDVI, EVI, and NDVI, LSWI; (3)and Mapping LSWI; (3) paddy Mapping rice paddyﬁelds using rice fields the phenology-based using the phenology-based algorithm; andalgorithm; (4) Accuracy and (4) Assessment. Accuracy Assessment.

FigureFigure 6. OverviewOverview of of the the methodology for for fusion-and phenology-based paddy paddy rice rice mapping using thethe Landsat Landsat data, data, MODIS MODIS data data and and other other ancillary data. 3.1. STARFM Prediction 3.1. STARFM Prediction STARFM is the most widely used spatiotemporal fusion model, which has stable and reliable STARFM is the most widely used spatiotemporal fusion model, which has stable and reliable performance in predicting fine resolution images [40,41]. In the study, STARFM was used to fuse performance in predicting fine resolution images [40,41]. In the study, STARFM was used to fuse Landsat and MODIS surface reflectance data to generate Landsat-like images. The algorithm was Landsat and MODIS surface reflectance data to generate Landsat-like images. The algorithm was proposed based on two assumptions: (1) if MODIS and Landsat surface reflectances are equal on date proposed based on two assumptions: (1) if MODIS and Landsat surface reflectances are equal on date t , then they should be equal at date t ; (2) if no change happens in the MODIS surface reflectance, k, then they should be equal at date 0 ; (2) if no change happens in the MODIS surface reflectance, then no change should be predicted at the Landsat spatial resolution [22]. STARFM takes into account then no change should be predicted at the Landsat spatial resolution [22]. STARFM takes into account the spectral and spatial similarities between pixels, using one or two base pairs of Landsat and MODIS the spectral and spatial similarities between pixels, using one or two base pairs of Landsat and images at date t and one MODIS image for the prediction date t to predict a synthetic Landsat-like MODIS images kat date and one MODIS image for the prediction0 date to predict a synthetic image with a 30 m spatial resolution at the prediction date [22]. In order to minimize the effect of Landsat-like image with a 30 m spatial resolution at the prediction date [22]. In order to minimize the pixel outliers, the algorithm uses a moving window from fine-resolution images (Landsat) to search effect of pixel outliers, the algorithm uses a moving window from fine-resolution images (Landsat) for similar pixels by using spatial, temporal and spectral information. Then, the surface reflectance to search for similar pixels by using spatial, temporal and spectral information. Then, the surface for the central pixels of the window at date t is computed with a weighting function by introducing reflectance for the central pixels of the window0 at date is computed with a weighting function by additional information of similar pixels as follows: introducing additional information of similar pixels as follows: Xw Xw Xn L(xω yω,, t=) =∑∑∑ ×(W ( (,M,(x ), y+, t ) + L(x,ω −(y ω , t ) M,(,x ,)y , t )), (1) 2,, 2 0 ijk i i 0 , 2, 2 k i ),i k (1) i=1 j=1 k=1 × −

where wis is the the searching searching window window size, size,( xω , y,ω )is is the the central central pixel pixel of of the the moving moving window, window, and andW ijkis 2 2 isthe the weight weight to to decide decide the the contribution contribution of of the the similar similar pixels pixels toto thethe estimatedestimated reflectancereflectance of the central central pixel. L(x ω y ω , t ) is the predicted surface reflectance of pixel (x ω y ω ) with the fine resolution at , 0 , pixel. 2, 2, is the predicted surface reflectance of pixel 2 , 2 with the fine resolution at date t0. M(xi,yi, t0), M(xi, yi, tk) are the surface reflectances of MODIS pixel (xi, yi) at date t0 and tk,

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( ,, ) ( ,, ) ( ,) dateRemote Sens.. 2019 , 11 , 1699 , are the surface reflectances of MODIS pixel at date 9and of 24 , respectively. , , is the surface reflectance of Landsat pixel , at date . More detailed descriptions of the algorithm can be found in Gao et al., 2006 [22]. respectively. L(x ω , y ω , tk) is the surface reflectance of Landsat pixel (x ω , y ω ) at date tk. More detailed In the study,2 the2 MODIS images used in STARFM were selected 2based2 on the date of available descriptions of the algorithm can be found in Gao et al., 2006 [22]. Landsat images to minimize the time interval between the base pair. In addition, the time interval In the study, the MODIS images used in STARFM were selected based on the date of available between the acquisition date ( ) and the prediction date ( ) was also taken into consideration for Landsat images to minimize the time interval between the base pair. In addition, the time interval more precise fine images. In order to capture spectral characteristics during key phenology phases between the acquisition date (t ) and the prediction date (t ) was also taken into consideration for (e.g., rice flooding and transplantingk period) and remove noises0 from other vegetation and crops, we more precise fine images. In order to capture spectral characteristics during key phenology phases tried to make Landsat and Landsat-like images distributed evenly during the growing season. (e.g., rice flooding and transplanting period) and remove noises from other vegetation and crops, Finally, the STARFM-predicted images combined with the real Landsat images were assembled into we tried to make Landsat and Landsat-like images distributed evenly during the growing season. a time series dataset (hereafter referred to as ‘fused time series data’) ready for phenology-based Finally, the STARFM-predicted images combined with the real Landsat images were assembled into paddy rice mapping. The Landsat and MODIS images used in STARFM and the Landsat and a time series dataset (hereafter referred to as ‘fused time series data’) ready for phenology-based STARFM-predicted images used in the phenology-based algorithm are shown in Figure 7. paddy rice mapping. The Landsat and MODIS images used in STARFM and the Landsat and STARFM-predicted images used in the phenology-based algorithm are shown in Figure7.

Figure 7. The details of images (MODIS and Landsat) used in the study, including the data used for the STARFM and phenology-based algorithm. Figure 7. The details of images (MODIS and Landsat) used in the study, including the data used for 3.2. Fusedthe STARFM Time Series and phenology-based NDVI and EVI and algorithm. LSWI Data

