applied sciences

Article Mapping Maize Cultivated Area Combining MODIS EVI Time Series and the Spatial Variations of Phenology over Huanghuaihai Plain

Xueting Wang 1,2, Sha Zhang 3, Lili Feng 4, Jiahua Zhang 2,5,* and Fan Deng 1,* 1 School of Geosciences, Yangtze University, 430100, ; [email protected] 2 Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, 100094, China 3 Remote Sensing Information and Digital Earth Center, College of Computer Science and Technology, University, Qingdao 266071, China; [email protected] 4 College of Mining and Surveying Engineering, Hebei University of Engineering, 056000, Hebei, China; [email protected] 5 University of Chinese Academy of Sciences, Beijing 100049, China * Correspondence: [email protected] (J.Z.); [email protected] (F.D.)

 Received: 24 February 2020; Accepted: 10 April 2020; Published: 13 April 2020 

Abstract: Crop phenology is a significant factor that affects the precision of crop area extraction by using the multi-temporal vegetation indices (VIs) approach. Considering the phenological differences of maize among the different regions, the summer maize cultivated area was estimated by using enhanced vegetation index (EVI) time series images from the Moderate Resolution Imaging Spectroradiometer (MODIS) over the Huanghuaihai Plain in China. By analyzing the temporal shift in summer maize calendars, linear regression equations for simulating the summer maize phenology were obtained. The simulated maize phenology was used to correct the MODIS EVI time series curve of summer maize. Combining the mean absolute distance (MAD) and p-tile algorithm, the cultivated areas of summer maize were distinguished over the Hunaghuaihai Plain. The accuracy of the extraction results in each province was above 85%. Comparing the maize area of two groups from MODIS-estimated and statistical data, the validation results showed that the R2 reached 0.81 at the city level and 0.69 at the county level. It demonstrated that the approach in this study has the ability to effectively map the summer maize area over a large scale and provides a novel idea for estimating the planting area of other crops.

Keywords: summer maize; cultivated area; phenology difference; multi-temporal MODIS EVI; Huanghuaihai Plain

1. Introduction Maize, as the primary staple, plays a vital role in agricultural production in China. It is very significant to estimate the cultivated area of maize for national food security and sustainable economic development [1,2]. Huanghuaihai Plain covers approximately 4.5% of the whole country, whereas the maize planting area accounts for about 30% of the total maize acreage, which is the main production areas of summer maize in China. The distribution of maize in this region affects grain policy making, adjustment of the cropping system structure, and academic studies related to maize in China [3,4]. Therefore, there is an increasing need for objective, timely, and accurate estimation of the maize area over the Huanghuaihai Plain. As a kind of earth observation technology, remote sensing can effectively acquire the spatial distribution and spectral information of ground objects, with wide geographic coverage and the

Appl. Sci. 2020, 10, 2667; doi:10.3390/app10082667 www.mdpi.com/journal/applsci Appl. Sci. 2020, 10, 2667 2 of 21 merit of large information capacity, high accuracy, and speed, which provides opportunities for crop identification [5–9]. Different crops have various spectral characteristics in remotely sensed images [10,11]. The multi-temporal vegetation indices (VIs) reflect the changes in the spectral features of vegetation over time, which can discriminate different crop types [12–16]. Many previous research studies employed the multi-temporal VIs approaches to map the maize cultivated area. Wardlow and Egbert used temporal normalized difference vegetation index (NDVI) data from 250 m moderate resolution imaging spectroradiometer (MODIS) images to classify the maize over the U.S. Central Great Plains [17]. Gu et al. reconstructed a mid-scale time series NDVI dataset derived from the fusion of MODIS and thematic mapper (TM) images based on the wavelet transform to estimate the maize area in Yuanyang County of Province [18]. Maguranyanga and Murwira combined the temporal NDVI series and ancillary field data to map the maize area in the large-scale commercial farms of Zimbabwe though the maximum entropy method [19]. Tang et al. adopted the identification method for spring maize based on spectral and phenological features derived from the MODIS land surface reflectance time series data in Northeast China [20]. In contrast, although these studies were valuable for the recognition of maize, there are still some challenges to be faced in mapping the spatial distribution of maize on a large scale due to the spatial variations of maize phenology. The spectral features of summer maize on remote sensing images are different at the same time, as the maize phenology varies in different regions. In order to weaken the influence of phenology differences on the accuracy of crop area extraction, some researchers have considered the influence of latitude on summer maize phenology, when using the multi-temporal VIs approach to map the maize area on a large scale. For example, Zhang et al. used the lag values of phenology change with latitude to modify the MODIS-enhanced vegetation index (EVI) time series curve of maize and mapped the spring maize in Northeast China [21]. Wang et al. also considered the differences in maize phenology with latitude to discriminate the summer maize based on the multi-temporal MODIS EVI images over the Huanghuaihai Plain [22]. In both studies, the influence of latitude on crop phenology was taken into account when using multi-temporal MODIS images mapping the summer maize area on a large scale, which had improved the accuracy of maize area extraction. In fact, the phenology of summer maize is not only affected by latitude but also sensitive to the change in environmental conditions, as well as changes with the environmental variation. The maize phenology is sensitive to changes in environmental conditions, resulting in the maize phenology that would be altered by environmental variability [23–28]. In the past several years, the apparent warming climate caused early flowing and maturity and consequently shortened the reproductive growth stage of maize [29]. The mean temperature as one of the thermal conditions is closely related to the sowing and seedling emergence time [30–32]. The accumulated temperature is routinely used to determine crop planting schedules, so the accumulated temperature shift will lead to the change in maize growth stages [33]. Moisture is an essential requirement for maize growth, so precipitation has effects on the growth of maize. It was reported that the correlations between precipitation and the length of various maize growing phases were positive [26,34]. Furthermore, geographical position is a vital factor for maize growth [35,36]. Liu et al. found that for every 1◦ increase in latitude, northward, the growth durations of sowing to emergence and emergence to silking were significantly increased by 0.7 and 1.25 d, respectively [37]. This means the maize phenology has been changed in different regions. It can be clearly seen that the phenology of maize varied with the changes in annual average temperature, accumulated temperature, precipitation, and geographical location. On a large scale, with a great difference in the environment of different regions, the phenologies of maize are different, which lead to the difference in the spectral characteristics of summer maize in the remote sensing image. There is, therefore, the phenological differences of maize caused by various environmental factors being combined with the multi-temporal MODIS EVI images to map the cultivated area of summer maize for better extraction accuracy on a large scale. In the present study, multiple linear regression equations were constructed to simulate the summer maize phenology through analyzing the relationship between environmental factors and summer maize Appl. Sci. 2020, 10, 2667 3 of 21 phenology over the Huanghuaihai Plain. The simulated phenology of summer maize was obtained through environmental factors data (longitude, latitude, annual average temperature, annual precipitation, and 10 C accumulated temperature), which acted as an independent variable to modify the summer ≥ ◦ maize MODIS EVI time series curve and generate the standard maize EVI time series curve over Huanghuaihai Plain. Combined with the mean absolute distance (MAD) and the P-tile algorithm, the summer maize planting area was discriminated successfully in the study area. It provides a novel idea for Appl. Sci. 2020, 10, x FOR PEER REVIEW 3 of 22 improving the accuracy of maize area extraction using multi-temporal remote sensed data on a large scale. to modify the summer maize MODIS EVI time series curve and generate the standard maize EVI time 2. Study Area andseries Data curve over Huanghuaihai Plain. Combined with the mean absolute distance (MAD) and the P- tile algorithm, the summer maize planting area was discriminated successfully in the study area. It provides a novel idea for improving the accuracy of maize area extraction using multi-temporal 2.1. Study Area andremote Reference sensed data Area on a large scale.

2. Study Area and Data Huanghuaihai Plain (Figure1) (32 ◦–40◦ N, 114◦–121◦ E), which includes Beijing, , Hebei, Henan, and ,2.1. Study isarea the and mainReference planting Area area of summer maize in China. The region belongs to the monsoon climate ofHuanghuaihai medium latitudes Plain (Figure with 1) (32°–40° four N, clearly 114°–121° distinct E), which includes seasons Beijing, [38 ].Tianjin, The Hebei, annual precipitation is 500–900 mm. TheHenan, precipitation and Shandong, is isthe mostly main planting concentrated area of summer in maize summer, in China. accounting The region belongs for to about 70% of the the monsoon climate of medium latitudes with four clearly distinct seasons [38]. The annual annual precipitation.precipitation The is mean500–900 annualmm. The precipitation temperature is mostly is concentrated 8–15 ◦C[ in39 summer,]. Summer accounting maize for phenology is significantly differentabout 70% due of the to annual the di precipitation.fference The in climatemean annual among temperature regions is 8–15 °C over [39]. HuanghuaihaiSummer maize Plain. phenology is significantly different due to the difference in climate among regions over CityHuanghuaihai (Figure1 ),Plain. with a relatively single planting structure, was selected as the reference Ruzhou City (Figure 1), with a relatively single planting structure, was selected as the reference area, which is located in the middle of Henan Provence (33◦560–34◦200 N, 112◦310–113◦070 E). Summer area, which is located in the middle of Henan Provence (33°56′–34°20′ N,112°31′–113°07′ E). Summer maize is the mainmaize summer is the main crop summer in thecrop in region, the region, accounting accounting for forabout about 70% of the 70% summer of the crop summerplanting crop planting area. Therefore, choosingarea. Therefore, Ruzhou choosing City Ruzhou as the City reference as the reference area area can can reduce reduce thethe eventevent of of mixed mixed classification. classification.

(a) Huanghuaihai Plain, study area (b) Ruzhou City, reference area Figure 1. TheFigure spatial 1. The distribution spatial distribution of of the the study study area area (a) and (a reference) and referencearea (b). area (b). Commented [M1]: The figure need to be changed. 2.2. Data Description and Processing 2.2. Data Description and Processing 2.2.1. Landsat7 ETM+ Image and Pre-Processing 2.2.1. Landsat7 ETMThe+ Imagespatial resolution and Pre-Processing of Landsat7 ETM+ (https://glovis.usgs.gov) is 30 m, enabling high- precision extraction of crop area on a small scale [40,41]. According to the weather conditions, image The spatial resolutionquality, and phonological of Landsat7 characteristics ETM+ of( https:summer// maize,glovis.usgs.gov the ETM+ images) ison 30June m, 9th enabling(sowing), high-precision September 13th (milk-ripe), and October 31st (harvested stage), 2012, were selected to obtain the extraction of cropsummer area maize on a cultivated small scalearea in [40the ,reference41]. According area. Striping, to radiation the weather calibration, conditions, atmospheric image quality, and phonologicalAppl. characteristics Sci. 2020, 10, x; doi: FOR of PEER summer REVIEW maize, the ETM+ images www.mdpi.com/journal/applsci on June 9th (sowing), September 13th (milk-ripe), and October 31st (harvested stage), 2012, were selected to obtain the summer maize cultivated area in the reference area. Striping, radiation calibration, atmospheric correction based on the FLAASH model, and clipping were performed for 3 images before classification.

