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Article Quantification of Changes in Rice Production for 2003–2019 with MODIS LAI Data in Pursat Province, Cambodia

Yu Iwahashi 1 , Rongling Ye 1, Satoru Kobayashi 2, Kenjiro Yagura 3, Sanara Hor 4 , Kim Soben 5 and Koki Homma 1,*

1 Graduate School of Agricultural Science, Tohoku University, Sendai 9808572, Japan; [email protected] (Y.I.); [email protected] (R.Y.) 2 Center for Southeast Asian Studies, Kyoto University, Kyoto 6068501, Japan; [email protected] 3 Faculty of Economics, Hannan University, Matsubara 5808502, Japan; [email protected] 4 Faculty of Land Management and Land Administration, Royal University of Agriculture, Phnom Penh 12401, Cambodia; [email protected] 5 Center for Agricultural and Environmental Studies, Royal University of Agriculture, Phnom Penh 12401, Cambodia; [email protected] * Correspondence: [email protected]; Tel./Fax: +81-22-757-4083/4085

Abstract: Rice is not merely a staple food but an important source of income in Cambodia. Rapid socioeconomic development in the country affects farmers’ management practices, and rice production has increased almost three-fold over two decades. However, detailed information about the recent changes in rice production is quite limited and mainly obtained from interviews and statistical data. Here, we analyzed MODIS LAI data (MCD152H) from 2003 to 2019 to quantify rice production changes in Pursat Province, one of the great rice-producing areas in Cambodia. Although the LAI Citation: Iwahashi, Y.; Ye, R.; showed large variations, the data clearly indicate that a major shift occurred in approximately 2010 af- Kobayashi, S.; Yagura, K.; Hor, S.; ter applying smoothing methods (i.e., hierarchical clustering and the moving average). This finding Soben, K.; Homma, K. Quantification is consistent with the results of the interviews with the farmers, which indicate that earlier-maturing of Changes in Rice Production for cultivars had been adopted. Geographical variations in the LAI pattern were illustrated at points 2003–2019 with MODIS LAI Data in analyzed along a transverse line from the mountainside to the lakeside. Furthermore, areas of dry Pursat Province, Cambodia. Remote season cropping were detected by the difference in monthly averaged MODIS LAI data between Sens. 2021, 13, 1971. https:// January and April, which was defined as the dry season rice index (DSRI) in this study. Consequently, doi.org/10.3390/rs13101971 three different types of dry season cropping areas were recognized by nonhierarchical clustering of the annual LAI transition. One of the cropping types involved an irrigation-water-receiving area Academic Editors: Nicolas Greggio and Ephrem Habyarimana supported by canal construction. The analysis of the peak LAI in the wet and dry seasons suggested that the increase in rice production was different among cropping types and that the stagnation Received: 13 April 2021 of the improvements and the limitation of water resources are anticipated. This study provides Accepted: 15 May 2021 valuable information about differences and changes in rice cropping to construct sustainable and Published: 18 May 2021 further-improved rice production strategies.

Publisher’s Note: MDPI stays neutral Keywords: Cambodia; dry season cropping; leaf area index; MODIS; remote sensing; rice with regard to jurisdictional claims in published maps and institutional affil- iations. 1. Introduction Rice is the principal crop in Cambodia, where it occupies 75 percent of the cultivated land. It is also one of the important industries in Cambodia, since more than 20 percent Copyright: © 2021 by the authors. of working-age people are involved in rice production, processing, and marketing [1]. Licensee MDPI, Basel, Switzerland. After achieving self-sufficiency in 1995, the total production of rice in the country has This article is an open access article increased three-fold [2], and rice has recently become a promising commodity for export [3]. distributed under the terms and Promoting rice production is of significant importance to economic development and conditions of the Creative Commons poverty reduction in Cambodia [4]. The increase in rice production was achieved by the Attribution (CC BY) license (https:// expansion of the cultivation area and improved yield [4]. However, limited quantitative creativecommons.org/licenses/by/ information on factors driving the increase in production makes future prospects uncertain. 4.0/).

Remote Sens. 2021, 13, 1971. https://doi.org/10.3390/rs13101971 https://www.mdpi.com/journal/remotesensing Remote Sens. 2021, 13, 1971 2 of 17

Pursat Province, located in western Cambodia, is a typical rice-producing area, re- flecting the current situation of the whole country over a shorter time scale [5]. It was reported that the increase in rice production in Pursat Province occurred as a result of drastic changes in cultivation management, such as expanding the use of chemical fertilizers and introducing modern varieties. Yagura et al. (2020) also pointed out that one of the key factors in these drastic changes was irrigation infrastructure [5]. The Cambodian government has been promoting the construction and revitalization of its irrigation infrastructure [6,7]. Several reports have indicated that the construction of irrigation channels increases rice productivity in the wet season by providing supplemental irrigation and reducing flood risks. Additionally, the availability of irrigation water expanded the rice production area in the dry season [8]. However, these reports were either case studies based mostly on intensive and extensive interviews with farmers or statistical data. Temporal and spatial analyses of the impacts of irrigation availability on rice production have rarely been conducted. Satellite-based remote sensing has been widely used as an effective tool to evaluate regional crop growth. For example, the moderate resolution imaging spectroradiometer (MODIS) provides leaf area index (LAI) products as a growth indicator at constant (4- or 8-day) intervals with a spatial resolution of 500 m. Additionally, vegetation indices such as the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) can be obtained from MODIS data. Several studies have investigated crop yield modeling in combination with the MODIS LAI product and other vegetation indices. Son et al. (2013) demonstrated regional yield prediction in the Vietnamese rice cropping area with the MODIS LAI product (MOD15A2) and the EVI calculated by MODIS spectral data (MOD09A1) [9]. Planting, heading, and harvesting dates were reasonably estimated by the EVI calculated from MODIS spectral data (MOD13Q1 and MYD13Q1) in Cambodia [10]. Changes in crop production can also be analyzed for a two-decade period because the MODIS product has been collected since 2000. The EVI and NDVI from MODIS data were integrated with a rice phenology model for a sixteen-year period to reveal the effects of weather variation in a study by Wang et al. (2020) [11]. Song et al. (2020) used the MODIS LAI product from 2003 to 2018 to monitor spatiotemporal changes in the winter wheat phenology in China in response to climate warming [12]. However, the spatial resolution of these products (500 m) is often considered too large for field-scale analysis of rice production in Asia, and the unexpected fluctuation in the LAI caused mostly by cloud contamination also restricts its inclusion in analysis. Therefore, some noise-filtering or smoothing procedures to exclude unacceptable noise are necessary. To overcome the limitations associated with these fluctuations and the spatial resolution of these data, in this study, cluster analysis and averaging were applied to time series data from the MODIS LAI product in Pursat Province. Based on the analysis, changes in rice production over 17 years (2003–2019) were evaluated. In addition, interviews with local farmers reinforced our understanding of the background of rice production changes. Special attention was paid to areas where dry season rice was cultivated because irrigation had an important role in it. The prospects and potential for enhancing rice production in the region are discussed.