3.2. FusedIn the Time phenology-based Series NDVI and algorithm, EVI and LSWI the paddy Data rice fields can be identified by its unique physical characteristics. The characteristics can be detected by using the relationship between the time-series LSWI,In NDVIthe phenology-based and EVI [13,16, 42algorithm,]. The three the indices paddy arerice widely fields can used be in identified the study by of its vegetation unique physical canopy, characteristics.biomass and phenology. The characteristics Therefore, can for be each detected image by in using the fused the relationship time series between data, we the calculated time-series the LSWI, NDVI and EVI [13,16,42]. The three indices are widely used in the study of vegetation canopy, NDVI, EVI, and LSWI using the surface reflectance values of the blue band (ρblue), red band (ρred), biomass and phenology. Therefore, for each image in the fused time series data, we calculated the NIR band (ρNIR) and SWIR band (ρSWIR). The functions are as follows: NDVI, EVI, and LSWI using the surface reflectance values of the blue band (), red band (), NIR band ( ) and SWIR band ( ). The functionsρNIR areρred as follows: NDVI = − , (2) ρNIR+ ρred NDVI = , (2) ρNIR ρ = − red EVI 2.5 , (3) EVI = 2.5× ×ρNIR + 6 ρred 7.5 ρ,blue + 1 (3) ××.×− × ρNIR ρSWIR LSWI =− , (4) LSWI = ρNIR + ρSWIR, (4) In addition, we calculated the difference between LSWI and EVI (LSWI EVI) and the difference In addition, we calculated the difference between LSWI and EVI (LSWI −− EVI) and the difference LSWI and NDVI (LSWI NDVI) for each image in the fused time series data. Finally, for comparison LSWI and NDVI (LSWI −− NDVI) for each image in the fused time series data. Finally, for comparison purposes, we also obtained another two sets of VIs (EVI, NDVI, and LSWI) data calculated from the purposes, we also obtained another two sets of VIs (EVI, NDVI, and LSWI) data calculated from the Landsat only dataset and the MODIS only dataset, respectively. Landsat only dataset and the MODIS only dataset, respectively. 3.3. Identification of Paddy Rice Fields 3.3. Identification of Paddy Rice Fields 3.3.1. Algorithms to Identify Inundation/Flooding Signals 3.3.1. Algorithms to Identify Inundation/Flooding Signals In the study, only paddy rice, natural wetlands, and some vegetation (e.g., grass, trees, and shrubs growing on the edge of water bodies) have flooding/inundation events/signals. Previous

Remote Sens. 2019, 11, x FOR PEER REVIEW 10 of 24

RemoteIn Sens. the2019 study,, 11, 1699 only paddy rice, natural wetlands, and some vegetation (e.g., grass, trees,10 ofand 24 shrubs growing on the edge of water bodies) have flooding/inundation events/signals. Previous works have proved that LSWI-NDVI ≥ 0 or LSWI-EVI ≥ 0 coincide with flooding events [13,16,42]. works have proved that LSWI-NDVI 0 or LSWI-EVI 0 coincide with flooding events [13,16,42]. Generally, the pixels identified as flooded≥ are a mixture of≥ water and plants during the whole year [9]. Generally,For paddy the rice, pixels this identifiedphenomenon as flooded can be found are a mixture in Figu ofre water8. Paddy and rice plants is the during only thecrop whole that needs year [ 9to]. Forbe transplanted paddy rice, this into phenomenon a mixed environment can be found of water in Figure and8. soil. Paddy Before rice istransplanting, the only crop paddy that needs rice tofields be transplantedare covered with into awater. mixed After environment transplanting, of water as andrice soil.plants Before grow transplanting, in flooded fields, paddy rice rice fields fields are are a coveredmixture withof rice water. plants, After soil transplanting, and water (open-canopy as rice plants stage). grow inThen, flooded rice fields,plantsrice continue fields areto grow a mixture until ofplants rice plants,canopy soilcompletely and water covers (open-canopy the fields. In stage). the open-canopy Then, rice plantsstage, the continue indices to of grow paddy until rice plants fields canopyhave unique completely temporal covers characteristics: the fields. In the the LSWI open-canopy values of stage, paddy the fields indices are of higher paddy than rice their fields NDVI have uniqueand EVI temporal values. This characteristics: phenomenon the is LSWI called values flooding of paddy events/signals fields are (Figure higher than8). their NDVI and EVI values. This phenomenon is called flooding events/signals (Figure8).

Figure 8. The temporal proﬁle of paddy rice VIs (NDVI, EVI, and LSWI) at the AOIs, the nighttime

LST,Figure and 8. theThe crop temporal calendar profile are of shown paddy in rice the VIs figure. (NDVI, The EVI, nighttime and LSWI) LST first at the over AOIs, 0 ◦C, the 5 ◦nighttimeC, 10 ◦C areLST, marked. and the crop calendar are shown in the figure. The nighttime LST first over 0 ℃, 5 ℃, 10 ℃ are marked. Paddy rice transplanting date varies in different regions. Identification of the growing season and the floodingPaddy rice/transplanting transplanting period date ofvaries paddy in different rice could regions. help us Identification select images of at the appropriate growing season times and for betterthe flooding/transplanting distinguishing paddy period ricefrom of paddy other rice vegetation could help types us [select10,14, 31images], and at improve appropriate the e timesfficiency for ofbetter paddy distinguishing rice field detection. paddy rice Crops from needother certainvegetation accumulated types [10,14,31], temperatures and improve to complete the efficiency their life of cycle,paddy so rice it isfield more detection. reliable Crops to use need nighttime certain LST accumu as a physicallated temperatures indicator forto complete biophysical their limitation life cycle, [ 7so]. Hence,it is more the reliable nighttime to use MODIS nighttime LST LST data as was a physical used to indicator determine for biophysical the growing limitation season and [7]. theHence, paddy the ricenighttime flooding MODIS/transplanting LST data of thewas study used area.to determine First, the firstthe andgrowing last datesseason of nighttimeand the paddy LST larger rice thanflooding/transplanting 0 ◦C were extracted of the as thestudy start area. and First, end the dates first ofand the last plant dates growing of nighttime season LST of larger the study than area0 ℃ (Figurewere extracted4a,d). Second, as the start according and end to dates previous of the studies, plant growing the start season date ofof nighttimethe study area LST (Figure above 54a,d).◦C orSecond, 10 ◦C according was determined to previous as the studies, likely start the datestart ofdate flooding of nighttime and transplanting LST above (SOF)5 °C [or10 ,1031 ].℃ Then, was bydetermined comparing as the the temporal likely start profiles date of flooding VIs and nighttime and transplanting LST (Figure (SOF)8, the [10,31]. flooding Then, signals by comparing of paddy ricethe fieldstemporal mainly profiles occur of before VIs and the nighttime date that LSWILST (Fig andure EVI 8, curvesthe flooding cross), signals the start of datepaddy of nighttimerice fields LSTmainly over occur 5 ◦C before was close the date to the that SOF LSWI in the and study EVI (Figurecurves 4cross),b). The the flooding start date signals of nighttime of paddy LST rice over can 5 last°C was for aboutclose to two the months SOF in the after study transplanting (Figure 4b). until The rice flooding canopy signals almost of paddy covers rice the entirecan last fields for about [13], sotwo the months length after of the transplanting flooding/transplanting until rice canopy phase almost was SOF covers+60. the The entire phenology fields [13], stages so the recorded length by of thethe phenologyflooding/transplanting calendar matched phase wellwas withSOF+60. the temperature-definedThe phenology stages plant recorded growing by seasonthe phenology and the temperature-defined flooding and transplanting period of paddy rice (Figure8).

Remote Sens. 2019, 11, 1699 11 of 24

Considering that NDVI is limited under a closed canopy and soil background [43], and EVI is more sensitive to NIR and more robust to biomass variation [44], we marked the pixels with flooding signals by using rule LSWI-EVI 0 for each image during the flooding and transplanting period. ≥ Then we assumed pixels with flooding signals to be “potential or likely” paddy rice pixels and got a preliminary paddy rice map.