2.2.2. MODIS EVI Data and Pre-Processing MOD13Q1 data were derived from the National Aeronautics and Space Administration (NASA) (https://search.earthdata.nasa.gov). MOD13Q1 data provide an EVI vegetation layer every 16 days Appl. Sci. 2020, 10, x FOR PEER REVIEW 4 of 23

summer maize cultivated area in the reference area. Striping, radiation calibration, atmospheric correction based on the FLAASH model, and clipping were performed for 3 images before classification.

Appl. Sci. 2020, 10, 2667 4 of 21 2.2.2. MODIS EVI Data and Pre‐Processing MOD13Q1 data were derived from the National Aeronautics and Space Administration (NASA) at 250(https://search.earthdata.nasa.gov). m spatial resolution. The summer MOD13Q1 maize data growth provide period an EVI (2012-06-01–2012-10-31) vegetation layer every 16 days contained at MOD13Q1250 m dataspatial for resolution. 9 phases, The covering summer h26v04, maize h26v05,growth period h27v04, (2012 and‐06 h27v05.‐01–2012‐ First,10‐31) thecontained images in the sameMOD13Q1 phase data were for spliced, 9 phases, and covering the spliced h26v04, images h26v05, were h27v04, then and subjected h27v05. First, to projection the images conversion in the processing.same phase The projectedwere spliced, coordinate and the systemspliced wasimages UTM_Zone_51N, were then subjected and to the projection geographic conversion coordinate processing. The projected coordinate system was UTM_Zone_51N, and the geographic coordinate system was CGS_WGS_1984. Finally, the boundary data of Huanghuaihai and Ruzhou were used to system was CGS_WGS_1984. Finally, the boundary data of Huanghuaihai and Ruzhou were used to mask the processed images. mask the processed images. 2.2.3. Land Use Data 2.2.3. Land Use Data The landThe use data (Globeland30,land http://www.globallandcover.comuse data /GLC30Download(Globeland30,/index.aspx ) of spatialhttp://www.globallandcover.co 30 m resolution in 2010 werem/GLC30Download/index.aspx) employed to modify the extracted of spatial area 30 m of resolution summer maize in 2010 in the referencewere area. employed The cultivated to modify landthe extracted (code 10) area dataset of summer in the maize land usein the data reference was overlaid area. The with cultivated the spatial distributionland (code of summer 10) dataset maize in the from land use ETM data+ images was overlaid to exclude with the the spatial influence distribution of other of summer land use maize types on the extractionfrom ETM+ results images in to the exclude reference the influence area. of other land use types on the extraction results in the reference area. 2.2.4. Phenological and Meteorological Data 2.2.4. Phenological and Meteorological Data Summer maize phenological and meteorological data of Huanghuaihai Plain in 2012 were derived Summer maize phenological and meteorological data of Huanghuaihai Plain in 2012 were from the National Meteorological Information Center (http://data.cma.cn). In this study, the observation derived from the National Meteorological Information Center (http://data.cma.cn). In this study, the phenology data from 32 agricultural meteorological stations are gathered, including latitude, longitude, observation phenology data from 32 agricultural meteorological stations are gathered, including maizelatitude, fertility longitude, name, and maize fertility fertility date name, that and was fertility converted date that into was the converted day of the into year the (DOY) day of the at eachyear site. The location(DOY) at of each agro-meteorological site. The location stations of agro is‐meteorological shown in Figure stations2, and is theshown phenological in Figure information2, and the is listedphenological in Table1. information is listed in Table 1.

FigureFigure 2. The 2. The spatial spatial distribution distribution of of agro-meteorological agro‐meteorological sites sites in in Huanghuaihai Huanghuaihai Plain. Plain.

The gridThe grid dataset dataset of the of the daily daily value value of of surface surface temperaturetemperature and and precipitation precipitation in China in China with with the the spatial resolution of 0.5° was employed to obtain the meteorological elements in the study area. The spatial resolution of 0.5◦ was employed to obtain the meteorological elements in the study area.

The gridAppl. dataset Sci. 2020, was10, x; doi: generated FOR PEER by REVIEW the thin plate spline (TPS) method basedwww.mdpi.com/journal/applsci on the data of precipitation and air temperature of the high-density site on the ground in China in the special national information materials, as well as the GTOPO30 data. The grid dataset was defined as the CGS_WGS_1984 geographic coordinate system and was projected into the UTM_Zone_51N coordinate system. We resampled the grid data with 250 m spatial resolution by the bilinear algorithm. Based on the processed grid dataset, the annual average temperature, annual precipitation, and 10 C accumulated temperature ≥ ◦ in Huanghuaihai Plain were calculated from the grid data with 250 m spatial resolution. In addition, the annual average temperature, annual precipitation, and 10 C accumulated temperature at 32 ≥ ◦ agro-meteorological stations were obtained (Table1). Appl. Sci. 2020, 10, 2667 5 of 21

Table 1. Phenology and environmental factors of summer maize in Huanghuaihai Plain in 2012.

Sites Environmental Factors Phenological Information (DOY)

Average 10 ◦C Longit-ude Latitude Annual Accumulated≥ Precipitation Seeding Name Sowing Trefoil Seven-Leaf Joint-ing Tassel-ing Milk-ripe Matura-tion (◦E) (◦N) Temperature Temperature (mm) Emergence (◦C) (◦C day) Nanyang 112.58 33.03 14.61 640.3 4919.5 155 164 168 180 192 209 235 254 Neixiang 111.86 33.05 14.51 635.1 5014.5 163 169 171 181 198 212 232 252 Ruzhou 112.83 34.18 7.08 422.8 4930.8 157 164 168 180 192 212 241 258 115.66 34.45 14.91 638.5 5035.6 168 174 176 186 200 219 243 262 Qixian 114.78 34.53 9.5 479.5 4969.8 160 166 170 186 198 215 241 261 Zhengzho-u 113.65 34.72 14.81 435.9 5093.4 161 166 168 180 194 216 241 260 Heze 115.43 35.25 15.39 417.5 4854.4 170 176 178 190 202 220 243 268 113.88 35.31 13.84 439.4 5085.9 160 166 172 182 198 214 235 262 Jining 116.58 35.45 13.48 518.9 5050.5 170 184 188 198 208 228 250 272 Juxian 118.83 35.58 13.42 842.1 4614.6 171 178 182 192 206 221 248 268 115.01 35.7 13.42 398.1 4793.8 159 176 182 192 210 221 252 262 114.37 36.12 14.55 498.1 4878.3 166 172 177 186 198 219 245 268 Taian 117.15 36.16 13.57 587.7 4783.9 179 184 188 200 217 235 248 272 Jiaozhou 120 36.3 13.22 606 4545.7 167 174 178 190 210 228 262 276 Gaomi 119.75 36.41 13.67 650.9 4521.4 168 172 178 188 200 212 243 264 Liaocheng 115.96 36.48 14.06 465.3 4739 165 172 180 190 208 220 252 272 Feixiang 114.8 36.55 14.81 531.5 4850.1 158 168 174 188 208 219 243 268 Shexian 113.66 36.56 11.71 524.1 3701.2 168 174 180 194 208 227 258 282 Hanting 119.18 36.75 13.47 563.7 4600.7 164 168 176 188 200 219 241 262 Laiyang 120.7 36.93 11.71 587.3 4360.4 173 179 182 194 206 223 254 272 Jiyang 117.11 36.98 12.68 659.6 4850.6 166 170 174 186 200 216 243 270 Neiqiu 114.5 37.28 12.15 539.4 4840.3 158 165 171 182 197 215 242 272 Gaoyi 114.62 37.6 14.68 521.8 4823.8 159 166 170 178 184 198 217 241 Luanchen-g 114.63 37.88 12.12 521.8 4823.8 165 182 176 186 198 219 239 272 Huanghua 117.35 38.37 11.7 760.2 4649.1 175 182 186 196 212 228 252 278 Hejian 116.08 38.45 12.49 666.4 4658.8 164 170 178 190 204 217 241 272 Rongchen-g 115.85 39.05 13.57 730.9 4493.9 167 175 181 191 202 221 248 276 Bazhou 116.38 39.12 12.03 791.2 4587.1 160 168 182 174 196 217 243 266 Zhuozhou 115.96 39.48 12.71 730.9 4493.9 169 176 178 189 202 221 249 272 Baodi 117.28 39.7 13.05 881.6 4542.8 168 178 176 192 202 221 250 267 Tongxian 116.63 39.92 12.88 875.5 4554.2 172 178 183 198 206 225 256 274 Sanhe 117.08 39.96 13.09 881.6 4542.8 173 178 182 190 202 223 252 273 Appl. Sci. 2020, 10, x FOR PEER REVIEW 8 of 23 Appl. Sci. 2020, 10, 2667 6 of 21

2.2.5. Statistical Data 2.2.5. Statistical Data The statistics of the summer maize cultivated area are from the statistical yearbooks [42–51]. The statisticsThe of statistics the total of surface the summer area of maize summer cultivated maize on area 43 city are‐levels from were the statistical collected, yearbooks including [Beijing,42–51]. TheTianjin, statistics 7 cities of in the Hebei, total surface16 cities area in Henan, of summer and 18 maize cities on in 43 Shandong. city-levels Meanwhile, were collected, the statistics including of Beijing,the total Tianjin, surface 7 area cities of in summer Hebei, 16 maize cities inon Henan, 245 county and‐ 18levels cities were in Shandong. gathered, Meanwhile, including 9 the counties statistics in ofBeijing, the total 6 counties surface in area Tianjin, of summer 51 counties maize in on Hebei, 245 county-levels 99 counties in were Henan, gathered, and 80 including counties in 9 countiesShandong. in Beijing, 6 counties in Tianjin, 51 counties in Hebei, 99 counties in Henan, and 80 counties in Shandong. 3. Methodology 3. Methodology The overall process for the recognition of the summer maize cultivated area in Huanghuaihai The overall process for the recognition of the summer maize cultivated area in Huanghuaihai Plain is displayed in Figure 3. The main process includes: (1) The support vector machine (SVM) Plain is displayed in Figure3. The main process includes: (1) The support vector machine (SVM) algorithm was adopted to distinguish the spatial distribution of summer maize in the reference area. algorithmThe summer was maize adopted MODIS to distinguish EVI time series the spatial curve distributionwas acquired of through summer masking maize in multiple the reference periods area. of TheMODIS summer EVI images maize MODISand the EVIdistribution time series of summer curve was maize acquired in the through reference masking area, which multiple was periodssmoothed of MODISwith the EVI Savizky–Golay images and themethod distribution [52–55]. of (2) summer Through maize the stepwise in the reference regression area, method which wasto analyze smoothed the withrelationship the Savizky–Golay between summer method maize [52– 55phenological]. (2) Through observation the stepwise data regression and environmental method to factors analyze from the relationship32 agrometeorological between summer stations, maize multiple phenological linear regression observation equations data and were environmental constructed factorsto simulate from the 32 agrometeorologicalsummer maize phenology. stations, The multiple simulated linear phenology regression acted equations as the corrected were constructed parameter to for simulate the MODIS the summerEVI time maize series phenology.curve to obtain The simulatedthe equations phenology of the standard acted as summer the corrected maize parameter EVI time forseries the curve MODIS in EVIthe study time series area. (3) curve By calculating to obtain the the equations mean absolute of the distances standard summer(MAD) map maize between EVI time standard series summer curve in themaize study EVI area. time (3) series By calculating and actual the MODIS mean absolute EVI time distances series (MAD) images, map and between setting standard the appropriate summer maizethresholds EVI time with series the p and‐tile actualmethod, MODIS the summer EVI time maize series planting images, andarea setting was estimated the appropriate in Huanghuaihai thresholds withPlain. the p-tile method, the summer maize planting area was estimated in Huanghuaihai Plain.