2. Materials and Methods 2.1. Study Area and Cropping Season Pursat Province is in the western part of Cambodia between Tonle Sap Lake and the northern end of the Cardamom Mountains. Rivers and lakes in this region are often ﬂooded in the wet season, resulting in the inundation of the surrounding area. The lowland area of Pursat Province is a part of the Tonle Sap Plain. The western side of the province shares a boundary with Thailand. Pursat is characterized by a tropical monsoon climate. The dry season (DS) starts in November and ends in April, with the driest month being February. The wet season (WS) starts in May and ends in October, with bimodal rainfall peaks in June and Septem- Remote Sens. 2021, 13, 1971 3 of 18

starts in May and ends in October, with bimodal rainfall peaks in June and September/Oc- Remote Sens. 2021, 13, 1971 tober [8]. Rice is the dominant crop grown on lowland plains, where the majority3 of 17 of WS rice is sown or transplanted from May to June and harvested from October to January, depending on the maturity trait of the cultivar and water availability. Cultivation types and ber/Octoberseasons are [8 described]. Rice is the in dominant Figure crop1. Growth grown on duration lowland plains,is determined where the by majority the sensitivity of of cultivarsWS rice is to sown daylength. or transplanted According from Mayto Ouk to June et al. and (2009) harvested [13], fromearly October cultivars to January, with low sen- sitivitydepending to daylength on the maturity grow for trait less of the than cultivar 120 anddays, water and availability. those with Cultivation greater sensitivity types to and seasons are described in Figure1. Growth duration is determined by the sensitivity daylengthof cultivars mature to daylength. earlier than According the middle to Ouk of et al.October. (2009) [Intermediate13], early cultivars cultivars with low with rela- tivelysensitivity low sensitivity to daylength grow grow for for 120 less to than 150 120days, days, and and those those with with greater sensitivity sensitivity to mature fromdaylength mid-October mature earlierto mid-November. than the middle ofLate October. cultivars Intermediate with relatively cultivars with low relatively sensitivity to daylengthlow sensitivity grow for grow more for 120than to 150150 days,days, and and those those with with greater greater sensitivity sensitivity mature mature from after the mid-Octobermiddle of November. to mid-November. With Late irrigation cultivars wate with relativelyr, second low (DS) sensitivity rice cultivations to daylength can be grow for more than 150 days, and those with greater sensitivity mature after the middle started from November to December, after the WS rice’s cultivation is finished. The DS of November. With irrigation water, second (DS) rice cultivations can be started from rice Novemberis harvested to December, from January after the to WSFebruary. rice’s cultivation With enough is ﬁnished. water The in DS March rice is or harvested April, farmers can fromestablish January the to first February. crop from With enoughone to waterthree in months March orearlier April, and farmers can can therefore establish plant the a second ﬁrstcrop crop (Figure from one1). to three months earlier and can therefore plant a second crop (Figure1).

Dry season (DS) Wet season (WS)

Cultivation type Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sep. Oct. Nov. Dec. Late Intermediate Direct seeding Early + DS Early WS + DS (Middle)

Figure 1.Figure Major 1. Majorcultivation cultivation types types and and durations durations in PursatPursat Province. Province. Modified Modified from Kamoshitafrom Kamoshita (2009) [14 (2009)] (pp. 107–120). [14] (pp. The 107–120). The greengreen and and yellow yellow boxes boxes indicate indicate transplanting transplanting and and harvesting harvesti times,ng times, respectively. respectively. The green The shaded green box shaded indicates box the indicates time the time of seedof seed broadcasting. broadcasting. 2.2. Interview with Farmers on Cultivation Management 2.2. InterviewInterviews with withFarmers rice-growing on Cultivation farmers Management were conducted at one of the centers of the rice- producingInterviews areas with in Pursatrice-growing Province infarmers August were 2014. Full-timeconducted farmers at one who of agreed the centers to the of the rice-producingsurvey were selectedareas in as Pursat interviewees. Province The in location August and 2014. cultivation Full-time management farmers approach who agreed to were confirmed by interviews with 13 farmers of 13 fields in the Trapeang chorng (TC) and the survey were selected as interviewees. The location and cultivation management ap- Khnar Totueng (KT) communes in Bakan district. Additional interviews were conducted proachwith were 4 farmers confirmed for each by of 4 interviews fields in the Svaywith Doun13 farmers Kaev (SDK), of 13 Boeng fields Khnar in the (BK), Trapeang Ta Lou chorng (TC)(TL), and and Khnar Rumlech Totueng (Ru) communes (KT) communes in Bakan district in Bakan in August district. 2015. Additional Changes in cultivationinterviews were conductedmanagement with and4 farmers the reasons for foreach those of 4 changes fields werein the identified Svay Doun in the Kaev interviews (SDK), (Figure Boeng2, Khnar (BK),Table Ta 1Lou). The (TL), results and were Rumlech used to (Ru) understand communes the LAI in changes Bakan atdistrict each location in August because 2015. the Changes in cultivationfarmers’ field management did not exactly and match the thereasons analyzed for pixel.those changes were identified in the inter- views2.3. (Figure LAI Data 2, Analysis Table 1). The results were used to understand the LAI changes at each location2.3.1. because LAI Data the farmers’ field did not exactly match the analyzed pixel. MODIS data (MCD15A2H version 6), which have been provided since July 2002, for the seventeen-year period 2003–2019 were downloaded from the website of the Earth Observing System Data and Information System (EOSDIS) (https://search.earthdata.nasa. gov/, accessed on 6 November 2018 for 2003–2017, 24 June 2019 for 2018 and 18 August 2020 for 2019). MCD15A2H contains a combined product, including the fraction of photosynthetically active radiation (FPAR) and leaf area index (LAI), of an 8-day composite dataset with a 500-m pixel size. The MODIS LAI/FPAR algorithm uses spectral information