3.3.2. Implementation of Phenology-Based Paddy Rice Mapping Algorithm Inﬂuenced by factors such as atmospheric conditions and other land cover types that have some similar physical characteristics to paddy rice, the preliminary paddy rice map was inevitably polluted by noises. Therefore, according to the phenology characteristics and the temporal proﬁles of VIs of other land cover types in the study area (Figure9)[ 3,9,14,31], masks were established to remove these noises as follows:

1. Natural vegetation. Forests, grasslands and shrubs grow earlier and are greener than paddy rice, so the EVI values of natural vegetation are higher than the EVI values of paddy field before the rice plants begin to grow. We identified the pixels with maximum EVI value 0.30 before the ≥ mid-flooding/transplanting period (corresponding to the images before May 29 in the study) as natural vegetation and generated a preliminary mask of natural vegetation. Then we combined the preliminary mask with the ALOS/PALSAR-based fine resolution (25 m) forest map (2017) to get a final natural vegetation mask. 2. Sloping land. Rice plants grow in water and therefore cannot be planted in sloping land. The sloping land mask was generated based on the rule of slope 3 to exclude the areas with ≥ ◦ low probabilities of growing paddy rice by using the SRTM DEM data. 3. Sparse vegetation. There are some low-vegetated lands in the field roads, built-up land, water edge and other areas. Those low-vegetated lands have very low greenness within the entire growing season (Figure9 and Figure 11). Thus, pixels with the maximum EVI value 0.60 within the ≤ plant growing season (nighttime LST > 0 ◦C) were labeled as sparse vegetation. In addition, the preliminary paddy rice map had obvious strips. That was mainly because the EVI values of paddy rice pixels reached their maximum from July to August, while the images in July all contained SLC-off gaps from Landsat ETM+. Therefore, the maximum EVI values of paddy rice pixels in the strip region were mostly replaced by the paddy rice EVI values from images in September, and those paddy rice pixels were misclassified into sparse vegetation. Referring to the dates of the Landsat ETM+ images in the fused time series data, pixels in the strip region with the maximum EVI value 0.55 within the plant growing season (nighttime LST > 0 C) were ≤ ◦ classified as sparse vegetation. 4. Open canopies in permanent flooding areas, such as vegetation (grass, trees, shrubs) growing on the edge of water bodies. Pixels in the open canopies are a mixture of natural vegetation and water and have flooding signals. Therefore, it is necessary to distinguish open canopies in permanent flooded areas from open canopies in seasonally flooded areas. Unlike seasonal flooding areas such as rice fields, permanent flooded areas usually have flooding signals throughout the entire growing season. Therefore, if a pixel had the flooding signal for all images within the entire growing season, then it was marked as a permanent flooded canopy. 5. Natural wetlands. There are natural wetlands in the Sanjiang Plain due to long-term waterlogging. When the temperature rises above 0 ◦C, natural wetlands start to flood due to snowmelt. Therefore, wetland vegetation has grown a few weeks before the flooding signals appear in paddy rice fields. The difference in EVI values between the wetland vegetation and paddy rice reaches to the maximum around the middle of June (Figures9 and 10). Therefore, if a pixel with flooding signals had maximum EVI value 0.30 between LST > 0 C and mid-June after excluding the ≥ ◦ masks described above, then it was classified as natural wetlands. Remote Sens. 2019, 11, x FOR PEER REVIEW 11 of 24 calendar matched well with the temperature-defined plant growing season and the temperature- defined flooding and transplanting period of paddy rice (Figure 8). Considering that NDVI is limited under a closed canopy and soil background [43], and EVI is more sensitive to NIR and more robust to biomass variation [44], we marked the pixels with flooding signals by using rule LSWI-EVI ≥ 0 for each image during the flooding and transplanting period. Then we assumed pixels with flooding signals to be “potential or likely” paddy rice pixels and got a preliminary paddy rice map.

3.3.2. Implementation of Phenology-Based Paddy Rice Mapping Algorithm Influenced by factors such as atmospheric conditions and other land cover types that have some similar physical characteristics to paddy rice, the preliminary paddy rice map was inevitably polluted by noises. Therefore, according to the phenology characteristics and the temporal profiles of VIs of other land cover types in the study area (Figure 9) [3,9,14,31], masks were established to remove these Remote Sens. 2019, 11, 1699 12 of 24 noisesRemote Sens.as follows: 2019, 11, x FOR PEER REVIEW 12 of 24

plant growing season (nighttime LST > 0 °C) were labeled as sparse vegetation. In addition, the preliminary paddy rice map had obvious strips. That was mainly because the EVI values of paddy rice pixels reached their maximum from July to August, while the images in July all contained SLC-off gaps from Landsat ETM+. Therefore, the maximum EVI values of paddy rice pixels in the strip region were mostly replaced by the paddy rice EVI values from images in September, and those paddy rice pixels were misclassified into sparse vegetation. Referring to the dates of the Landsat ETM+ images in the fused time series data, pixels in the strip region with the maximum EVI value ≤ 0.55 within the plant growing season (nighttime LST > 0 °C) were classified as sparse vegetation. 4. Open canopies in permanent flooding areas, such as vegetation (grass, trees, shrubs) growing on the edge of water bodies. Pixels in the open canopies are a mixture of natural vegetation and water and have flooding signals. Therefore, it is necessary to distinguish open canopies in permanent flooded areas from open canopies in seasonally flooded areas. Unlike seasonal flooding areas such as rice fields, permanent flooded areas usually have flooding signals throughout the entire growing season. Therefore, if a pixel had the flooding signal for all images within the entire growing season, then it was marked as a permanent flooded canopy. 5. Natural wetlands. There are natural wetlands in the Sanjiang Plain due to long-term waterlogging. When the temperature rises above 0 ℃, natural wetlands start to flood due to snowmelt. Therefore, wetland vegetation has grown a few weeks before the flooding signals appear in paddy rice fields. The difference in EVI values between the wetland vegetation and Figure 9. The seasonal dynamics of NDVI, EVI and LSWI for the major land cover types at select sites, Figurepaddy 9. rice The reaches seasonal todynamics the maximum of NDVI, around EVI and the LSWI middle for the of major June land (Figures cover 9types and at 10). select Therefore, sites, if extracted from the fused time series data. (a) Forest, (b) paddy, (c)water, (d)upland crops, (e)built-up extracteda pixel with from flooding the fused signals time series had data. maximum (a) Forest, EVI (b )value paddy,≥ ( 0.30c)water, between (d)upland LST crops, > 0 °C ( eand)built-up mid-June land, and (f) wetland. All sites were selected according to ﬁeld photos and high spatial resolution land,after andexcluding (f) wetland. the masks All sites described were selected above, according then it wasto field classified photos asand natural high spatial wetlands. resolution Google Earth images. Google Earth images.