Figure 3. The flowchart flowchart to map the summer maize cultivated area over Huanghuaihai Plain.

3.1. Method of Maize Area IdentificationIdentification in the Reference AreaArea By surveying thethe phenologicalphenological characteristics characteristics of of the the main main crops crops in in the the reference reference area area (Figure (Figure4), 4), it canit can be be seen seen that that summer summer maize maize was was sowed sowed in earlyin early and and middle middle June, June, which which presented presented as bareas bare soil soil on remoteon remote sensing sensing images. images. The The sowing sowing dates dates of peanut of peanut and and cotton cotton were were between between late Aprillate April and earlyand early May. TheMay. planting The planting area of area peanut of peanut and cotton and showedcotton showed vegetation vegetation information information on remote on sensingremote sensing images inimages mid-June. in mid It‐June. is the It best is the time best to time distinguish to distinguish summer summer maize maize from otherfrom other summer summer crops crops in mid-June. in mid‐ FromJune. From July to July September, to September, the summer the summer maize maize was in was the periodin the period from seven-leaf from seven to‐ maturation,leaf to maturation, which showedwhich showed vegetation vegetation information information on the on remote the remote sensing sensing images. images. In late In October, late October, summer summer maize maize had beenhad been harvested harvested and winterand winter wheat wheat was sowing was sowing or had or seedling had seedling emergence emergence in the in reference the reference area, so area, the summerso the summer maize plantingmaize planting area displayed area displayed the bare the soil bare spectrum soil spectrum feature feature on the remote on the sensingremote sensing images.

Appl. Sci. 2020, 10, x; doi: FOR PEER REVIEW www.mdpi.com/journal/applsci Appl. Sci. 2020, 10, 2667 7 of 21 Appl. Sci. 2020, 10, x FOR PEER REVIEW 9 of 23

Hence,images. the Hence, images the with images good with quality good on quality 9 June, on 13 September,9 June, 13 September, and 31 October, and 2012,31 October, represented 2012, direpresentedfferent spectrum different features spectrum at diff erentfeatures growth at different stages of growth maize, whichstages were of maize, used towhich extract were the summerused to maizeextract acreage the summer in the maize reference acreage area. in The the specific reference steps area. were The as specific follows: steps were as follows:

Figure 4. Phenology calendar of principal crops in the reference area.area.

(1).(1). Combined with with Google Google Earth, Earth, the the training training samples samples of of road, road, vegetation, habitation, habitation, water water body, body, and bare soil were selected from threethree periodsperiods ofof ETMETM++ data by visual interpretation.interpretation. The SVM algorithm was used to classify the three images. (2).(2). The vegetation vegetation coverage coverage of of the the ETM+ ETM+ imageimage on on 13 13 September September 2012, 2012, and and the the bare bare soil soil coverage coverage of theof the ETM+ ETM images+ images on on 9 June 9 June 2012 2012 and and 13 13 October October 2012, 2012, were were recognized. Spatial intersectionintersection operations (Zhang et al., 2010) were performed for recognizing vegetation and bare soil coverage fromfrom the remote sensing images in the reference area. (3).(3). The result of images intersection processing was superimposed with the cultivated land dataset (code 10) from the land use data of 30 m spatial resolution in 2010 (GlobeLand 30), to correct thethe extracted summer maize acreage in the reference area.

3.2. Multiple Linear Stepwise Regression 3.2. Multiple Linear Stepwise Regression In processes of stepwise regression analysis, contributions of environmental factors to variances In processes of stepwise regression analysis, contributions of environmental factors to variances in the regression equation are taken as criteria [8,56]. Significant factors that affect summer maize in the regression equation are taken as criteria [8,56]. Significant factors that affect summer maize phenology are introduced into the regression equation one by one, and factors with no significant phenology are introduced into the regression equation one by one, and factors with no significant influence on summer maize phenology are removed one by one. Stepwise regression analysis can influence on summer maize phenology are removed one by one. Stepwise regression analysis can not notonly only establish establish “optimal” “optimal” regression regression observations, observations, but also but overcome also overcome the multicollinearity the multicollinearity to a certain to a certainextent [57–59]. extent [ 57Given–59]. the Given widely the widelyreported reported sensitivity sensitivity of maize of growth maize growth to surroundings, to surroundings, it seems it seemsimportant important to consider to consider the effects the eff ectsof various of various factors factors on onthe the maize maize phenology phenology in in different different regions. regions. Therefore,Therefore, inin thisthis research,research, wewe employedemployed multiplemultiple linear stepwise regression to fitfit thethe relationshiprelationship between summersummer maize maize phenology phenology and and environmental environmental factors factors (i.e., longitude, (i.e., longitude, latitude, latitude, annual averageannual temperature, annual precipitation, and 10 C accumulated temperature). average temperature, annual precipitation,≥ ◦and ≥10 °C accumulated temperature). 3.3. The Mean Absolute Distance 3.3. The Mean Absolute Distance The similar measurement method is one of the main approaches to classify crop planting The similar measurement method is one of the main approaches to classify crop planting information based on the multi-temporal vegetation indices (VIs) [60–62]. The basic idea of the similar information based on the multi‐temporal vegetation indices (VIs) [60–62]. The basic idea of the similar measurement method is to establish VI curves of different features, and then compare the similarity of measurement method is to establish VI curves of different features, and then compare the similarity the time series VI curves between the pixel to be divided and the referenced pixel, which can determine of the time series VI curves between the pixel to be divided and the referenced pixel, which can its type. In the current research, MAD [18] was selected as the indicator of similar measurement. By determine its type. In the current research, MAD [18] was selected as the indicator of similar comparing the similarity between the actual MODIS EVI pixel and the standard summer maize EVI measurement. By comparing the similarity between the actual MODIS EVI pixel and the standard pixel, the MAD map was calculated in the study area. The specific formula of MAD is as follows: summer maize EVI pixel, the MAD map was calculated in the study area. The specific formula of MAD is as follows: Xn 1 dij = xik xjk (1) 1 n mk − k=1 d=ij x ik‐ x jk (1) mk k=1 where dij is the value of the MAD; mk is the number of the pixels of the template image in the k sequence,where dij takingis the value the pixel of the of each MAD; raster mk is of the the number standard of summer the pixels maize of the EVI template as the template, image in and them k issequence, 1; xik is the taking actual the value pixel ofof MODIS each raster EVI of in the the standard k sequence; summerxjk is themaize standard EVI as EVI the valuetemplate, of summer and mk maizeis 1; xik in is thethe sequence;actual valuen is of the MODIS total number EVI in ofthe the k sequence; time series xjk (n is= the9). standard EVI value of summer maize in the sequence; n is the total number of the time series (n = 9).

Appl. Sci. 2020, 10, x; doi: FOR PEER REVIEW www.mdpi.com/journal/applsci Appl. Sci. 2020, 10, x FOR PEER REVIEW 10 of 23 Appl. Sci. 2020, 10, 2667 8 of 21 3.4. The p‐Tile Algorithm

3.4. TheThe p-Tile p‐tile Algorithm algorithm [21,63], a classical way to select the threshold, was proposed by Doyle in 1962. The research objects were divided into the target mode Po and the background mode Pb, and the The p-tile algorithm [21,63], a classical way to select the threshold, was proposed by Doyle in quantile p‐value was obtained by the ratio of Po and Pb, which is equal to the prior probability. The 1962. The research objects were divided into the target mode P and the background mode P , and suitable threshold value can be found according to the prior probability,o such that the target bmode the quantile p-value was obtained by the ratio of P and P , which is equal to the prior probability. was greater than the threshold value and the backgroundo bmode was less than the threshold value. The suitable threshold value can be found according to the prior probability, such that the target mode The specific formulae are as follows: was greater than the threshold value and the background mode was less than the threshold value. The specific formulae are as follows: P = Po/Pb (2) P = Po/Pb (2) f(x,y) ≤ T (3) f (x,y) T (3) ≤ f (f(x,y)x,y) > > TT (4)(4)

where Po is the target mode, Pb is the background mode, f(x,y) is the research objects (MAD map), and where Po is the target mode, Pb is the background mode, f (x,y) is the research objects (MAD map), T is the threshold. The ratio of the statistical summer maize acreage and the total area of every and T is the threshold. The ratio of the statistical summer maize acreage and the total area of every province was calculated as a prior probability in Huanghuaihai Plain. The histogram of the MAD province was calculated as a prior probability in Huanghuaihai Plain. The histogram of the MAD map map of each province was accumulated until the proportion of accumulated value was greater or of each province was accumulated until the proportion of accumulated value was greater or equal to equal to the prior probability, and this is when the mad value was the optimal threshold T. the prior probability, and this is when the mad value was the optimal threshold T.

4.4. Results and Discussion

4.1.4.1. Extraction and Reconstruction ofof SummerSummer MaizeMaize MODISMODIS EVIEVI Time SeriesSeries CurveCurve inin thethe ReferenceReference Areaarea BasedBased onon ETMETM++ data, the spatial distributiondistribution of of summer summer maize maize (38.5 (38.5 × 101033 ha) in Ruzhou is shown × inin FigureFigure5 5.. The The result result of of summer summer maize maize extraction extraction in in Ruzhou Ruzhou City City was was verified verified by by the the statistics statistics of of thethe totaltotal surfacesurface areaarea of of summer summer maize maize (43.4 (43.4 × 101033 ha), and the accuracy waswas higherhigher thanthan 88%.88%. ItIt waswas × revealedrevealed thatthat thethe wayway ofof intersectionintersection operationoperation basedbased onon didifferentfferent spectralspectral characteristicscharacteristics inin maizemaize criticalcritical periodsperiods cancan acquireacquire thethe maizemaize cultivatedcultivated areaarea withwith highhigh precisionprecision inin thethe relativelyrelatively singlesingle area.area.

FigureFigure 5.5. TheThe spatialspatial distributiondistribution ofof summersummer maizemaize inin thethe referencereference areaarea from from Landsat Landsat ETM ETM+.+.