Remote Sens. 2021, 13, 1971 4 of 17

from the MODIS red (648 nm) and near-infrared (NIR, 858 nm) surface reﬂectance and the secondary algorithm uses empirical relationships between the NDVI and canopy LAI and FPAR. LAI data were used for the analysis.

Table 1. Basic information from the interviewed farmers. For the data on the four locations in 2015, summaries of four neighboring farmers’ interviews are provided, and were recorded near their paddy ﬁelds. All communes in this table correspond to the locations of interviewed farmers’ houses. Abbreviations used in this table are as follows: CEM, crop establishment method; TP, transplanting; BC, broadcasting.

Year Commune ID Cultivars Seeding Time CEM GPS Somaly, +other 12.5811, 2014 Trapeang chorng TC1 May TP cultivar(s) 103.7622 12.5814, Trapeang chorng TC2 Somaly Mid–May Both 103.7671 12.5812, Trapeang chorng TC3 Somaly Early May Both 103.7683 12.5973, Trapeang chorng TC4 Somaly, Riang Chey Unknown Both 103.8049 12.5975, Trapeang chorng TC5 Somaly, Phka Rumduol Mid–May BC 103.8046 12.5961, Trapeang chorng TC6 Somaly Late June TP 103.8034 12.5959, Trapeang chorng TC7 Somaly Late June Both 103.8039 Somaly, Phka Rumduol, 12.6066, Trapeang chorng TC8 Early June Both Riang Chey 103.7885 Somaly, Phka Rumduol, 12.6040, Trapeang chorng TC9 Early May BC Riang Chey 103.7869 12.5915, Khnar Totueng KT1 Somaly Early May BC 103.7284 12.5966, Khnar Totueng KT2 Somaly, Sen Kra Ob Late April BC 103.7317 12.5998, Khnar Totueng KT3 Somaly Early May BC 103.7349 12.6004, Khnar Totueng KT4 Somaly Early May BC 103.7309 Sen Pidao, IRs, April – Early June/Late 12.7125, 2015 Svay Doun Kaev SDK BC +2 cultivars Sep. – Early Oct. 103.6364 Somaly, Riang Chey, 12.5416, Boeung Khnar BK Mid-June – July BC +2 cultivars 103.6736 Somaly, Riang Chey, 12.4929, Ta Lou TL No information BC +3 cultivars 103.6459 12.5969, Rumlech Ru Somaly, IRs, +1 cultivar Early April – Early Aug. BC 103.6910

2.3.2. Point-Level Analysis The LAI time series data were extracted for each single pixel corresponding to the 17 locations of the interviewed farmers with the Global Positioning System (GPS) coor- dinates. Each dataset had 17-year data points with a time series of 46 LAI values (8-day intervals) per year. Since three pairs of locations, TC2 and TC3, TC4 and TC5, and TC6 and TC7, are close to each other and included in one pixel respectively, a total of 14 datasets were obtained. Nine points were additionally selected on a transverse line from the mountainside to Tonle Sap Lake to clarify spatial variations in LAI (Figure2). The time series LAI data were extracted for each single pixel of 9 points. Hierarchical clustering was performed for each point of the interviewed farmers’ ﬁelds and the transverse line to evaluate temporal changes in LAI patterns during the period from 2003 to 2019. A clustering algorithm was applied to calculate the Euclidean distance between each yearly dataset of 46 data sequences (8-day intervals), and a dendrogram was Remote Sens. 2021, 13, 1971 5 of 17

created based on the Ward method. Two or three clusters were deﬁned when the distance between the clusters was greater than the smallest distance between the yearly data on the Remote Sens. 2021, 13, 1971 bottom of the dendrogram. The annual transition of LAI was calculated by averaging4 of the 18

Remote Sens. 2021, 13, 1971 data of the same date in each cluster and is represented by a moving average of ﬁve4 dataof 18

points (40 days).