Finally,Finally, allall ofof thethe pixelspixels inin thethe aboveabove maskmask werewere excludedexcluded fromfrom thethe preliminarypreliminary paddypaddy ricerice mapmap andand obtainobtain aa ﬁnalfinal paddypaddy ricerice mapsmaps (hereafter(hereafter referredreferred toto asas ‘the‘the fusion-basedfusion-based paddypaddy ricerice map’).map’). 3.4. Evaluation of Fusion-and Phenology-Based Paddy Rice Map Strategy 3.4. Evaluation of Fusion-and Phenology-Based Paddy Rice Map Strategy 3.4.1. Evaluation of STARFM Prediction 3.4.1. Evaluation of STARFM Prediction We evaluated the accuracy of STARFM predictions by comparing the STARFM-predicted images with theWe realevaluated Landsat the images. accuracy The imagesof STARFM used as predictions input data andby validationcomparing data the areSTARFM-predicted shown in Table1. Forimages example, with the the real Landsat Landsat image images. on 27 The May, images the MODIS used as image input ondata 17–25 and Mayvalidation were useddata are to predictshown thein Table Landsat-like 1. For example, image on the 19 Landsat May, and image then on the 27 Landsat-like May, the MODIS image image was comparedon 17–25 May with were the original used to Landsatpredict the image Landsat-like on 19 May image for evaluation. on 19 May, and then the Landsat-like image was compared with the original Landsat image on 19 May for evaluation.

Table 1. Images used in the STARFM for assessment.

Remote Sens. 2019, 11, 1699 13 of 24

Table 1. Images used in the STARFM for assessment.

Prediction Test Input Landsat tk Input MODIS tk Input MODIS t0 Validation Landsat t0 1 19/05/2018 17/05/2018 25/05/2018 27/05/2018 2 27/05/2018 25/05/2018 17/05/2018 19/05/2018 3 06/07/2018 04/07/2018 05/08/2018 07/08/2018

We selected three groups of data to evaluate the accuracy of STARFM-predicted images in different situations, including (1) using one fine resolution image to predict the Landsat-like image of the latter date (Prediction Tests 1 and 3), and (2) using one fine resolution image to predict the Landsat-like image of the previous date (Prediction Test 2). In addition, the time interval between date tk and date t0 was also taken into consideration: the time interval of the Prediction Test 3 was close to one month, which was much longer than Prediction Test 1 and Prediction Test 2 (eight days). We employed the root mean square error (RMSE), correlation coefficient (r) and absolute average error (AAD) of predicted reflectance compared with real reflectance (including blue, green, red, NIR, SWIR1 and SWIR2 bands) to quantitatively assess the accuracy. AAD can reflect the difference between fusion images and real images. The closer the AAD is to zero, the better the prediction is. r can well detect the consistency between fusion images and real images. The closer the r is to one, the more similar the fusion images are to the real images. RMSE can well measure the deviation of fusion images from real images. The smaller the RMSE, the better the prediction. The functions are shown in Formulas (5)–(7). 1 XN AAD = (xi yi) , (5) N i=1 | − | P cov(x, y) = (xi x) (yi y) r = = i 1 − ∗ − , (6) p qhP i hP i Var[x]Var[y] (x x)2 (y y)2 i=1 i − ∗ i=1 i − r XN 2 RMSE = (xi yi) /N, (7) i=1 − where N is the number of pixels involved in the calculation, x is the value of each pixel in fusion images, y is the value of each pixel in the corresponding real image, x, y are the mean values of x and y, respectively. Moreover, the RMSE, r, AAD between fused NDVI/LSWI/EVI values and Landsat NDVI/LSEI/EVI values were calculated to assess the performance of the key VIs used in the study.

3.4.2. Comparison with Other Classification Results For comparison purposes, we used the same phenology-based strategies to generate two additional maps from Landsat images only and from MODIS time series images, respectively. We also compared the fusion-and phenology-based strategy with the random forest (RF) classification method. RF is a well-known, widely used learning-based algorithm. In this study, RF was chosen for its stability since it is not sensitive to noise. Then RF was employed based on the same set of features, i.e., time series NDVI, EVI, LSWI and LSWI-EVI from each of the 13 Landsat and Landsat-like images. For the RF classifier, we generated ROIs for all land cover types (i.e., paddy rice, upland crop, forest, wetland/swamp, water body, and built-up land) in the same way described in Section 2.3 to support classification (16,371 pixels). Two parameters were defined as follows: the number of the trees was set to 200, and the number of the random variable to split each node of the individual tree was set to eight. First, 200 bootstrap samples were drawn from the 2/3 of the training data set. The remaining 1/3 of the training data called out-of-bag data was used for evaluating the performance of RF. Then, the decision trees were constructed based on randomly selected features of the bootstrapped sample. Finally, the class label of each pixel was determined by the majority voting of the decision trees. The classification result was divided into two categories as well: paddy rice and others. At the same time, the feature importance (namely variable importance in RF) was obtained, which allowed us Remote Sens. 2019, 11, 1699 14 of 24 to analyze the importance of each image and different time windows. The feature importance was estimatedRemote Sens. by 2019 the, 11 degradation, x FOR PEER REVIEW of model prediction, caused by the random permutation [45]. Then 14 we of 24 used the same set of reference samples as in the accuracy assessment of the phenology-based paddy ricerice map map to to evaluate evaluate the the four four classification classification results results by by comparing comparing the the map map class class and and reference reference class class at at thethe reference reference sample sample locations. locations. The The number number of of samples samples for for training training the the models models in in RF RF was was close close to to the the numbernumber of of samples samples for for accuracy accuracy assessment. assessment.

4.4. Result Result

4.1.4.1. Accuracy Accuracy of of the the STARFM STARFM Predictions Predictions ScatterScatter density density plots plots in in Figure Figure 11 11 show show the the relationships relationships of of the the VIs VIs between between the the predicted predicted and and thethe actual actual values values of of PredictionPrediction TestTest 11/2/3./2/3. All the the data data in in the the scatter scatter density density plots plots fall fall close close to tothe the 1:1 1:1line. line. Therefore, Therefore, all the all theresults results show show that thatthe STARFM the STARFM algorithm algorithm can predict can predict the fine the resolution ﬁne resolution images imageswith high with accuracy high accuracy and andreliably reliably express express the thespectral spectral information information of of the the Landsat Landsat imageimage inin the the correspondingcorresponding period. period.