TheThe summersummer maize maize MODIS MODIS EVI EVI time time series series images images of the of reference the reference area were area obtained were obtained by masking by themasking spatial the distribution spatial distribution area of summerarea of summer maize andmaize multi-temporal and multi‐temporal MODIS MODIS EVI images EVI images from from the referencethe reference area. area. The average The average value ofvalue summer of summer maize MODIS maize EVIMODIS images EVI in images each period in each was period calculated, was whichcalculated, was regardedwhich was as regarded the summer as the maize summer EVI value maize to EVI obtain value the to original obtain the MODIS original EVI MODIS time series EVI curvetime series of summer curve maize of summer in the referencemaize in the area. reference Smoothing area. the Smoothing original MODIS the original EVI time MODIS series EVI curve time of summerseries curve maize of withsummer Savitzky–Golay maize with filtering,Savitzky–Golay the smoothed filtering, MODIS the smoothed EVI time MODIS series curve EVI oftime summer series curve of summer maize was gained in the reference area (Figure 6). The Gaussian function was used

Appl. Sci. 2020, 10, x; doi: FOR PEER REVIEW www.mdpi.com/journal/applsci Appl. Sci. 2020, 10, 2667 9 of 21 Appl. Sci. 2020, 10, x FOR PEER REVIEW 11 of 23 tomaize fit the was smoothed gained inMODIS the reference EVI time area series (Figure curve6). of The summer Gaussian maize function in the was reference used to area. fit the The smoothed formula is as follows: MODIS EVI time series curve of summer maize in the reference area. The formula is as follows:

22 2 ib12 ib ib 3 ()2 () 2 () 2 cc12i b i b c3 i b (5) EVI a e( − a1 ) e ( a− 2 ) e( i ( 145,177− 3 ) 289) EVIi =12a e − c1 + a e − c 32 + a e − c3 (i = 145, 177 289) (5) i 1 × 2 × 3 × ··· where i is the day of the year (DOY), EVIi is the simulated EVI value of summer maize in the i EVI referencewhere is area the dayon DOY of the i, yearand the (DOY), parametersi is the of the simulated simulated EVI EVI value function of summer are shown maize inin theTable reference 2. area on DOY i, and the parameters of the simulated EVI function are shown in Table2. Table 2. Simulated enhanced vegetation index (EVI) function parameters of summer maize in Table 2. Simulated enhanced vegetation index (EVI) function parameters of summer maize in reference area. reference area. Parameters Data Parameters Data Parameters Data Parameters Data Parameters Data Parameters Data a1 0.282 a2 0.321 a3 0.341 a1 0.282 a2 0.321 a3 0.341 b1 217.8 b2 254.7 b3 204.7 b1 217.8 b2 254.7 b3 204.7 c1 c20.871 20.87 c2 c2 23.1723.17 c3c3 61.9 61.9

ItIt is is thus thus clear fromfrom FigureFigure6 6that that the the value value of summerof summer maize maize MODIS MODIS EVI EVI increased increased first first and thenand thendecreased decreased with with time time in the in reference the reference area, area, consisting consisting of the of growth the growth trajectory trajectory of summer of summer maize. maize. From FromDOY DOY 157 to 157 225, to summer 225, summer maize maize was in was the in periods the periods of sowing of sowing to tasseling, to tasseling, which which was the was rapid the growth rapid growthstage, and stage, the and EVI the value EVI gradually value gradually increased. increased. It entered It the entered period the of period tasseling of aroundtasseling DOY around 225, whichDOY 225,was which the most was vigorous the most growth vigorous stage, growth and the stage, value and of EVIthe alsovalue reached of EVI the also peak reached value. the From peak DOY value. 225 Fromto 258, DOY summer 225 to maize 258, wassummer in the maize periods was of in tasseling the periods to maturation, of tasseling and to graduallymaturation, matured and gradually with the matureddecrease with in EVI the value. decrease in EVI value.

FigureFigure 6. Moderate 6. Moderate Resolution Resolution Imaging Imaging Spectroradiometer Spectroradiometer (MODIS) (MODIS) EVI EVI time time series series curve curve of summer of maize in the reference area. summer maize in the reference area. 4.2. Construction of Standard Summer Maize EVI Time Series Curve in the Study Area 4.2. Construction of Standard Summer Maize EVI Time Series Curve in the Study Area 4.2.1. Estimation of Summer Maize Phenology 4.2.1. Estimation of Summer Maize Phenology The summer maize phenology is divided into eight key stages, which are sowing, seedling emergence,The summer trefoil, maize seven-leaf, phenology jointing, is tasseling, divided milk-ripe,into eight and key maturation. stages, which The are phenology sowing, of seedling summer emergence,maize in di fftrefoil,erent regions seven varies‐leaf, jointing, due to the tasseling, different naturalmilk‐ripe, environments. and maturation. In this study,The phenology employing theof summer maize in different regions varies due to the different natural environments. In this study,

Appl. Sci. 2020, 10, x; doi: FOR PEER REVIEW www.mdpi.com/journal/applsci Appl. Sci. 2020, 10, 2667 10 of 21 multiple linear stepwise regression method, the phenological observation data and the corresponding meteorological data of 32 agrometeorological stations were analyzed, and the multiple linear regression equations between summer maize phenology and natural environmental factors were constructed. The specific formulas are as follows:

Y = 1.24X 0.0026X + 0.0087X + 29.27 (6) 1 1 − 4 5

Y2 = 1.07X1 + 0.68X2 + 23.60 (7) Y = 1.12X + 0.58X 0.0030X + 40.51 (8) 3 1 2 − 4 Y = 1.17X 0.0054X + 77.97 (9) 4 1 − 4 Y = 1.20X 0.0049X + 85.79 (10) 5 1 − 4 Y = 1.06X 0.0059X + 123.9 (11) 6 1 − 4 Y = 1.34X 0.54X 0.0124 X + 155.36 (12) 7 1 − 3 − 4 Y = 0.64X + 1.04X 0.0112X + 207.9 (13) 8 1 2 − 4 where Y1, Y2, Y3, Y4, Y5, Y6, Y7, and Y8 are the analog DOY of the sowing, seedling emergence, trefoil, seven-leaf, jointing, tasseling, milk-ripe, and maturation stages, respectively; X1 is the longitude, X2 is the latitude, X is the annual average temperature, X is the annual precipitation, and X is the 10 C 3 4 5 ≥ ◦ accumulated temperature. The phenology observation data in 2007, 2009, 2011, and 2012 were used to verify the analog values of summer maize phenology that were calculated by the multiple linear regression equations (Equations (6) to (13)) at 32 agro-meteorological stations, respectively. The correlation coefficient (R) values were between 0.559 and 0.790 (Figure7), which showed that the correlation between the simulated phenology and the actual phenology of summer maize was significant. It was illustrated that the summer maize phenology simulated by multiple linear regression equations were reliable in the current study. According to the multiple linear regression equations, the simulated results of summer maize phenology in the study area were calculated and are shown in Figure8. Generally, the eight growth stages of summer maize in Hunaghuaihai Plain showed a trend of delaying from the southwest to northeast. The differences in seedling emergence, seven-leaf, and joint stages were the smallest, and the DOY discrepancy among the regions was about 16 days in the study area, whereas the difference in the maturation phase of summer maize was largest, up to about 32 days. The occurrence of this event may be related to the climate with high temperature in summer and a larger temperature difference between North and South in winter in Huanghaihai Plain [64,65]. Based on the summer maize phenology of Ruzhou City (Table1) and the simulated phenology of summer maize in the study area, the lag at each pixel from the sowing stage to maturation stage of maize was roughly calculated using the following equations. The corresponding values of i and s are shown in Table3. Appl. Sci. 2020, 10, 2667 11 of 21 Appl. Sci. 2020, 10, x FOR PEER REVIEW 13 of 23

2007 2009 2011 2012 175 175 175 175 R = 0.693 R = 0.678 R = 0.663 R = 0.724 170 170 170 170 Sowing 165 165 165 165 160 160 160 160 155 155 150 160 170 180 150 160 170 180 150 160 170 180 150 160 170 180

180 180 185 180 R = 0.672 R = 0.708 R = 0.666 R = 0.565 180 175 175 175 Seeding 175 170 170 170 emergence 170 165 165 165 165 155 165 175 185 155 165 175 185 160 165 170 175 180 185 160 170 180 190

190 185 185 185 R = 0.687 R = 0.776 R = 0.630 R = 0.614 185 180 180 180 175 175 175 Trefoil 175 170 170 170 165 165 165 165 160 170 180 190 160 170 180 190 160 170 180 190 165 175 185 Simulation

200 195 200 200 R = 0.750 R = 0.747 R = 0.603 R = 0.657 195 195 195

Seven‐leaf phenology 190 190 190 190 185 185 185 185 180 180 180 180 175 185 195 205 170 180 190 200 175 185 195 205 175 185 195 205

of

summer

210 210 210 210 R = 0.593 R = 0.743 R = 0.567 R = 0.559

205 205 205 205 Jointing maize

200 200 200 200

(DOY) 195 195 195 195 180 190 200 210 220 185 195 205 215 185 195 205 215 190 200 210 220

230 225 230 230 R = 0.760 R = 0.789 R = 0.605 R = 0.684 225 225 225 Tasseling 220 220 220 220 215 215 215 215 210 210 210 210 200 210 220 230 240 200 210 220 230 200 210 220 230 240 200 210 220 230 240

260 255 260 260 R = 0.653 R = 0.709 R = 0.723 R = 0.724 250 Milk‐ripe 250 250 250 245 240 240 240 240 230 235 230 230 230 240 250 260 225 235 245 255 265 225 235 245 255 265 230 240 250 260

275 275 280 290 R = 0.696 R = 0.727 R = 0.634 R = 0.680 270 270 280 265 270 265 270 260 Maturation 260 260 255 260 255 250 250 250 240 250 260 270 280 290 240 250 260 270 280 250 260 270 280 250 260 270 280

The actual phenology of summer maize (DOY)

FigureFigure 7. Correlation7. Correlation between between actual actual summer summer maize maize phenology phenology and simulated and simulated summer summer maize phenology maize withphenology environmental with environmental factors in Huanghuaihai factors in Huanghuaihai Plain in 2012. Plain in 2012.

Appl. Sci. 2020, 10, x; doi: FOR PEER REVIEW www.mdpi.com/journal/applsci Appl. Sci. 2020, 10, x FOR PEER REVIEW 14 of 23

According to the multiple linear regression equations, the simulated results of summer maize phenology in the study area were calculated and are shown in Figure 8. Generally, the eight growth stages of summer maize in Hunaghuaihai Plain showed a trend of delaying from the southwest to northeast. The differences in seedling emergence, seven‐leaf, and joint stages were the smallest, and the DOY discrepancy among the regions was about 16 days in the study area, whereas the difference in the maturation phase of summer maize was largest, up to about 32 days. The occurrence of this Appl. Sci. 2020, 10, 2667 12 of 21 event may be related to the climate with high temperature in summer and a larger temperature difference between North and South in winter in Huanghaihai Plain [64,65].