Figure 2. Interviewed farmers’ locations (blue circles) and additional analyzed points (green squares). The dotted rectangle Figure 2. Interviewed farmers’ locations (blue circles) and additional analyzed points (green squares). The dotted rectangle in the right ﬁgure is the area analyzed for DS rice cropping. Some of the adjacent locations in the left ﬁgure (indicated with *) Figurein the right 2. Interviewed figure is the farmers’ area analyzed locations fo (bluer DS ricecircles) cropping. and addition Some ofal theanalyzed adjacent points locations (green in squares). the left figure The dotted (indicated rectangle with inwere*) thewere includedright included figure in theinis the same samearea set analyzed set of pixelsof pixels fo fromr DSfrom therice the MODIS cropping. MODIS LAI LAISome data data of (see the (see Figure adjacent Figure3). Elevation 3).locations Elevation datain the data were left were downloadedfigure downloaded (indicated from witfrom theh *)websitethe were websiteincluded of the Consultative in ofthe samethe Group set ofConsultative pixels on International from theGroup MODIS Agricultural LAIon data ResearchIn (seeternational Figure (CGIAR) 3). ElevationAgricultural (https://srtm.csi.cgiar.org/srtmdata/ data wereResearch downloaded (CGIAR) from, theaccessed(https://srtm.csi.cgiar.o website on 22 Februaryof rg/srtmdata/,the 2021). Consultative accessed onGroup 22 February on 2021).International Agricultural Research (CGIAR) (https://srtm.csi.cgiar.org/srtmdata/, accessed on 22 February 2021).

TC2 TC2 TC5 TC3 TC5 TC3 TC6 TC4 TC6 TC4 TC7 TC7

(a) (b) (a) (b) Figure 3.3. GoogleGoogle Earth images taken on 88 FebruaryFebruary 2020.2020. The whitewhite rectanglesrectangles show the pixelspixels ofof MODISMODIS LAILAI data.data. The Figuregreen pins pins3. Google indicateindicate Earth ( a(a)) TC2 TC2images ( left(left )taken and) and TC3on TC3 8 ( rightFebruary (right) and) and 2020. (b )(b TC4) TheTC4 (right whit (right)e and rectangles) and TC5 TC5 (left show(left) in) the inthe the right pixels right rectangle of rectangle MODIS and LAIand TC6 data.TC6 (left )( leftThe and) greenand TC7 pins (right indicate) in the (a) leftTC2 rectangle. (left) and TC3 (right) and (b) TC4 (right) and TC5 (left) in the right rectangle and TC6 (left) TC7 (right) in the left rectangle. and TC7 (right) in the left rectangle.

Remote Sens. 2021, 13, 1971 6 of 17

2.3.3. Analysis of Dry Season Rice In addition to point-level analysis, the rectangular area shown in Figure2 was analyzed to reveal the spread of the dry season rice’s cultivation. Bakan district was mainly covered in the area, and the surrounding districts were partially included. Two districts in Battambang Province near the boundary, the Moung Russei and Rukh Kiri districts, were also incorporated. The rectangle contained the Pursat River, Svay Doun Kaev River, Kambot River, Chambot River, Boeung Khnar canal, Damnak Ompil canal, Kbal Hong canal, and several streams and reservoirs. According to the results of the point-level analysis, LAI peaks occur mostly in January for the DS rice, and the lowest LAI is commonly observed in April. Therefore, DS cultivation could be estimated by the difference in the monthly average LAI between January and April, which was deﬁned as the dry season rice index (DSRI) in this study. The data corresponded to days of the year (DOYs) 1, 9, 17, and 25 and DOYs 89, 97, 105, and 113 for January and April, respectively. The DSRI for each pixel was calculated during 2003–2019. Since the number of pixels were huge, k-means clustering, a nonhierarchical method for classifying pixels based on the DSRI value of each year, was conducted for the area. The number of clusters and iterations was set to 5 and 100, respectively. The pixels belonging to the clusters that showed an increasing trend in the DSRI were selected as areas of dry season cultivation. For the selected pixels, 782 LAI values (46 data sequences per year from 2003 to 2019) were extracted and the pixels were further classiﬁed into 5 clusters by k-means clustering based on the 782 successive values. After averaging the LAI values of each cluster at each time point and calculating the moving averages of 5 consecutive values, clusters that showed LAI patterns of rice cultivation were selected. The peak values of both the dry and wet season of the selected cluster were extracted to indicate changes in the peak values. In addition, the annual LAI transitions of the selected clusters were analyzed by hierarchical clustering to identify changes from 2003 to 2019.

2.4. Software LAI data extraction from the MCD15A2H dataset and geographical presentation were performed using Quantum Geographic Information System (QGIS version 2.1.8) software. Hierarchical clustering and k-means clustering were performed using R (version 4.0.0, R development Core Team) and RStudio software (RStudio, PBC). The R functions and packages used in this study were the hclust and dist functions for hierarchical clustering and the k-means function for k-means clustering in the stats package.

3. Results 3.1. Changes in Rice Cultivation Management 3.1.1. Changes in the Fields of the Interviewed Farmers Most interviewed farmers grew Somaly, which is a fragrant landrace with high quality and value (Table1). It is a mixture of different but closely related cultivars [ 15]. Other common cultivars were Phka Rumduol, Sen Kra Ob, and Sen Pidao, which also produce fragrant rice [13,16]. Phka Rumduol and Riang Chey are photosensitive and have intermediate maturity traits, while Sen Kra Ob, Sen Pidao, and IR varieties are not photosensitive and mature in less than 120 days [13,15]. Another notable finding is that many of the farmers reduced the number of cultivars they grew, and Somaly accounted for a larger proportion as a result (Tables1 and2). The cultivar shift occurred in approximately 2010 (Table2). Since the non-photosensitive and early maturing cultivars can be grown in any season, the farmers were likely to grow them in the DS. Most farmers wanted to conduct double cropping, but its application was severely restricted by the availability of irrigation water. The farmers stated that the water source was insufficient in the DS even though their paddy fields accessed an irrigation channel. Traditionally, transplanting was performed, but broadcasting has increased recently due to labor shortages [5]. As indicated in this paper, many of the farmers adopted broadcasting to save time and labor (Table1). On the other hand, none of the farmers Remote Sens. 2021, 13, 1971 7 of 17

owned a harvester, and only one farmer had a tractor. The most common machinery among the farmers was a cultivator, which half of them purchased from 2005 to 2015 (Table2). Chemical fertilizers have recently become common, while other chemicals, such as pesticides, insecticides, and herbicides, have not yet been used by some farmers.