FigureFigure 11. 11.Scatter Scatter density density plots plots of of the the real real VIs VIs and and the the predicted predicted ones ones by by the the STARFM STARFM in in (a ()a Prediction) Prediction TestTest 1, 1, (b ()b Prediction) Prediction Test Test 2, 2, (c )(c Prediction) Prediction Test Test 3. 3. Tables2 and3 list the results of AAD, RMSE and r calculations on the six bands and VIs between Tables 2 and 3 list the results of AAD, RMSE and r calculations on the six bands and VIs between the predicted images and the real images. For reﬂectance (six bands), the RMSE values vary from 0.01 the predicted images and the real images. For reflectance (six bands), the RMSE values vary from 0.01 to 0.08, r values range from 0.66 to 0.93, and AAD values vary from 0.01 to 0.05; for VIs, the RMSE to 0.08, r values range from 0.66 to 0.93, and AAD values vary from 0.01 to 0.05; for VIs, the RMSE values vary from 0.05 to 0.27, r values range from 0.71 to 0.96, and AAD values vary from 0.03 to values vary from 0.05 to 0.27, r values range from 0.71 to 0.96, and AAD values vary from 0.03 to 0.16. 0.16. Generally, the comparison between real Landsat and STARFM-predicted images show high Generally, the comparison between real Landsat and STARFM-predicted images show high correlations and small biases, both for reﬂectance (six bands) and VIs. The fusion image of Prediction correlations and small biases, both for reflectance (six bands) and VIs. The fusion image of Prediction Test 3 shows greater errors and lower correlations than the fusion images of Prediction Test 1 and 2. Test 3 shows greater errors and lower correlations than the fusion images of Prediction Test 1 and 2. The AAD and RMSE values of Prediction Test 3 (ranges from 0.01 to 0.05, and 0.01 to 0.08, respectively) The AAD and RMSE values of Prediction Test 3 (ranges from 0.01 to 0.05, and 0.01 to 0.08, respectively) were generally larger than Prediction Test 1 (ranges from 0.01 to 0.03, and 0.02 to 0.04, respectively) and Prediction Test 2 (ranges from 0.02 to 0.04, and 0.01 to 0.05, respectively). The r values of prediction Test 3 (ranges from 0.65 to 0.73) were smaller than Prediction Test 1 (ranges from 0.79 to 0.93) and Prediction Test 2 (ranges from 0.79 to 0.93). One potential explanation for the

Remote Sens. 2019, 11, 1699 15 of 24 were generally larger than Prediction Test 1 (ranges from 0.01 to 0.03, and 0.02 to 0.04, respectively) and Prediction Test 2 (ranges from 0.02 to 0.04, and 0.01 to 0.05, respectively). The r values of prediction Test 3 (ranges from 0.65 to 0.73) were smaller than Prediction Test 1 (ranges from 0.79 to 0.93) and Prediction Test 2 (ranges from 0.79 to 0.93). One potential explanation for the relatively low accuracy of Prediction Test 3 may be due to that its time interval between base date and prediction date was larger than the other two. In Prediction Test 3, the paddy rice has undergone more rapid growth around August, which further makes the difference between the image on the base date and the image on the prediction date larger and brings more errors to the fused image. There is a small difference between the image predicted by the real image on the previous date and the image acquired by the real image on the later date (Prediction Test 1 and 2). Therefore, when using real fine images to predict images, the images before and after the prediction date both can be taken into consideration. However, the time interval between the acquisition date and the prediction date needs to be taken into consideration. In the study, all the situations above have been taken into consideration for more precise, predicted images.

Table 2. Root Mean Square Error (RMSE), Correlation Coefficient (r), and Absolute Average Difference (AAD) between the STARFM-predicted surface reflectance and the corresponding Landsat surface reflectance.

Prediction Test 1 Prediction Test 2 Prediction Test 3 Band RMSE r AAD RMSE r AAD RMSE r AAD Blue 0.02 0.80 0.01 0.02 0.79 0.01 0.01 0.68 0.01 Green 0.02 0.83 0.01 0.02 0.82 0.01 0.01 0.72 0.01 Red 0.02 0.89 0.02 0.02 0.88 0.02 0.02 0.74 0.01 NIR 0.04 0.93 0.03 0.04 0.91 0.03 0.08 0.67 0.05 SWIR1 0.04 0.92 0.03 0.04 0.92 0.03 0.04 0.73 0.03 SWIR2 0.04 0.91 0.03 0.04 0.92 0.03 0.04 0.66 0.02

Table 3. Root Mean Square Error (RMSE), Correlation Coeﬃcient (r), and Absolute Average Diﬀerence (AAD) between the STARFM-predicted NDVI/EVI/LSWI values and the corresponding Landsat NDVI/EVI/LSWI values.

Prediction Test 1 Prediction Test 2 Prediction Test 3 VI RMSE r AAD RMSE r AAD RMSE r AAD NDVI 0.13 0.90 0.07 0.09 0.94 0.06 0.13 0.95 0.05 EVI 0.05 0.71 0.03 0.05 0.96 0.03 0.27 0.96 0.16 LSWI 0.20 0.86 0.11 0.15 0.89 0.09 0.14 0.82 0.06

Figure 12 shows an example of temporal profiles of paddy rice vegetation indices (NDVI, EVI, and LSWI) extracted from fused time series data. We can see clearly the growing cycle of paddy rice, with the growing season peaks around July 28, the flooding and transplanting period starts around May 1 and ends around July 1. It is worth noting that if we only use Landsat data, we will get a much narrower time window corresponding to the flooding and transplanting period and will have few available scenes during the peak growing season. Remote Sens. 2019 11 Remote Sens. 2019,, 11,, 1699x FOR PEER REVIEW 1616 of of 24