Figure 8. The simulated maize phenology using environmental factors in Huanghuaihai Plain in 2012. Figure 8. The simulated maize phenology using environmental factors in Huanghuaihai Plain in 2012.

Based on the summer maize phenology of Ruzhou City (Table 1) and the simulated phenology Lagi = Ys Yrs (14) of summer maize in the study area, the lag at each pixel− from the sowing stage to maturation stage ofwhere maizei is was the roughly day of the calculated year (DOY); using s represents the following the time equations. series of The summer corresponding maize phenology; values ofLag i andi are s arethe shown day’s delay in Table from 3. DOY 161 to 289, and Ys and Yrs represent the simulated summer maize phenology of the study area and the phenology of summer maize in the reference area on DOY i, respectively. Lagi = Ys − Yrs (14) The growth stages of summer maize used in Equation (14) are the periods of seedling emergence, whereseven-leaf, i is the jointing, day of tasseling, the year milk-ripe,(DOY); s represents and maturation. the time series of summer maize phenology; Lagi are the day’s delay from DOY 161 to 289, and Ys and Yrs represent the simulated summer maize phenology of the Tablestudy 3. areaThe correspondingand the phenology values ofof i,summers, and summer maize maize in the phenology. reference area on DOY i, respectively.i The 161 growth stages 177 of 193summer 209 maize used 225 in Equation 241 (14) 257 are the periods 273 of seedling 289 emergence,s seven2‐leaf, jointing, 4 tasseling, 5 milk 6‐ripe, and 6 maturation. 7 8 8 8 Summer Seedling maize Seven-leaf Join-ting Tassel-ing Tassel-ing Milk-ripe Maturation Maturation Maturation emergenceTable 3. The corresponding values of i, s, and summer maize phenology. phenology i 161 177 193 209 225 241 257 273 289 s 2 4 5 6 6 7 8 8 8 4.2.2. Construction of Standard Summer Maize EVI Time Series Curve in the Study Area Summer maize Seedling Seven‐ Join‐ Tassel‐ Tassel‐ Milk‐ The phenology of summer maize is different in various regions on aMaturation large scale. Maturation The features Maturation of the phenology emergence leaf ting ing ing ripe summer maize MODIS EVI time series curve in the reference area cannot represent the MODIS EVI time series curve of the entire study area. It was necessary to modify the summer maize MODIS EVI time 4.2.2. Construction of Standard Summer Maize EVI Time Series Curve in the Study Area series according to the various analog phenologies of summer maize in Huanghuaihai Plain. Hence, in the currentThe phenology research, of employing summer maize the lag is value different of summer in various maize regions phenology on a large to correct scale. Equation The features (2), the of thestandard summer EVI maize time seriesMODIS equation EVI time of summer series curve maize in in the the reference study area area was cannot obtained. represent The formula the MODIS is as follows: Appl. Sci. 2020, 10, x; doi: FOR PEER REVIEW www.mdpi.com/journal/applsci 2 2 2 (i Lag ) b (i Lag ) b (i Lag ) b ( − i − 1 ) ( − i − 2 ) ( − i − 3 ) SEVI = a e − c1 + a e − c2 + a e − c3 (i = 145, 177 289) (15) i 1 × 2 × 3 × ··· where SEVIi is the standard summer maize EVI value of the study area on DOY i, and the parameters of the standard EVI function are shown in Table2. Appl. Sci. 2020, 10, 2667 13 of 21

4.2.3. Validation of Standard Summer Maize EVI Time Series Curve in the Study Area Ten counties were randomly selected in the study area to validate the standard summer maize EVI time series curve. According to the method in Section 3.1, the planting area of summer maize in 10 validated regions was mapped by Landsat-7 ETM+ images. The maize distribution region extracted by Landsat was used to mask the MODIS EVI time images, and the actual MODIS EVI time series values of summer maize in 10 validated areas were obtained by calculation. The actual MODIS EVI time series values of summer maize were compared with the standard EVI time series values of summer maize by Equation (15). The results showed that the correction coefficient between the actual summer maize MODIS EVI time series curve and the standard summer maize EVI time series curve was all greater than 0.9 with p < 0.01 (Table4). It indicated that the standard curve simulated by Equation (15) was consistent with the actual curve.

Table 4. Correlation analysis between the actual MODIS EVI curve and the simulation standard EVI curve of maize.

Bao-di Dong-ng Gao-Mi Gu-Cheng Kai-Feng Lu-Shan Meng-Hou Shou-Uang Xi-Ping Xu-Shui R 0.905 ** 0.917 ** 0.907 ** 0.926 ** 0.989 ** 0.955 ** 0.989 ** 0.922 ** 0.968 ** 0.912 ** Sig. 0.002 0.001 0.002 0.000 0.000 0.000 0.000 0.000 0.000 0.001 (Two-tailed) N9999999999 Note: ** Indicates the correlation is significant with p < 0.01.

4.3. Summer Maize Recognition in Huanghuaihai Plain

4.3.1. The MAD between Standard Summer Maize EVI and Actual MODIS EVI The MAD map of the study area is shown in Figure9, where the minimum value is 0 and the maximum is 6. The lower the MAD value, the greater the similarity between the actual EVI value and the simulated EVI value, which indicated that the summer maize planting area is more likely. On the contrary, the summer maize planting area is less likely. According to the p-tile algorithm, the optimal thresholds of five provinces were determined: 0.85 for Beijing, 0.89 for Tianjin, 0.71 for Hebei, 0.59 for Henan, and 0.74 for Shandong. The value of MAD that was smaller than the optimal threshold was considered to be the summer maize area by comparing every raster value of the MAD map with the optimal threshold value, which was used to extract the spatial distribution of summer maize in HuanghuaihaiAppl. Sci. 2020 Plain., 10, x FOR PEER REVIEW 16 of 23

FigureFigure 9. The 9. The mean mean absolute absolute distance distance (MAD)(MAD) map map in in Huanghuaihai Huanghuaihai Plain Plain in 2012. in 2012.

4.3.2. Extraction Results of Summer Maize in Huanghuaihai Plain The spatial distribution of the summer maize planting area of the study area is shown in Figure 10. It can be seen from Figure 10 that the distribution of summer maize is sparse in Beijing, Tianjin, and the western and southern parts of Henan Province, and uniform in other regions of Huanghuaihai Plain. The event is owed to the agricultural planting structure, social development, and ecological environment in China. There is less area for agricultural planting in Beijing and Tian as the developed cities. Rice and wheat are the main crops in the south of Henan Province, and woodland covers most of the western Henan Province. Hebei, Shandong, and the central and northern parts of Henan Province are flat with good photoperiod and temperature conditions, which are suitable for maize growth [66,67]. The spatial distribution of summer maize in the current study was consistent with the previous extracted results [68] in northern and southern Hebei Province, northwest of Shandong Province, and northern Henan Province, which can manifest the extracted results with a certain reliability.

Figure 10. The distribution of the maize planting area in Huanghuaihai Plain in 2012.

Appl. Sci. 2020, 10, x; doi: FOR PEER REVIEW www.mdpi.com/journal/applsci Appl. Sci. 2020, 10, x FOR PEER REVIEW 16 of 23

Appl. Sci. 2020, 10Figure, 2667 9. The mean absolute distance (MAD) map in Huanghuaihai Plain in 2012. 14 of 21

4.3.2. Extraction Results of Summer Maize in Huanghuaihai Plain 4.3.2. Extraction Results of Summer Maize in Huanghuaihai Plain The spatial distribution of the summer maize planting area of the study area is shown in Figure 10. It Thecan spatialbe seen distribution from Figure of 10 the that summer the distribution maize planting of summer area of themaize study is sparse area is shownin Beijing, in Figure Tianjin, 10 . andIt can the be seenwestern from and Figure southern 10 that theparts distribution of Henan of summerProvince, maize and isuniform sparse inin Beijing, other Tianjin,regions andof Huanghuaihaithe western and Plain. southern The event parts is of owed Henan to Province, the agricultural and uniform planting in otherstructure, regions social of Huanghuaihaidevelopment, andPlain. ecological The event environment is owed to in the China. agricultural There is planting less area structure, for agricultural social development, planting in Beijing and ecological and Tian asenvironment the developed in China. cities. There Rice isand less wheat area for are agricultural the main crops planting in inthe Beijing south andof Henan Tian as Province, the developed and woodlandcities. Rice covers and wheat most are of the the main western crops inHenan the south Province. of Henan Hebei, Province, Shandong, and woodland and the covers central most and of northernthe western parts Henan of Henan Province. Province Hebei, are Shandong, flat with good and thephotoperiod central and and northern temperature parts ofconditions, Henan Province which areare suitable flat with for good maize photoperiod growth [66,67]. and temperature The spatial conditions, distribution which of summer are suitable maize for maizein the growthcurrent [study66,67]. wasThe consistent spatial distribution with the previous of summer extracted maize inresults the current [68] in studynorthern was and consistent southern with Hebei the Province, previous northwestextracted results of Shandong [68] in northernProvince, and and southern northern Hebei Henan Province, Province, northwest which can of Shandong manifest Province,the extracted and resultsnorthern with Henan a certain Province, reliability. which can manifest the extracted results with a certain reliability.

FigureFigure 10. 10. TheThe distribution distribution of of the the maize maize planting planting area area in in Huanghuaihai Huanghuaihai Plain Plain in in 2012. 2012.

Appl.4.3.3. Sci. Validation 2020, 10, x; doi: of the FOR Extracted PEER REVIEW Summer Maize Area in Huanghuaihai Plainwww.mdpi.com/journal/applsci The cultivated areas of summer maize in Beijing, Tianjin, Hebei, Henan, and Shandong extracted by MODIS EVI time series images were 129.7 103, 175.4 103, 2995.4 103, 3322.7 103, and × × × × 3428.2 103 ha, respectively. The planting area of summer maize was largest in Henan Province × and the smallest in Beijing. The statistics and the extraction of summer maize were used to analyze the overestimation and underestimation of area extraction. If the comparison results were positive, it indicated that the extraction area was larger than the statistical area, which was overestimation. Otherwise, if the comparison was negative, it was underestimated. As can be seen from Table5, the extraction of summer maize was overestimated in Hebei, Henan, and Shandong, and underestimated in Beijing and Tianjin. The extraction accuracy of Tianjin Province was the highest, reaching 98.04%, and Shandong Province was the lowest, 86.41%. The accuracy of the extraction results of five Provinces was over 85% in the study. Appl. Sci. 2020, 10, x FOR PEER REVIEW 17 of 23

4.3.3. Validation of the Extracted Summer Maize Area in Huanghuaihai Plain The cultivated areas of summer maize in Beijing, Tianjin, Hebei, Henan, and Shandong extracted by MODIS EVI time series images were 129.7 × 103, 175.4 × 103, 2995.4 × 103, 3322.7 × 103, and 3428.2 × 103 ha, respectively. The planting area of summer maize was largest in Henan Province and the smallest in Beijing. The statistics and the extraction of summer maize were used to analyze the overestimation and underestimation of area extraction. If the comparison results were positive, it indicated that the extraction area was larger than the statistical area, which was overestimation. Otherwise, if the comparison was negative, it was underestimated. As can be seen from Table 5, the extraction of summer maize was overestimated in Hebei, Henan, and Shandong, and underestimated in Beijing and Tianjin. The extraction accuracy of Tianjin Province was the highest, reaching 98.04%, and Shandong Province was the lowest, 86.41%. The accuracy of the extraction results of five Provinces was over 85% in the study. Appl. Sci. 2020, 10, 2667 15 of 21 Table 5. Comparison of extracted area and statistical data of summer maize for each province in 2012.