Table 2. Major changes in cultivation management and years for pairs of clusters. TC2 and TC3, TC4 and TC5, and TC6 and TC7 were in the same sets of pixels (indicated with *1–3). No information about machines was collected at KT1-4 (indicated with *4). TC8, TC9, and KT1 were not grouped into clusters (shown as “null”).

ID Major Changes in Cultivation Management (Year) Cluster 1 Cluster 2 TC1 Change cultivars (2012) 2003-10 2011-19 TC2 Change cultivars (2011) 2003-10 2011-19 TC3 Change cultivars (2008-09), Buy a cultivator (2011) TC4 Change cultivars (unknown), Buy a cultivator (2012) 2003–2009, 12, 13, 15, 16, 2010, 11, 14, 17, 18 TC5 Change cultivars (2015), Buy a cultivator (2011) 19 TC6 Buy a cultivator (2009) 2003-09, 12, 16 2010, 11, 13-15, 17-19 TC7 Change cultivars (2014) TC8 No changes obtained null null TC9 Buy a cultivator (2007) null null KT1 Change cultivars (2008) *4 null null KT2 Change cultivars (2008) *4 2003-10 2011-19 KT3 Change cultivars (2009) *4 2003-10 2011-19 KT4 Change cultivars (2009) *4 2003-10 2011-19 SDK Change cultivars (2010, 14, 15), Buy a cultivator (2005, 09, 15) 2003-08, 13 2009-12, 14-19 BK Change cultivars (2010), Buy a cultivator (2013) 2003-06, 08, 10 2007, 09, 11-19 TL Buy a cultivator (2010 or 12), Change cultivars (2015) 2003-12, 15 2013, 14, 16-19 Change cultivars (2005, 11, 15), Buy a cultivator and a tractor Ru 2003-12 2013-19 (2011, 15)

As shown in Figure4, the annual LAI transitions had some fluctuations in values due to effects of cloud, but they became smoothed by the moving average of 5 consecutive data values. Therefore, the moving average helped us to illustrate the annual LAI transitions clearly. The transitions in the fields of the interviewed farmers presented large variations among years, with one or two peaks per year (Figure5). Hierarchical clustering showed that 17 years were divided in approximately 2010, although some exceptional years were observed (Table2). At KT3, the transition of cluster 1 (before 2011) demonstrated a peak on DOY 297, while that of cluster 2 (after 2010) exhibited two peaks, on DOYs 1 and 257 (Figure6a). The change from cluster 1 to cluster 2 indicated that the farmers shifted to earlier-maturing cultivars. The larger LAI in cluster 2 may reflect the increase in rice productivity. In addition, another peak that overlapped the end of the year and the beginning of the next year appeared in cluster 2, which suggests DS rice cropping. A similar pattern was observed at KT2 and KT4. The transition at SDK had two peaks, DOY 5 (during the DS) and approximately DOY 197 (during the WS) in both clusters 1 and 2 (Figures5b and6b); the peak of cluster 2 in the DS reached approximately double that of the WS, although no significant change in the WS was observed. At TC8, TC9, and KT1, no clusters were obtained because the proportion of paddy fields was relatively low in the pixels (Figure7). Remote Sens. 2021, 13, 1971 8 of 18

Remote Sens. 2021, 13, 1971 8 of 18

DOYs 1 and 257 (Figure 6a). The change from cluster 1 to cluster 2 indicated that the farmers shifted to earlier-maturing cultivars. The larger LAI in cluster 2 may reflect the increase in riceDOYs productivity. 1 and 257 (Figure In addition, 6a). The another change frompeak cluster that overlapped 1 to cluster 2the indicated end of thatthe theyear farm- and the beginningers shifted of the to earlier-maturing next year appeared cultivars. in clus Theter larger 2, which LAI in suggests cluster 2 may DS ricereflect cropping. the increase A similar inpattern rice productivity. was observed In addition, at KT2 anotherand KT4. peak The that transition overlapped at theSDK end had of thetwo year peaks, and theDOY 5 beginning of the next year appeared in cluster 2, which suggests DS rice cropping. A sim- (during the DS) and approximately DOY 197 (during the WS) in both clusters 1 and 2 ilar pattern was observed at KT2 and KT4. The transition at SDK had two peaks, DOY 5 (Figures(during 5b theand DS) 6b); and the approximately peak of cluster DOY 2 in 197 the (during DS reached the WS) approximately in both clusters double 1 and that 2 of the WS,(Figures although 5b and no6b); significant the peak of change cluster 2in in the the WSDS reachedwas observed. approximately At TC8, double TC9, that and of KT1, Remote Sens. 2021, 13, 1971 no clustersthe WS, werealthough obtained no significant because change the proportion in the WS of was paddy observed. fields At was TC8, relatively TC9, and low KT1, in8 the of 17 pixelsno (Figureclusters were7). obtained because the proportion of paddy fields was relatively low in the pixels (Figure 7). 5 5 4 4

3 3 LAI

2 LAI 2

1 1

0 0 11 32 32 60 60 91 91 121 121 152 152 182 182 213 213 244 244 274 305 305 335 335 DOY DOY

FigureFigure 4. Annual4. Annual LAI LAI transitions transitions in 2019 at at KT3. KT3. The The dotted dotted line line shows shows raw rawdata dataand the and solid the line solid line Figureshows 4. Annual moving LAI averages transitions of 5 consecutive in 2019 at values. KT3. The dotted line shows raw data and the solid line showsshows moving moving averages averages of of 5 5consecutive consecutive values. values.