Figure 12. Example of VIs temporal profiles from the fused time series data. The top and bottom rows Figure 12. Example of VIs temporal profiles from the fused time series data. The top and bottom rows show the fused images and the real Landsat images from different times used in the study, respectively show the fused images and the real Landsat images from different times used in the study, (site: 132.32◦N, 47.31◦E). respectively (site: 132.32°N, 47.31°E). 4.2. Comparisons with Non-Fusion-Based and RF Classification Results 4.2. Comparisons with Non-Fusion-Based and RF Classification Results In order to verify the availability of the new strategy in paddy rice field mapping, the MODIS and the LandsatIn order time to verify series the imagery availability were alsoof the used new to strategy map paddy in paddy rice byrice using field the mapping, same classification the MODIS strategiesand the Landsat described time in Section series 3,imagery respectively were (hereafter also used referred to map to aspaddy ‘the Landsat-based rice by using paddy the same rice map’,classification ‘the MODIS-based strategies described paddy rice in map’). Section In addition,3, respectively the RF (hereafter classification referred method to wasas ‘the used Landsat- to map paddybased paddy rice based rice map’, on the ‘the same MODIS-based fused time series paddy data rice (hereafter map’). In referred addition, to the as ‘theRF classification RF-based paddy method rice map’).was used The to resultsmap paddy of the rice above based four on classifications the same fused are time shown series in Figuredata (hereafter 13. Most referred of the paddy to as ‘the rice fieldsRF-based were paddy distributed rice map’). in flat The areas results near of rivers, the above which four was classifications consistent with are the shown real situation.in Figure 13. Most of theAll paddy maps rice maintained fields were good distributed spatial in consistency. flat areas near Compared rivers, which with was the consistent Landsat-based with the mapreal (Figuresituation. 13 e,f), the map based on the fused time series data (Figure 13c,d) can better distinguish paddyAll rice maps from maintained wetland/ swampgood spatial vegetation, consistency. and had Compared higher classification with the Landsat-based accuracy for map paddy (Figure rice fields,13e,f), especiallythe map based the paddy on the rice fused fields time near series rivers, data wetlands, (Figure and 13c,d) marshes. can better In addition, distinguish the fusion-based paddy rice classificationfrom wetland/swamp can effectively vegetation, remove and the SLC-ohad higherff gap eclffectsassification by using accuracy the phenology-based for paddy rice algorithm. fields, However,especially thethe Landsat paddy imageryrice fields can near hardly rivers, exactly wetlands, extract phenologyand marshes. information In addition, such the as thefusion-based maximum EVIclassification/NDVI value can effectively due to data remove limitation the SLC-off (Figure ga 12p effects). Thus, by using there the were phenology-based some commission algorithm. errors, omissionsHowever, errorsthe Landsat and obvious imagery SLC-o canff hardlygap eff ectsexactly in theextract Landsat-based phenology map.information Compared such with as the MODIS-basedmaximum EVI/NDVI map (Figure value 13 dueg,h), to the data fusion-based limitation map(Figure contained 12). Thus, more there spatial were details. some Forcommission example, iterrors, can clearly omissions show errors some and patchy obvi andous fragmentedSLC-off gap paddy effects rice in the fields Landsat-based and the roads map. between Compared paddy with rice fields,the MODIS-based which were omittedmap (Figure on the 13g,h), MODIS-based the fusion-b map.ased Compared map contained with the RF-based more spatial map (Figuredetails. 13 Fori,j), theexample, fusion-based it can clearly map alsoshow contained some patchy more and spatial fragmented details for paddy it can rice clearly fields show and the roads between fields.paddy rice The fields, paddy which rice fieldswere omitted estimated on fromthe MO theDIS-based Landsat-based map. Compared map and thewith MODIS-based the RF-based map accounted(Figure 13i,j), for aboutthe fusion-based 29.38% (6.48 map10 4alsoha), contained 32.11% (7.08 more10 spatial4 ha) of details the study for respectively,it can clearly which show were the × × smallerroads between than that fields. estimated The paddy from therice fusion-based fields estimated map from (33.46%, the Landsat-based 7.38 104 ha). map and the MODIS- based map accounted for about 29.38% (6.48 × 104 ha), 32.11% (7.08 × 10× 4 ha) of the study respectively, which were smaller than that estimated from the fusion-based map (33.46%, 7.38 × 104 ha).

Remote Sens. 2019, 11, 1699 17 of 24 Remote Sens. 2019, 11, x FOR PEER REVIEW 17 of 24

FigureFigure 13. The 13. The comparison comparison of of four four paddy paddy rice rice maps:maps: ( (aa)) Landsat Landsat image image (false (false color color composite, composite, include include red band,red band, green green band band and and blue blue band); band); (c ()c) fusion-based fusion-based paddy paddy rice rice map, map, and and (e) (Landsat-basede) Landsat-based paddy paddy rice map;rice map; (g) MODIS-based (g) MODIS-based paddy paddy rice map;rice map; (i) RF-based (i) RF-based paddy paddy rice rice map. map. (b, d(b,f,,dh,,fj,)h display,j) display the the spatial detailsspatial of (a ,detailsc,e,g,i )of respectively (a,c,e,g,i) respectively within the within black the box. black box.

ConfusionConfusion matrices matrices in in Table Table4 4 show that that the the over overallall accuracy accuracy and Kappa and coefficient Kappa coe of thefficient fusion- of the fusion-basedbased paddy paddy rice map rice were map 98.19% were and 98.19% 0.96 respectively, and 0.96 respectively,which were larger which than were the MODIS-based larger than the paddy rice map (89.23%, 0.78) and the Landsat-based paddy rice map (92.12%, 0.84). The difference MODIS-based paddy rice map (89.23%, 0.78) and the Landsat-based paddy rice map (92.12%, 0.84). between the fusion-based map and Landsat-based map mainly in the classification of sparse The difference between the fusion-based map and Landsat-based map mainly in the classification vegetation in the areas of strips and wetlands. Greater commission and omission errors were found of sparsein the vegetation MODIS-based in themap. areas Therefore, of strips the combinat and wetlands.ion of fusion Greater images, commission Landsat ETM and + and omission OLI can errors weremake found the in phenology-based the MODIS-based algorithm map. Therefore,more powerf theul combinationin mapping paddy of fusion rice fields images, and Landsat improve ETM + andclassification OLI can make accuracy. the phenology-based Furthermore, for algorithmcomparison morepurposes, powerful the overall in mapping accuracy paddy and Kappa rice fields and improvecoefficient classification of the RF-based accuracy. paddy rice Furthermore, map were ca forlculated comparison in the same purposes, way, and the were overall 93.53%, accuracy 0.87 and Kapparespectively. coefficient This of the further RF-based indicates paddy that our rice classifica map weretion calculatedstrategy used in in the this same paper way, can andobtain were higher 93.53%, 0.87 respectively.classification accuracy. This further indicates that our classification strategy used in this paper can obtain higher classification accuracy. Remote Sens. 2019, 11, 1699 18 of 24

Table 4. The confusion matrixes between the four maps and areas of interest (AOIs).