Table 5. Comparison ofStatistical extracted area andExtracted statistical data ofHigh summer (+) maize or for each province in 2012. Province Accuracy/% 3 3 Area/10Statisticalha Area/10Extractedha LowHigh (− (+)/%) or Low Province Accuracy/% Beijing Area133.0/10 3 ha 129.7Area/ −103 ha 2.51( ) /% 97.49 − TianjinBeijing 179.0 133.0 175.4 129.7 −1.962.51 98.04 97.49 − HebeiTianjin 2684.3 179.0 2995.4 175.4 11.591.96 88.41 98.04 − HenanHebei 3100.0 2684.3 3322.7 2995.4 7.18 11.59 92.81 88.41 ShandongHenan 3010.0 3100.0 3428.2 3322.7 13.59 7.18 86.41 92.81 Shandong 3010.0 3428.2 13.59 86.41 Note: High (+) or low (−)/% = (statistical area‐extracted area)/statistical area×100, and accuracy (%) is Note: High (+) or low ( )/% = (statistical area-extracted area)/statistical area 100, and accuracy (%) is 100-Abs (high100‐Abs or low). (high or low).− ×

The statistical data data of of the the maize maize area area from from 43 43cities cities and and 245 245counties counties in Huanghuaihai in Huanghuaihai Plain Plainwere wereemployed employed to verify to verify the extraction the extraction results. results. Comparing Comparing the maize the acreage maize acreageof two groups of two from groups MODIS from‐ MODIS-estimatedestimated and statistical and statistical data, the data, validation the validation results resultsshowed showed that the that R2 thereached R2 reached 0.81 with 0.81 an with Root an RootMean Mean Square Square Error Error (RMSE) (RMSE) of 143.8 of 143.8 ha at ha the at city the level city level and the and R the2 reached R2 reached 0.69 with 0.69 with an RMSE an RMSE of 18.7 of 18.7ha at ha the at county the county level level (Figure (Figure 11). 11Those). Those demonstrated demonstrated that thatthere there was was a good a good correlation correlation between between the themaize maize area area estimated estimated by MODIS by MODIS satellite satellite data and data statistical and statistical data. Compared data. Compared to the previous to the previous research researchthat used that the usedmulti the‐temporal multi-temporal remote sensing remote images sensing to imagesmap the to summer map the maize summer area, maize the extraction area, the extractionaccuracy was accuracy significantly was significantly improved improvedin the current in the research current [20,21,69]. research [ 20,21,69].

120 700 y = 1.06x + 1.98 . y = 1.14x ‐ 8.45 . R2 = 0.69 . R2 = 0.811 . RMSE = 18.7×103 ha 600 RMSE = 143.8 ×103 ha 100 N = 245 .

on N = 43 . ha)

3

500 on

ha) 80

(10 3

/ based

/(10

400 based

area

60 sensing area

300 sensing 40 Extracted remote 200 xtracted E remote 1:1 line 20 100 Fitted line 0 0 0 100 200 300 400 500 600 700 0 20406080100120 Statistical area /(103 ha) Statistical area /(103 ha) a. City level b. County level

Figure 11.11. Correlation between the extracted area based on remote sensing and statistical area of summer maize in Huanghuaihai PlainPlain inin 2012.2012.

Six county-level regions (, Gaotang, Linzhang, Lushan, Yongnian, Xinji) were selected fromAppl. Sci. the 2020 study, 10, x; area doi: asFOR the PEER sampling REVIEW validation areas. Based on Landsat ETMwww.mdpi.com/journal/applsci+ images with a spatial resolution of 30 m, the distribution regions of summer maize in the six regions in 2012 were obtained by the method used to estimate summer maize area in the reference area in Section 3.1. As shown in Figure 12, the summer maize distribution areas mapped by ETM+ images were roughly consistent with those drawn by MODIS images. The extraction results were compared to the summer maize distribution areas drawn based on the MODIS data. The two kinds of maize distribution areas were superposed and calculated to obtain the area of the public part. The public area is 29.4 ha in Changyuan, 37.7 ha in Gaotang, 36.6 ha in Linzhang, 24.7 ha in Lushan, 33.9 ha in Yongnian, and 43.2 ha in Xinji (Table6). The proportions of the public part to the extraction results by Landsat images, the extraction results by MODIS images, and the statistical data were used to quantitatively analyze the relative accuracy of summer maize extracted with MODIS data. The proportion of the public part area to the extraction results based on MODIS and Landsat images was compared. The results showed that Appl. Sci. 2020, 10, x FOR PEER REVIEW 18 of 23

Six county‐level regions (Changyuan, Gaotang, Linzhang, Lushan, Yongnian, Xinji) were selected from the study area as the sampling validation areas. Based on Landsat ETM+ images with a spatial resolution of 30 m, the distribution regions of summer maize in the six regions in 2012 were obtained by the method used to estimate summer maize area in the reference area in Section 3.1. As shown in Figure 12, the summer maize distribution areas mapped by ETM+ images were roughly consistent with those drawn by MODIS images. The extraction results were compared to the summer maize distribution areas drawn based on the MODIS data. The two kinds of maize distribution areas were superposed and calculated to obtain the area of the public part. The public area is 29.4 ha in Changyuan, 37.7 ha in Gaotang, 36.6 ha in Linzhang, 24.7 ha in Lushan, 33.9 ha in Yongnian, and 43.2 ha in Xinji (Table 6). The proportions of the public part to the extraction results by Landsat images, theAppl. extraction Sci. 2020, 10 results, 2667 by MODIS images, and the statistical data were used to quantitatively analyze16 of 21 the relative accuracy of summer maize extracted with MODIS data. The proportion of the public part area to the extraction results based on MODIS and Landsat images was compared. The results showedthe gap that was the largest gap inwas Lushan largest county, in Lushan with county, 90.7% andwith 62.5%, 90.7% and respectively; 62.5%, respectively; the gap was the smallest gap was in smallestChangyuan, in Changyuan, 67.7% and 67.7% 68.6%, and respectively 68.6%, respectively (Table6). The (Table mean 6). proportion The mean ofproportion the public of part the areapublic to partthe statisticalarea to the data statistical was 82.3%, data was which 82.3%, meets which the request meets the for precision.request for These precision. suggest These that suggest the method that theproposed method in proposed this paper in is this reliable. paper is reliable.

(a) Changyuan (b) Gaotang (c) Linzhang

(d) Lushan (e) Xinji (f) Yongnian

FigureFigure 12. 12. ComparisonComparison of of spatial spatial distribution distribution of of summer summer maize maize extraction extraction results results based based on on MODIS MODIS andand Landsat Landsat images images in in 2012 2012 (Left: (Left: MODIS, MODIS, Right: Right: Landsat). Landsat). Table 6. Comparison of summer maize extraction results based on MODIS and Landsat images in 2012. Table 6. Comparison of summer maize extraction results based on MODIS and Landsat images in 2012. Proportions Proportions Proportions Extracted Extracted Statistical Public of the Public of the Public of the Public Area of Area of Region Data/103 Area/103 toProportions the to theProportions Results toProportions the Results MODIS/103 Landsat/103 ha Extracted ha Statisticalof the Basedof on the Basedof the on Statistical ha haExtracted Public Area of DataPublic/% to MODISPublic/% to LandsatPublic/ %to Region Data/103 Area of Area/103 MODIS/103 the the Results the Results Changyua-n 29.4ha 29.7Landsat/10 29.33ha 20.1ha 68.4 67.7 68.6 Gaotang 37.7 34.0ha 34.4 28.3 75.1Statistical 83.2Based on Based 82.3 on Linzhang 36.6 43.9 37.5 32.9 89.9Data/% MODIS/% 74.9 Landsat/% 87.7 ChangyuaLushan 24.7 25.9 37.6 23.5 95.1 90.7 62.5 29.4 29.7 29.3 20.1 68.4 67.7 68.6 Yongnian‐n 33.9 35.0 25.3 22.3 65.8 63.7 88.1 Xinji 43.2 60.1 51.2 45.9 99.4 76.4 89.6 Gaotang 37.7 34.0 34.4 28.3 75.1 83.2 82.3 average — — — — 82.3 76.1 79.8 Linzhang 36.6 43.9 37.5 32.9 89.9 74.9 87.7 Lushan 24.7 25.9 37.6 23.5 95.1 90.7 62.5 4.3.4.Yongnian Extraction 33.9 Results in Di35.0fferent Years25.3 22.3 65.8 63.7 88.1 XinjiThe area extraction43.2 algorithm60.1 in the51.2 present research45.9 was employed99.4 to map76.4 the summer89.6 maize average — — — — 82.3 76.1 79.8 cultivated area in 2013 over Huanghuaihai Plain. The spatial distribution characteristics of summer maize in 2013 were basically consistent with those in 2012. It can be seen from Table7 that the summer maize acreage from MODIS images in Beijing, Tianjin, Hebei, Henan, and Shandong were 105.1 103, 4.3.4. Extraction Results in Different Years × 178.2 103, 2630.4 103, 3104.3 103, and 2707.0 103 ha, respectively. Compared to the statistical × × × × area, the precision of summer maize extraction results in five provinces was all above 85%. Meanwhile, the extraction results of the summer maize area from 43 cities and 245 countries of the study area in 2013 2 3 Appl.were Sci. compared 2020, 10, x; todoi: the FOR statistical PEER REVIEW data, which showed that R was 0.81 withwww.mdpi.com/journal/applsci an RMSE of 132.6 10 ha × at the city level and R2 was 0.68 with RMSE of 17.3 103 ha at the county level (Figure 13). It can × be found that the precision of extraction results in different years had a high accuracy by using the standard summer maize EVI time series to map the summer maize, indicating that the method has a certain universality over a large scale. Appl. Sci. 2020, 10, x FOR PEER REVIEW 19 of 23

The area extraction algorithm in the present research was employed to map the summer maize cultivated area in 2013 over Huanghuaihai Plain. The spatial distribution characteristics of summer maize in 2013 were basically consistent with those in 2012. It can be seen from Table 7 that the summer maize acreage from MODIS images in Beijing, Tianjin, Hebei, Henan, and Shandong were 105.1 × 103, 178.2 × 103, 2630.4 × 103, 3104.3 × 103, and 2707.0 × 103 ha, respectively. Compared to the statistical area, the precision of summer maize extraction results in five provinces was all above 85%. Meanwhile, the extraction results of the summer maize area from 43 cities and 245 countries of the study area in 2013 were compared to the statistical data, which showed that R2 was 0.81 with an RMSE of 132.6 × 103 ha at the city level and R2 was 0.68 with RMSE of 17.3 × 103 ha at the county level (Figure 13). It can be found that the precision of extraction results in different years had a high accuracy by using the standard summer maize EVI time series to map the summer maize, indicating Appl. Sci. 2020, 10, 2667 17 of 21 that the method has a certain universality over a large scale.