(a) (b)

RemoteFigure Sens. 2021 5. Annual, 13, 1971 LAI transitions for each year at two interviewed farmers’ locations: (a) KT3 and (b) SDK. The data shown9 of 18 Figure 5. Annual LAI transitions for each year at two interviewed farmers’ locations: (a) KT3 and (b) SDK. The data shown are the moving averages of(a )5 consecutive values. (b) are the moving averages of 5 consecutive values. Figure 5. Annual LAI transitions for each year at two interviewed farmers’ locations: (a) KT3 and (b) SDK. The data shown are the moving3 averages of 5 consecutive values. 2.5 cluster 1 2.5 cluster 2 2 2 1.5 1.5 LAI LAI 1 1

0.5 0.5

0 0 1 32 60 91 121 152 182 213 244 274 305 335 1 32 60 91 121 152 182 213 244 274 305 335 DOY DOY (a) (b)

FigureFigure 6. Annual 6. Annual transitions transitions of average of average LAI LAI for for each each cluster cluster at twoat two interviewed interviewed farmers farmers locations: locations: ((aa)) KT3KT3 and ( (b)) SDK. SDK. The The data shown are the moving averages of 5 consecutive values. data shown are the moving averages of 5 consecutive values.

TC8 KT1

TC9

(a) (b) Figure 7. Google Earth images taken on 8 February 2020. The white rectangles show pixels of MODIS LAI data. The green pins indicate (a) TC8 in the upper rectangle and TC9 in the lower rectangle and (b) KT1.

3.1.2. Changes at Points on the Transverse Line Geographic variations in the annual LAI transitions and their changes were observed on the transverse line. P8 and P9 (Figure S1), which had different patterns from other sites during most of the year, probably represented a non-crop vegetation type (Figure 8a). Additionally, P1 exhibited a different annual LAI transition, with quite high values in cluster 1 (2003–2006), suggesting that P1 was a forested area. However, the LAI of cluster 2 (2007–2019) declined drastically, indicating deforestation and a land use change in approximately 2007 (Figure 9a). From P2 to P7, the annual LAI transitions showed similar patterns, with one or two peaks in a year, as those shown in the fields of the interviewed farmers. Hierarchical clustering tended to classify consecutive years into the same clusters, except P6 (Figure 8b and Table 3).

Remote Sens. 2021, 13, 1971 9 of 18

3 2.5 cluster 1 2.5 cluster 2 2 2 1.5 1.5 LAI LAI 1 1

0.5 0.5

0 0 1 32 60 91 121 152 182 213 244 274 305 335 1 32 60 91 121 152 182 213 244 274 305 335 DOY DOY (a) (b) Remote Sens. 2021, 13, 1971 9 of 17 Figure 6. Annual transitions of average LAI for each cluster at two interviewed farmers locations: (a) KT3 and (b) SDK. The data shown are the moving averages of 5 consecutive values.

TC8 KT1

TC9

(a) (b)

FigureFigure 7. Google 7. Google Earth Earth images images taken taken on on 8 8 February February 2020. 2020. TheThe whitewhite rectangles show pixels pixels of of MODIS MODIS LAI LAI data. data. The The green green pins indicatepins indicate (a) TC8 in(a) the TC8 upper in the rectangle upper rectangle and TC9 and in the TC9 lower in the rectangle lower rectangle and (b) KT1. and (b) KT1.

3.1.2.3.1.2. Changes Changes at atPoints Points on on the the Transverse Transverse Line Line GeographicGeographic variations variations in inthe the annual annual LAI LAI transitions transitions and and their their changes changes were were observed observed onon the the transverse transverse line. line. P8 and P8 andP9 (Figure P9 (Figure S1), which S1), which had different had different patterns patterns from other from sites other duringsites duringmost of most the ofyear, the year,probably probably represented represented a non-crop a non-crop vegetation vegetation type type (Figure(Figure 8a).8a). Additionally,Additionally, P1 P1 exhibited exhibited a differentdifferent annual annual LAI LAI transition, transition, with with quite quite high high values values in cluster in cluster1 (2003–2006), 1 (2003–2006), suggesting suggesting that P1 that was P1 a was forested a forested area. However,area. However, the LAI the of LAI cluster of cluster 2 (2007– 2 (2007–2019)2019) declined declined drastically, drastically, indicating indicating deforestation deforestation and a land and use a land change use in change approximately in ap- proximately2007 (Figure 20079a). (Figure From P29a). to From P7, theP2 to annual P7, the LAI annual transitions LAI transitions showed similarshowed patterns,similar patterns,with one with or twoone peaksor two in peaks a year, in asa year, those as shown those shown in the ﬁelds in the of fields the interviewedof the interviewed farmers. Remote Sens. 2021, 13, 1971 10 of 18 farmers.Hierarchical Hierarchical clustering clustering tended tended to classify to cl consecutiveassify consecutive years into years the sameinto the clusters, same clus- except ters,P6 except (Figure P68b (Figure and Table 8b3 and). Table 3).

P9 P6

(a) (b)

FigureFigure 8. Google8. Google Earth Earth Images Images taken taken on 8 on February 8 February 2020. 2020. The whiteThe white rectangles rectangles show show pixels pixels of the of data. the Thedata. yellow The yellow pins indicate pins (a) P7, P8,indicate and P9 from(a) P7, the P8, lower and P9 left from to the the upper lower right left andto the (b upper) P6. right and (b) P6.