Paddy Rice Map Class PA (%) UA (%) OA (%) Kappa Coefficient Paddy rice 97.96 98.42 Fusion-based 98.19 0.96 Others 98.42 97.95 Paddy rice 82.84 95.09 RemoteMODIS-based Sens. 2019, 11, x FOR PEER REVIEW 89.23 0.78 18 of 24 Others 95.68 84.68 Table 4. The confusionPaddy rice matrixes between 90.91 the four 93.23 maps and areas of interest (AOIs). Landsat-based 92.12 0.84 Paddy Rice Others 93.34 91.05 Kappa Class PA (%) UA (%) OA (%) Map Paddy rice 96.20 91.38 Coefficient RF-basedFusion- Paddy rice 97.96 98.42 93.53 0.87 Others 90.85 95.95 98.19 0.96 based Others 98.42 97.95 MODIS- Paddy rice 82.84 95.09 89.23 0.78 4.3. Evaluation of thebased Feature ImportanceOthers 95.68 84.68 Landsat- Paddy rice 90.91 93.23 92.12 0.84 All 52 featuresbased were sort intoOthers six groups93.34 by variable91.05 importance in descending order. For all features in one time windowPaddy (e.g., eachrice month), 96.20 the number 91.38 of features in each group was counted RF-based 93.53 0.87 and then its percentage in all featuresOthers of the90.85 time window95.95 was calculated (hereafter referred to as ‘the percentages of each group’). The importance of each month and the entire transplanting and 4.3. Evaluation of the Feature Importance flooding period was evaluated by the percentages of each group (Figure 14a): the higher the percentage of the crucial groupsAll 52 features in the timewere window,sort into six the groups higher by thevariable importance importance of in the descending time window. order. For The all importance features in one time window (e.g., each month), the number of features in each group was counted of each imageand then was its percentage presented in byall features the average of the time score window of itswas feature calculated importance (hereafter referred (Figure to as 14‘theb). On the one hand,percentages the result of showseach group’). that The April, importance May andof each June month contributed and the entire moretransplanting than otherand flooding months to the classification.period Especially, was evaluated the by images the percentages within theof each transplanting group (Figure and 14a): floodingthe higher periodthe percentage played of anthe important role in thecrucial classification, groups in the which time window, shows the the higher importance the importance of the of the images time window. in the keyThe importance phenology period of each image was presented by the average score of its feature importance (Figure 14b). On the one for paddyhand, rice identification.the result shows that From April, Figures May 8and and June 12 ,contributed the flooding more andthan transplantingother months to period the starts around Mayclassification. 1 and ends Especially, around the July images 1. This within may the explain transplanting why April,and flooding May andperiod June played were an deemed as the importantimportant time role window in the classification, in mapping which paddy shows rice the and importance prove of the the importance images in the ofkey the phenology images from the transplantingperiod and for floodingpaddy rice period.identification. On theFrom other Figure hand,s 8 and the 12, resultthe flooding indicates and transplanting that almost period all the images starts around May 1 and ends around July 1. This may explain why April, May and June were deemed within theas planting the important growing time window season in have mapping provided paddy ri valuablece and prove information the importance for of distinguishingthe images from different land coverthe types. transplanting Fused images and flooding were period. mostly On ranked the othe withr hand, higher the result importance. indicates that The almost potential all the explanation for the relativelyimages within high the importance planting growing of fused season images have provided may be valuable due to information it has filled for thedistinguishing gap of data in the flooding anddifferent transplanting land cover types. period Fused and images peak were growing mostly season ranked with (Figure higher 12 importance.) that play The an potential important role in explanation for the relatively high importance of fused images may be due to it has filled the gap of distinguishingdata in di thefferent flooding land and cover transplanting types. period This furtherand peak proved growing theseason necessity (Figure 12) and that the play importance an of those fusionimportant images role used in distinguishing in the classification. different land cover types. This further proved the necessity and the importance of those fusion images used in the classification.

Figure 14.FigureContribution 14. Contribution of different of different time time windows windows and and images to to the the classification. classification. (a) Contribution (a) Contribution of each monthof andeach themonth entire and transplanting the entire transplanting and flooding and flooding period toperiod the classificationto the classification through through evaluating its evaluating its feature importance. The top crucial features were colored in dark brown, while the least feature importance. The top crucial features were colored in dark brown, while the least decisive features decisive features were shown in dark green. (b) Contribution of each image to the classification by were shown in dark green. (b) Contribution of each image to the classification by calculating the average score of its feature importance. The rankings of importance for all images (in descending order) are marked. Remote Sens. 2019, 11, 1699 19 of 24

5. Discussion

5.1. Advantages of Fusion-and Phenology-Based Paddy Rice Mapping Strategy High-quality observations during paddy rice growth period are essential for paddy rice field mapping. There are persistent and heavy cloud coverages in most paddy rice planting regions, which brings uncertainties and noises to paddy rice classification due to those unfavorable observing conditions. Therefore, the ultimate goal of the study is to provide a pathway leading to an operational strategy for paddy rice mapping by alleviating or overcoming the data limitation to some degree. To achieve the goal, we presented a new strategy by integrating the STARFM spatiotemporal fusion algorithm and phenology-based algorithm. Our results show that the strategy performs better than the conventional phenology-based algorithm using MODIS data alone or using Landsat data alone. The paddy rice map shows more spatial details and lower errors than the Landsat-based and MODIS-based maps. This is mainly because that the fusion data helps recover the temporal profiles of VIs, which play an essential role in identifying unique phenological characteristics of paddy rice. The comparisons with the existing MODIS-map by Zhang et al., 2015 [17] and the existing Landsat-map by Dong et al., 2015 [14] also confirm that our map has more spatial details and fewer omission errors especially in SLC-off strip areas and regions with patchy and fragmented fields. Figure 15 compares the temporal profiles of VIs extracted from time series Landsat and Landsat-like data with those extracted from MODIS data at three sites (all the three sites were selected according to field photos). It can be seen that the EVI/NDVI values from the fused time series data show similar trends with those from MODIS data. Generally, the VIs based on the red-edge and NIR bands have strong correlations with LAI and are sensitive to the dynamics of the canopy and surface water content [17,46]. Therefore, the NDVI, EVI, LSWI should ideally capture the unique physical features of paddy rice during the flooding and transplanting phases. However, the MODIS LSWI time series failed to depict the flooding signals during the paddy rice transplanting and flooding period because of its low spatial resolution and mixed pixels. During the flooding and transplanting period, the LSWI values from the MODIS time series (the maximum LSWI value was lower than 0.40) were much smaller than those from the fused time series data (the maximum LSWI value was over 0.60). Using Landsat data alone cannot capture greening and maturing signals of rice plants. From Figure 15a,d,g, the increasing trend of NDVI/EVI values from May to July and decreasing trend from July to November were well reconstructed by the fused time series data. Therefore, the fusion images are desirable to capture phenological information and for paddy rice classification. Previous researches [23,32] reported that using all of the time-series Landsat and fused Landsat-like data for land cover classification may be not a judicious choice. However, in our study, we found that the utilization of time-series Landsat imagery is necessary. In their studies, SVM was used for classification. Redundant information in the spectral time series might cause overfitting and lower generalization power of SVM, thus leading to larger errors. Different from the training-based classification, the phenology-based algorithm used in our strategy relies on a clear phenological pattern to select the threshold suitable for paddy rice detection. Moreover, in our study, we found that our strategy-based classification performed better than the RF-based classification. In the map derived from RF, the roads between fields were misclassified as paddy rice, and there were obvious omission errors caused by SLC-off gaps. In addition, our method does not need training samples and is easy to implement. However, the RF-based map shows higher accuracy and lower errors than the Landsat-based and MODIS-based maps, which may indicate the role of the fusion images. The feature importance derived from the RF model reveals that features from fused Landsat-like data have higher ranks, confirming the necessities of spatiotemporal fusion in paddy rice mapping, regardless of the classification method used. Previous research by Qin et al., 2015 [3] using both Landsat OLI and ETM+ images in the same study area showed that there was reasonable agreement on the spatial distribution of the paddy rice fields, although their results were generated for the year of 2012 and 2013. The overall accuracy and Remote Sens. 2019, 11, 1699 20 of 24

Kappa coefficient of their results were 97.32% and 0.94 respectively, which were smaller than our result (98.19%, 0.96). According to the high-resolution images in Google Earth from 2012–2013 and 2018, we found that areas which showed differences in the two paddy rice maps may be attributed to either the omission error in the result by Qin et al. (associated with data limitation) or the expansion of rice paddyRemote Sens. fields 2019 in, 11 2012–2018., x FOR PEER REVIEW 20 of 24