600 y = 1.03x ‐ 13.86 120 y = 0.93x + 0.15 R² = 0.81 R2 = 0.68 × 3 3 500 RMSE=132.6 10 ha 100 RMSE = 17.3×10 ha

N=43 N = 245

on

ha)

on

ha) 3 400 3 80 /(10

based /(10

based

300 60 area

area

sensing

sensing

200 40 Extracted remote Extracted remote 1:1 100 20 Fitted 0 0 0 100 200 300 400 500 600 0 20406080100120 Statistical area /(103 ha) Statistical area /(103 hm2) a. City level b. County level

Figure 13. Correlation between the extracted area based on remote sensing and statistical area of Figure 13. Correlation between the extracted area based on remote sensing and statistical area of summer maize in Huanghuaihai Plain in 2013; (a) City level, (b). County level. summer maize in Huanghuaihai Plain in 2013; (a) City level, (b). County level. Table 7. Comparison of extracted area and statistical data of summer maize for each province in 2013. Table 7. Comparison of extracted area and statistical data of summer maize for each province in 2013. Statistical Extracted Area/103 High (+) or Low Province Accuracy/% StatisticalArea/103 ha Extractedha High (+) or( )low/% Province Accuracy/% 3 3 − BeijingArea/10 114.5ha Area/10 105.1ha (−)/% 8.21 91.79 − BeijingTianjin 114.5 191.7 105.1 178.2 −8.21 7.0591.79 92.95 − TianjinHebei 191.7 2693.6 178.2 2630.4 −7.05 2.3392.95 97.67 − Henan 3203.3 3104.3 3.09 96.91 Hebei 2693.6 2630.4 −2.33− 97.67 Shandong 3060.7 2707.0 11.56 88.44 Henan 3203.3 3104.3 −3.09− 96.91 Note:Shandong High (+) or low ( )3060.7/% = (statistical area-extracted2707.0 − area)/statistical area11.56 100, and accuracy88.44 (%) is 100-Abs (high or low). − × Note: High (+) or low (−)/% = (statistical area‐extracted area)/statistical area×100, and accuracy (%) is 5. Conclusions100‐Abs (high or low).

5. ConclusionsThe present study demonstrates a promising approach for mapping a summer maize cultivated area over a large scale, which considered the phenological differences of maize among the different The present study demonstrates a promising approach for mapping a summer maize cultivated regions. In practicality, the relationship between summer maize calendars and environmental factors area over a large scale, which considered the phenological differences of maize among the different was constructed and environmental factor data were employed to simulate the summer maize regions. In practicality, the relationship between summer maize calendars and environmental factors phenology in the whole study area. Simulated phenology of summer maize acted as a parameter was constructed and environmental factor data were employed to simulate the summer maize to obtain a standard EVI time series curve of summer maize. A MAD map was calculated through phenology in the whole study area. Simulated phenology of summer maize acted as a parameter to comparison between time series images of standard summer maize EVI and the actual MODIS-EVI. obtain a standard EVI time series curve of summer maize. A MAD map was calculated through Appropriate thresholds were set in five different provinces to map spatial distributions of summer comparison between time series images of standard summer maize EVI and the actual MODIS‐EVI. maize in the study area. This research has demonstrated that, in terms of precision analysis, the Appropriate thresholds were set in five different provinces to map spatial distributions of summer accuracy of the extraction result in each province was above 85%. Statistical data from the Statistical Appl.Yearbook Sci. 2020 were, 10, x; gathereddoi: FOR PEER for accuracyREVIEW evaluation, and the results showed thatwww.mdpi.com/journal/applsci the R2 reached 0.81 with an RMSE of 143.8 ha at the city level and the R2 reached 0.69 with an RMSE of 18.7 ha at the county level. Those indicated a robust potential for identifying areas where the summer maize was cultivated over a large scale. Of course, there are still many challenges in the estimation of the crop planting area while considering the complex agricultural structure, including crop types, management practices, and crop interplanting operation. Further studies can also attempt to use more high-spatial-resolution images to map the crop cultivated area and improve the accuracy of extraction results on a large scale.

Author Contributions: Conceptualization, J.Z. and F.D.; methodology, J.Z., X.W. and F.D.; validation, S.Z. and L.F.; writing—original draft preparation, X.W.; writing—review and editing, J.Z.; project administration, J.Z. All authors have read and agreed to the published version of the manuscript. Appl. Sci. 2020, 10, 2667 18 of 21

Funding: This work was jointly supported by the National Key Research and Development Program of China (No. 2016YFD0300101, 2016YFD0300110), the Shandong Key Research and Development Project (No. 2018GNC110025), “Taishan” Scholarship Project of Shandong Province (No. TSXD201712); and the Natural Science Foundation of China (No. 31671585, 41871253). Acknowledgments: We are very grateful to the editor and four anonymous reviewers for their valuable comments and suggestions. Conflicts of Interest: The authors declare no conflict of interest.

References

1. Zhao, J.R.; Wang, R.H. Development Process, Problem and Countermeasure of Maize Production in China. J. Agric. Sci. Technol. 2013, 15, 1–6. 2. Chen, S.; Kung, J.K.-S. Of maize and men: The effect of a New World crop on population and economic growth in China. J. Econ. Growth 2016, 21, 71–99. [CrossRef] 3. Ping, H.M.; Min, H.J.; Liu, P.H.; Lin, L.X. Research of Opportunities, Challenges and Countermeasures about WTO on Huanghuaihai Plain Agricultural Development. Res. Agric. Mod. 2003, 4, 28–32. 4. Kai, W.; Bu, L. Systematic analysis and optimized prospect for agriculture structure in Huanghuaihai Plain. Chin. J. Agric. Resour. Reg. Plan. 2007, 28, 22–25. 5. Dong, J.; Xiao, X. Evolution of regional to global paddy rice mapping methods: A review. ISPRS J. Photogramm. Remote Sens. 2016, 119, 214–227. [CrossRef] 6. Forkuor, G.; Conrad, C.; Thiel, M.; Zoungrana, B.J.B.; Tondoh, J.E. Multiscale Remote Sensing to Map the Spatial Distribution and Extent of Cropland in the Sudanian Savanna of West Africa. Remote Sens. 2017, 9, 839. [CrossRef] 7. Kyere, I.; Astor, T.; Grass, R.; Wachendorf, M. Multi-Temporal Agricultural Land-Cover Mapping Using Single-Year and Multi-Year Models Based on Landsat Imagery and IACS Data. Agronomy 2019, 9, 309. [CrossRef] 8. Zhang, S.; Zhang, J.H.; Bai, Y.; Yao, F.M. Extracting winter wheat area in Huanghuaihai Plain using MODIS-EVIdata and phenology difference avoiding threshold. Trans. Chin. Soc. Agric. Eng. 2018, 34, 150–158. [CrossRef] 9. Zhang, M.W.; Zhou, Q.B.; Chen, Z.X.; Liu, J.; Zhou, Y.; Cai, C.F. Crop discrimination in Northern China with double cropping systems using Fourier analysis of time-series MODIS data. Int. J. Appl. Earth Obs. Geoinf. 2008, 10, 476–485. [CrossRef] 10. Tamilarasan, V.; Sharma, S.K.; Nagabhushana, S.R. Optimum spectral bands for land cover discrimination. Adv. Space Res. 1983, 3, 287–290. [CrossRef] 11. Salmon, B.P.; Kleynhans, W.; Olivier, J.C.; van den Bergh, F.; Wessels, K.J. A modified temporal criterion to meta-optimize the extended Kalman filter for land cover classification of remotely sensed time series. Int. J. Appl. Earth Obs. Geoinf. 2018, 67, 20–29. [CrossRef] 12. Carrão, H.; Gonçalves, P.; Caetano, M. Contribution of multispectral and multitemporal information from MODIS images to land cover classification. Remote Sens. Environ. 2008, 112, 986–997. [CrossRef] 13. Chang, J.; Hansen, M.C.; Pittman, K.; Carroll, M.; Dimiceli, C. Corn and Soybean Mapping in the United States Using MODIS Time-Series Data Sets. Agron. J. 2007, 99, 1654–1664. [CrossRef] 14. Huete, A.; Didan, K.; Miura, T.; Rodriguez, E.P.; Gao, X.; Ferreira, L.G. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 2002, 83, 195–213. [CrossRef] 15. Wardlow, B.; Egbert, S.; Kastens, J. Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains. Remote Sens. Environ. 2007, 108, 290–310. [CrossRef] 16. Bendini, H.D.N.; Fonseca, L.M.G.; Schwieder, M.; Korting, T.S.; Rufin, P.; Sanches, I.D.A.; Leitao, P.J.; Hostert, P. Detailed agricultural land classification in the Brazilian cerrado based on phenological information from dense satellite image time series. Int. J. Appl. Earth Obs. Geoinf. 2019, 82, 101872. [CrossRef] 17. Wardlow, B.D.; Egbert, S.L. Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the U.S. Central Great Plains. Remote Sens. Environ. 2008, 112, 1096–1116. [CrossRef] Appl. Sci. 2020, 10, 2667 19 of 21