7 cluster 1 2.5 cluster 1 6 cluster 2 cluster 2 2 5

4 1.5 LAI LAI 3 1 2 0.5 1

0 0 1 32 60 91 121 152182 213 244274 305335 1 32 60 91 121 152182 213 244274 305335 DOY DOY

(a) (b)

3.5 cluster 1 cluster 1 1.5 cluster 2 cluster 2 3 cluster 1-1 cluster 3 cluster 1-2 2.5 1 2 LAI 1.5 LAI

1 0.5

0.5

0 0 1 32 60 91 121 152182 213 244 274 305335 1 32 60 91 121 152 182 213 244274 305335 DOY DOY

(c) (d) Figure 9. Annual transitions of average LAI for each cluster at four of the points on the transverse line: (a) P1, (b) P4, (c) P5, and (d) P7. Cluster 1 at P5 was divided into subclusters: 2003, 2004, 2005, 2006, 2008, and 2010 (cluster 1-1) and 2007, 2009, 2011, 2012, and 2013 (cluster 1-2). The data shown are the moving averages of 5 consecutive values.

Remote Sens. 2021, 13, 1971 10 of 18

P9 P6

(a) (b) RemoteFigure Sens. 2021 8. ,Google13, 1971 Earth Images taken on 8 February 2020. The white rectangles show pixels of the data. The yellow pins10 of 17 indicate (a) P7, P8, and P9 from the lower left to the upper right and (b) P6.

7 cluster 1 2.5 cluster 1 6 cluster 2 cluster 2 2 5

4 1.5 LAI LAI 3 1 2 0.5 1

0 0 1 32 60 91 121 152182 213 244274 305335 1 32 60 91 121 152182 213 244274 305335 DOY DOY

(a) (b)

3.5 cluster 1 cluster 1 1.5 cluster 2 cluster 2 3 cluster 1-1 cluster 3 cluster 1-2 2.5 1 2 LAI 1.5 LAI

1 0.5

0.5

0 0 1 32 60 91 121 152182 213 244 274 305335 1 32 60 91 121 152 182 213 244274 305335 DOY DOY

FigureFigure 9. 9.Annual Annual transitions transitions of of average average LAI LAI for for each each cluster cluster at at four four of theof the points points on theon the transverse transverse line: line: (a) P1,(a) (P1,b) P4,(b) (P4,c) P5, (c) andP5, (andd) P7. (d) Cluster P7. Cluster 1 at P51 at was P5 dividedwas divided into subclusters:into subclusters: 2003, 2003, 2004, 2004, 2005, 2005, 2006, 2006, 2008, and2008, 2010 and (cluster2010 (cluster 1-1) and 1-1) 2007, and 2009,2007, 2009, 2011, 2012, and 2013 (cluster 1-2). The data shown are the moving averages of 5 consecutive values. 2011, 2012, and 2013 (cluster 1-2). The data shown are the moving averages of 5 consecutive values.

Table 3. Clusters of 9 points on the transverse line. No effective clusters were obtained at P6, P8, and P9 (shown as “null”).

Cluster 1 Cluster 2 Cluster 3 or Other P1 2003-06 2007-19 — P2 2004-13, 17-19 2014-16 2003 P3 2003-12 2013-19 — P4 2003-06, 08, 10 2007, 09, 11-19 — P5 2003-13 2014-19 — P6 null null null P7 2003-09, 11, 12 2010, 13-17 2018-19 P8 null null null P9 null null null

Two clusters were obtained at P5 (Table3), and the differences in the annual LAI transition between them were similar to those found in the ﬁelds of interviewed farmers, such as TK3 (Figure9c). Another ﬁnding of note is that cluster 1 could be divided into two subclusters: 2003–2006, 2008, and 2010 (cluster 1-1) and 2007, 2009, and 2011–2013 (cluster 1-2). The change between cluster 1-1 and cluster 1-2 was characterized by the movement of the peak from DOY 297 to DOY 265. Since the timing of the peak of cluster 1–2 was similar to that of cluster 2, a change in cultivar may have occurred in approximately 2010, followed Remote Sens. 2021, 13, 1971 11 of 17

by an increase in the peak value after 2013 (Figure9c). These trends were also observed at P2, P3, and P4; however, no peak in the DS was recognized at these sites (Figure9b). A different annual LAI transition pattern was observed at P7 (Figure9d). The short period between the peaks on DOY 257 and DOY 329 in cluster 2 may indicate single cropping with a temporary decrease in LAI due to flooding rather than double cropping. The location of P7, which is flood prone, may support this assumption. Similar findings were observed for cluster 1, for which both peak values were low and the period between them was longer than that for cluster 2. The peaks on DOY 193 and DOY 233 in cluster 3 may also correspond to single cropping. The periods between the two peaks were 104, 72, and 40 days in cluster 1, 2, and 3, respectively. The decrease in this period indicates some improvement in the drainage system, which mitigated flood damage. The larger peak of LAI on DOY 345 in cluster 3 suggests that farmers started DS rice cropping in 2018.

3.2. Dry Season Rice Cropping 3.2.1. Detecting the Area of Dry Season Rice Cropping Changes in the DSRI were obviously different among clusters obtained in the k-means clustering (Figure 10). The geographical distribution of clusters is shown in Figure 11. Cluster 1 (C1) was mostly distributed around the Svay Don Kev River and Damnak Ompil irrigation canal and exhibited an apparent increase in the DSRI beginning in 2010. Cluster 2 (C2) was largely observed in the area surrounding C1 and showed a gradual increase in the DSRI; however, the increase in the DSRI in C2 was smaller and occurred later than in C1. Cluster 5 (C5) was predominant in the area nearest to the lake and showed a large Remote Sens. 2021, 13, 1971 fluctuation in the DSRI. Cluster 4 was found in the area close to C5, and the DSRI of 12 of 18 C4 was consistent with that of C5. The remaining area belonged to Cluster 3 (C3), with only a slight increase in the DSRI of the LAI beginning in approximately 2013. C1 and C2 showed constantly increasing tendencies. SDK, a field belonging to interviewed farmers, andwas KT4 included were in at C1, the while border P5, between which was C1 on and the C2. transverse P7, TC1, line, BK, belonged and Ru towere C2. at KT2, the boundary betweenKT3, and KT4C2 and were C3. at theThe border LAI pattern between of C1 the and rice C2. was P7, TC1,identified BK, and or Ru estimated were at the at these loca- tionsboundary as mentioned between C2 andabove. C3. The LAIareas pattern included of the ricein C1 was and identified C2 were or estimated provided at for further these locations as mentioned above. The areas included in C1 and C2 were provided for analysis. further analysis.