Figure 15.15. The temporal profilesprofiles of paddypaddy ricerice vegetationvegetation indicesindices (NDVI, EVI, andand LSWI)LSWI) extractedextracted fromfrom thethe fused time series data (left column ) and MODIS ( right column ) respectively, respectively, atat (a,,b) a paddy rice sitesite (132.27(132.27°N,◦N, 48.0048.00°E),◦E), ( c,d) a paddy ricerice sitesite (133.03(133.03°N,◦N, 47.3347.33°E),◦E), and ( e,f) a paddypaddy ricerice sitesite (132.32(132.32°N,◦N, 47.3147.31°E).◦E). The locations and the dates of th thee field field photos taken were marked in the photos. The VIs values obtained fromfrom fused images were marked with blackblack dots, and the VIs values obtained fromfrom MODISMODIS imagesimages werewere markedmarked withwith hollowhollow triangles.triangles. 5.2. Limitation and Future Opportunities Previous researches [23,32] reported that using all of the time-series Landsat and fused Landsat- like dataThere for are land still cover some classification limitations in may the strategybe not a judicious presented choice. in this However, study. First, in errorsour study, from we STARFM found predictionsthat the utilization may affect of thetime-series classification Landsat results. imagery How is these necessary. errors a Inffect their the studies, following SVM phenology-based was used for classificationclassification. resultsRedundant were information not fully investigated in the spectr inal this time study. series Generally, might cause the overfitting quality of and predicted lower Landsat-likegeneralization images power is affofected SVM, by thus the time leading interval to betweenlarger errors. the base Different pair and from the time the interval training-based between theclassification, acquisition the date phenology-based (tk) and the prediction algorithm date used (t0)[ in47 ].our A strategy greater time relies interval on a clear between phenologicaltk and t0 indicatespattern to worse select quality the threshold of the STARFM-predicted suitable for paddy image. rice detection. In our study, Moreover, the fusion in our image study, of Predictionwe found Testthat 3our shows strategy-based greater errors classification and lower performed correlations better than than the otherthe RF-based two (Table classification.3). The rapid In the growth map periodderived such from as RF, August the roads would between further fields enlarge were the mi diffsclassifiederence between as paddy images rice, fromand there different were dates obvious and causeomission more errors errors caused in the by fused SLC-off image. gaps. It In needs additi toon, be our investigated method does in not the need future training that how samples temporal and changeis easy to intensity implement. influences However, the performancethe RF-based ofmap the shows fusion higher algorithm, accuracy and and how lower errors errors in prediction than the influenceLandsat-based the construction and MODIS-based of time-series maps, phenologywhich may patterns.indicate the We role recognize of the fusi thaton there images. are continuousThe feature eimportancefforts in improving derived thefrom spatiotemporal the RF model fusionreveals model that features [19,21,48 from]. For fused example, Landsat-like Zhu et al.data developed have higher the enhancedranks, confirming spatial and the temporal necessities adaptive of spatiotemporal reflectance fusionfusion model in paddy (ESTARFM) rice mapping, using tworegardless base pairs of the of imagesclassification and proved method that used. the model can improve the prediction accuracy in heterogeneous regions [21]. However,Previous the algorithmresearch by was Qin based et al., on 2015 the assumption[3] using both that Landsat the reflectance OLI and changes ETM+ linearly,images in which the same may bestudy not area applicable showed for that predicting there was images reasonable in different agreem phenologyent on the periods. spatial Indistribution addition, it of has the been paddy shown rice fields, although their results were generated for the year of 2012 and 2013. The overall accuracy and Kappa coefficient of their results were 97.32% and 0.94 respectively, which were smaller than our result (98.19%, 0.96). According to the high-resolution images in Google Earth from 2012–2013 and 2018, we found that areas which showed differences in the two paddy rice maps may be attributed to either the omission error in the result by Qin et al. (associated with data limitation) or the expansion of rice paddy fields in 2012–2018.

5.2. Limitation and Future Opportunities

Remote Sens. 2019, 11, 1699 21 of 24 that using two MODIS-Landsat base pairs does not necessarily improve the prediction, and even increases the errors on some bands [23]. Future research may further improve the spatiotemporal fusion algorithm based on only one pair of base images. Second, the strategy presented in this study may not be directly used for other regions due to diﬀerent timing of phenological stages. Thresholds of the indices for paddy rice detection need to be carefully examined and selected when applying to other regions. The strategy may also face challenges for regions with very frequent cloud covers, such as in South Asia. Sparse availability of cloud-clear observations will hinder the application of optical remote sensing images. In those regions, SAR data that is not inﬂuenced by cloud may be a better choice for paddy rice mapping [18].

6. Conclusions In this study, we presented a fusion- and phenology-based strategy for paddy rice mapping in areas where severe cloud contamination limits data availability. MODIS LST data was first used to improve the selection of images and determination of the fusion images date at appropriate phenological stages. STARFM was used to predict Landsat-like images during rice growth period based on MODIS and Landsat images. The fusion images help to reconstruct the time series VIs at 30 m resolution, allowing for paddy rice mapping in patchy and fragmented fields. Auxiliary data including DEM data and forest map were also used to further improve the accuracy of the paddy rice classification result. The overall accuracy and the kappa coefficient of our paddy rice map were 98.19% and 0.96, respectively, which were higher than the maps based on MODIS, Landsat only. Compared with paddy rice maps based on low spatial resolution such as MODIS, our map has more spatial details information, and greatly reduce the influence of mixed pixels on the accuracy; compared with maps based on Landsat data alone, our map can effectively distinguish paddy rice fields from other crops, wetland marshes, and identify the paddy rice fields within areas covered by SLC-off gaps, clouds, shadows, et al. Our result shows that our strategy is effective and robust in the regions with limited available images, as well as the areas with patchy and fragmented fields. The results prove the role of the fusion images in mapping paddy rice and indicate the importance of high-quality images within the growing season for paddy rice mapping. With the unprecedented opportunities provided by fused time series imagery, it is believed that this proposed fusion-and phenology-based classification strategy is promising to facilitate the long-term and effective monitoring and mapping of paddy rice. Our future work will consider taking new satellite images, such as Landsat and Sentinel-2 MSI data, as the input data of the spatiotemporal fusion algorithm in order to provide higher spatial and higher temporal resolution time series data for the phenology-based algorithm.

Author Contributions: Q.Y. conceived and designed the experiment, wrote the main programs and wrote the paper. M.L. supervised the research and complemented the algorithm, provided significant suggestions in revising. Y.K. supervised the work and provided significant comments and suggestions. J.C. was involved in the data processing and writing—review & Editing. X.C. gave the help in paper writing. Funding: This work was funded by The National Key Research and Development Program of China (No. 2017YFC1500900) and Beijing Natural Science Foundation under Grant [5172002]. Acknowledgments: This work was supported by the Navigation and Location-based service (NAL) Lab, Peking University. We would thank Feng Gao providing the corresponding source codes. Last, we would like to thank the editors and the anonymous reviewers for their suggestions. Conflicts of Interest: The authors declare no conflict of interest.

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