18. Gu, X.H.; Pan, Y.C.; He, X.; Wang, J.H. Estimation of Maize Planting Area through the Fusion of Multi-source Images. In Proceedings of the Computer and Computing Technologies in Agriculture V; Springer: Berlin/Heidelberg, Germany, 2012; pp. 470–477. 19. Maguranyanga, C.; Murwira, A. Mapping Maize, Tobacco, and Soybean Fields in Large-Scale Commercial Farms of Zimbabwe Based on Multitemporal NDVI Images in MAXENT. Can. J. Remote Sens. 2014, 40, 396–405. [CrossRef] 20. Tang, K.; Zhu, W.; Zhan, P.; Ding, S. An Identification Method for Spring Maize in Northeast China Based on Spectral and Phenological Features. Remote Sens. 2018, 10, 193. [CrossRef] 21. Zhang, J.H.; Feng, L.L.; Yao, F.M. Improved maize cultivated area estimation over a large scale combining MODIS–EVI time series data and crop phenological information. ISPRS J. Photogramm. Remote Sens. 2014, 94, 102–113. [CrossRef] 22. Wang, X.T.; Zhang, S.; Deng, F.; Zhang, J.H. Mapping the Cultivated Areas of Summer Maize Using Spatial Variations of Crop Phenology over Huanghuaihai Plain. Chin. J. Agrometeorol. 2019, 40, 647–659. 23. Wang, C.; Wu, J.; Wang, X.; He, X.; Li, N. Non-linear trends and fluctuations in temperature during different growth stages of summer maize in the North China Plain from 1960 to 2014. Theor. Appl. Climatol. 2019. [CrossRef] 24. Shen, M.; Zhang, G.; Cong, N.; Wang, S.; Kong, W.; Piao, S. Increasing altitudinal gradient of spring vegetation phenology during the last decade on the Qinghai–Tibetan Plateau. Agric. For. Meteorol. 2014, 189, 71–80. [CrossRef] 25. Li, L.; Friedl, M.; Xin, Q.; Gray, J.; Pan, Y.; Frolking, S. Mapping Crop Cycles in China Using MODIS-EVI Time Series. Remote Sens. 2014, 6, 2473–2493. [CrossRef] 26. Wang, Z.; Chen, J.; Li, Y.; Li, C.; Zhang, L.; Chen, F. Effects of climate change and cultivar on summer maize phenology. Int. J. Plant Prod. 2016, 10, 509–525. 27. Lizaso, J.I.; Ruiz-Ramos, M.; Rodríguez, L.; Gabaldon-Leal, C.; Oliveira, J.A.; Lorite, I.J.; Sánchez, D.; García, E.; Rodríguez, A. Impact of high temperatures in maize: Phenology and yield components. Field Crops Res. 2018, 216, 129–140. [CrossRef] 28. Wang, N.; Wang, E.; Wang, J.; Zhang, J.; Zheng, B.; Huang, Y.; Tan, M. Modelling maize phenology, biomass growth and yield under contrasting temperature conditions. Agric. For. Meteorol. 2018, 250, 319–329. [CrossRef] 29. Xiao, D.P.; Qi, Y.Q.; Shen, Y.J.; Tao, F.L.; Moiwo, J.P.; Liu, J.F.; Wang, R.D.; Zhang, H.; Liu, F.S. Impact of warming climate and cultivar change on maize phenology in the last three decades in North China Plain. Theor. Appl. Climatol. 2016, 124, 653–661. [CrossRef] 30. Ahmad, I.; Wajid, S.A.; Ahmad, A.; Cheema, M.J.M.; Judge, J. Assessing the Impact of Thermo-temporal Changes on the Productivity of Spring Maize under Semi-Arid Environment. Int. J. Agric. Biol. 2018, 20, 2203–2210. [CrossRef] 31. Riley, G.J. Effects of high temperature on the germination of maize (Zea mays L.). Planta 1981, 151, 68–74. [CrossRef] 32. Tan, M.-X.; Wang, J.; Yu, W.-D.; He, D.; Wang, N.; Dai, T.; Sun, Y.; Tang, J.-Z.; Chang, Q. Temporal and spatial variation of the optimal sowing dates of summer maize based on both statistical and processes models in Henan Province, China. J. Appl. Ecol. 2015, 26, 3670–3678. 33. Hou, P.; Liu, Y.; Xie, R.; Ming, B.; Ma, D.; Li, S.; Mei, X. Temporal and spatial variation in accumulated temperature requirements of maize. Field Crops Res. 2014, 158, 55–64. [CrossRef] 34. Guo, E.; Zhang, J.; Wang, Y.; Alu, S.; Wang, R.; Li, D.; Ha, S. Assessing non-linear variation of temperature and precipitation for different growth periods of maize and their impacts on phenology in the Midwest of Jilin Province, China. Theor. Appl. Climatol. 2018, 132, 685–699. [CrossRef] 35. Yan, H.; Zhen, Y.; Dongbing, W.U.; Cao, G.; Yao, J.; Liu, X. Effect of Latitude on Growth Period and Quality of Maize (Zea mays L.). Chin. Agric. Sci. Bull. 2009, 27, 38–41. 36. Lu, A.G.; Pang, D.Q.; He, Y.Q.; Pang, H.X.; Yuan, L.L. Impact of Global Warming on Latitudinal Temperature Gradients in China. Sci. Geogr. Sin. 2006, 26, 345–350. 37. Liu, Y.; Xie, R.; Hou, P.; Li, S.; Zhang, H.; Ming, B.; Long, H.; Liang, S. Phenological responses of maize to changes in environment when grown at different latitudes in China. Field Crops Res. 2013, 144, 192–199. [CrossRef] Appl. Sci. 2020, 10, 2667 20 of 21

38. Wang, J.a.; Xiao, H.; Hartmann, R.; Yue, Y. Physical Geography of China and the U.S. In A Comparative Geography of China and the U.S.; Hartmann, R., Wang, J.A., Ye, T., Eds.; Springer: Dordrecht, The Netherlands, 2014; pp. 23–81. [CrossRef] 39. Zhu, X. Features of climate changes in typical rural area of Huanghuaihai Plain. Guizhou Agric. Sci. 2012, 3, 104–109. 40. Siqing, C.; , L.; Dafang, Z.; Xiangming, X. Characterization of land cover types in Xilin River Basin using multi-temporal Landsat images. J. Geogr. Sci. 2003, 13, 131–138. [CrossRef] 41. Kontgis, C.; Schneider, A.; Ozdogan, M. Mapping rice paddy extent and intensification in the Vietnamese Mekong River Delta with dense time stacks of Landsat data. Remote Sens. Environ. 2015, 169, 255–269. [CrossRef] 42. Bureau, Beijing Statistical. Beijing Statistical Yearbook; Beijing Statistical Press: Beijing, China, 2012. 43. Bureau, Hebei Province Statistical. Hebei Rual Statistical Yearbook; Beijing Statistical Press: Beijing, China, 2012. 44. Bureau, Henan Province Statistical. Henan Statistical Yearbook; Beijing Statistical Press: Beijing, China, 2012. 45. Bureau, Shandong Province Statistics. Shandong Statistical Yearbook; Beijing Statistical Press: Beijing, China, 2012. 46. Bureau, Tianjin Statistical. Tianjin Statistical Yearbook; Beijing Statistical Press: Beijing, China, 2012. 47. Bureau, Beijing Statistical. Beijing Statistical Yearbook; Beijing Statistical Press: Beijing, China, 2013. 48. Bureau, Hebei Province Statistical. Hebei Rual Statistical Yearbook; Beijing Statistical Press: Beijing, China, 2013. 49. Bureau, Henan Province Statistical. Henan Statistical Yearbook; Beijing Statistical Press: Beijing, China, 2013. 50. Bureau, Shandong Province Statistics. Shandong Statistical Yearbook; Beijing Statistical Press: Beijing, China, 2013. 51. Bureau, Tianjin Statistical. Tianjin Statistical Yearbook; Beijing Statistical Press: Beijing, China, 2013. 52. Pan, Z.K.; Huang, J.F.; Zhou, Q.B.; Wang, L.M.; Cheng, Y.X.; Zhang, H.K.; Blackburn, G.A.; Yan, J.; Liu, J.H. Mapping crop phenology using NDVI time-series derived from HJ-1 A/B data. Int. J. Appl. Earth Obs. Geoinf. 2015, 34, 188–197. [CrossRef] 53. Duan, S.W.; He, H.S.; Spetich, M. Effects of Growing-Season Drought on Phenology and Productivity in the West Region of Central Hardwood Forests, USA. Forests 2018, 9, 377. [CrossRef] 54. Bian, J.H.; Li, A.N.; Song, M.Q.; Ma, L.Q.; Jiang, J.G. Reconstruction of NDVI time-series datasets of MODIS based on Savitzky-Golay filter. J. Remote Sens. 2010, 14, 725–741. 55. Savitzky, A.; Golay, M.J.E. Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Anal. Chem. 1964, 36, 1627–1639. [CrossRef] 56. Yang, J.Y.; Mei, X.R.; Qin, L.; Yan, C.R.; He, W.Q.; Liu, E.K.; Liu, S. Variations of winter wheat growth stages under climate changes in northern China. Chin. J. Plant Ecol. 2011, 35, 623–631. [CrossRef] 57. Khikmah, L.; Wijayanto, H.; Syafitri, U. Modeling Governance KB with CATPCA to Overcome Multicollinearity in the Logistic Regression. J. Phys. Conf. Ser. 2017, 824, 012027. [CrossRef] 58. Lee, S.H.-H. Analysis of the multicollinearity of regression equations of shear wave velocities. Soils Found. 1992, 32, 205–214. [CrossRef] 59. Zhao, D.; Dong, C.; Guo, H.; Tian, W. Kinematic Calibration Based on the Multicollinearity Diagnosis of a 6-DOF Polishing Hybrid Robot Using a Laser Tracker. Math. Probl. Eng. 2018, 2018, 5602397. [CrossRef] 60. Evans, J.P.; Geerken, R. Classifying rangeland vegetation type and coverage using a Fourier component based similarity measure. Remote Sens. Environ. 2006, 105, 1–8. [CrossRef] 61. Guo, Y.S.; Liu, Q.S.; Liu, G.H.; Huang, C. Extraction of Main Crops in Yellow River Delta Based on MODIS NDVI Time Series. J. Nat. Resour. 2017, 32, 1808–1818. [CrossRef] 62. Guan, X.D.; Huang, C.; Liu, G.H.; Xu, Z.R.; Liu, Q.S. Extraction of Paddy Rice Area Using a DTW Distance Based Similarity Measure. Resour. Sci. 2014, 36, 267–272. 63. Li, H.Z.; Yang, C.; Liu, E.H.; Yin, H. P-tile Combined with Histogram-based FCM for Pavement Image Partitioning. Comput. Era 2010, 8, 32–34. 64. Zheng, J.Y.; Yi, Y.H.; Li, B.Y. A New Scheme for Climate Regionalization in China. Acta Geogr. Sin. 2010, 65, 3–12. [CrossRef] 65. Zheng, J.Y.; Bian, J.J.; Ge, Q.S.; Hao, Z.X.; Yin, Y.H.; Liao, Y.M. The climate regionalization in China for 1981–2010. Chin. Sci. Bull. 2013, 58, 3088–3099. 66. Zhao, J.; Yang, X. Average amount and stability of available agro-climate resources in the main maize cropping regions in China during 1981–2010. J. Meteorol. Res. 2018, 32, 146–156. [CrossRef] 67. Shi, S.; Han, Y.; Yu, W.; Cao, Y.; Cai, W.; Yang, P.; Wu, W.; Yu, Q. Spatio-temporal differences and factors influencing intensive cropland use in the Huang-Huai-Hai Plain. J. Geogr. Sci. 2018, 28, 1626–1640. [CrossRef] Appl. Sci. 2020, 10, 2667 21 of 21

68. Wang, H.Y.; Pan, X.P.; Luo, J.M.; Luo, Z.P.; Chang, C.P.; Shen, Y.J. Using remote sensing to analyze spatiotemporal variations in crop planting in the North China Plain. Chin. J. Eco-Agric. 2015, 23, 1199–1209. 69. Liu, J.; Huang, Y. Dynamic Monitoring of Summer Maize Planting Information for Spatial and Temporal Variations in Huanghuaihai Plain During 2000–2010. Spectrosc. Spectr. Anal. 2012, 32, 2534–2539.

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).