Figure 10. Seventeen-year transitions of the dry season rice index (DSRI). Figure 10. Seventeen-year transitions of the dry season rice index (DSRI).

Figure 11. Distribution of the DSRI clusters classified by the k-means method.

3.2.2. Types of and Changes in Rice Cropping The 1724 pixels included in C1 and C2 were further divided into five groups by k- means clustering based on 17 years of LAI data. The geographic distribution of the groups is shown in Figure 12. Group 1 (G1) was mainly distributed along a border between the Svay Doun Kaev commune in Bakan district, Pursat Province and Ruessei Krang in Moung Ruessei district, Battambang Province. In addition, the Ou Ta Paong commune was included in G1. Group 2 (G2) was the surrounding area of G1, and Group 3 (G3) was across the Rumlech, Khnar Totueng, and Trapeang chorng communes in Bakan district. G1 was located along the Svay Doun Kaev River and Svay Don Dev River. Regarding the location of the fields belonging to the interviewed farmers, SDK was in the area of G1, while Ru, BK, TC1, KT1, KT2, KT3, and KT4 were included in G3. Additionally, P5, located

Remote Sens. 2021, 13, 1971 12 of 18

and KT4 were at the border between C1 and C2. P7, TC1, BK, and Ru were at the boundary between C2 and C3. The LAI pattern of the rice was identified or estimated at these locations as mentioned above. The areas included in C1 and C2 were provided for further analysis.

Remote Sens. 2021, 13, 1971 12 of 17

Figure 10. Seventeen-year transitions of the dry season rice index (DSRI).

FigureFigure 11. 11. DistributionDistribution of of the the DSRI DSRI clusters clusters classified classiﬁed by by the the k-means k-means method. method.

3.2.2.3.2.2. Types Types of of and and Changes Changes in in Rice Rice Cropping Cropping TheThe 1724 1724 pixels pixels included included in in C1 C1 and and C2 C2 we werere further further divided divided into into five ﬁve groups groups by byk- meansk-means clustering clustering based based on 17 on years 17 years of LAI of data LAI. data.The geographic The geographic distribution distribution of the groups of the isgroups shown is in shown Figure in Figure12. Group 12. Group1 (G1) 1was (G1) mainly was mainly distributed distributed along along a border a border between between the Svaythe Svay Doun Doun Kaev Kaev commune commune in inBakan Bakan district district,, Pursat Pursat Province Province and and Ruessei Ruessei Krang Krang in in MoungMoung Ruessei Ruessei district, district, Battambang Battambang Province. Province. In In addition, addition, the the Ou Ou Ta Ta Paong commune waswas included included in in G1. G1. Group Group 2 2 (G2) (G2) was was the the su surroundingrrounding area area of of G1, G1, and and Group Group 3 3 (G3) (G3) was was acrossacross the the Rumlech, Rumlech, Khnar Khnar Totueng, Totueng, and and Trap Trapeangeang chorng chorng communes communes in in Bakan Bakan district. district. G1G1 was was located located along along the the Svay Svay Doun Doun Kaev Kaev Ri Riverver and and Svay Svay Don Don Dev Dev River. River. Regarding Regarding the the locationlocation of of the the fields ﬁelds belonging belonging to to the the inte interviewedrviewed farmers, farmers, SDK SDK was was in in the the area area of of G1, G1, whilewhile Ru, Ru, BK, BK, TC1, TC1, KT1, KT1, KT2, KT2, KT3, KT3, and and KT4 KT4 we werere included included in in G3. G3. Additionally, Additionally, P5, P5, located located on a transverse line, was encompassed by G3. Group 4 (G4) and Group 5 (G5) consisted of limited areas near the lake. The annual transition of the average LAI of G5 was mostly between 3 and 6, indicating that this group probably represented forest vegetation. In

addition, the area of G4 possibly consisted of forest or grassland because the LAI values of G4 were greater than 1 throughout the years (Figure not shown). Consequently, the values of G1, G2, and G3 were characterized as rice cultivation (Figure 13a–c). A significant decrease in the value of all groups except for G3 in late 2011 might have been caused by floods, as reported in Kamoshita and Ouk (2015) [17]. The annual LAI transition in G1 featured double peaks beginning in 2003, while that in G2 and G3 featured a single peak by 2011 (Figures 13 and 14). In other words, DS cropping has probably been conducted in the area of G1 since 2003 and might have started in G2 and G3 in approximately 2011. Since the area of G1 mainly surrounded the Svay Doun Kaev and Svay Doun Dev Rivers, double cropping was the predominant practice from an earlier year due to water accessibility. Water availability might be improved in G2 and G3 by constructing or repairing irrigation channels. Similar tendencies in G1 and G3 were observed in SDK and P5, respectively. Hierarchical analysis was conducted on the annual LAI transitions of G1, G2, and G3. The results reveal that the transitions changed from approximately 2011 or 2012 in these groups. In particular, the trends of the WS and DS rice were different among the three groups (Figure 14); the peak value of the WS rice did not differ significantly in G1 or G2, while an increasing tendency for DS rice was observed beginning in 2011 in G1, Remote Sens. 2021, 13, 1971 13 of 18

on a transverse line, was encompassed by G3. Group 4 (G4) and Group 5 (G5) consisted of limited areas near the lake. The annual transition of the average LAI of G5 was mostly Remote Sens. 2021, 13, 1971 13 of 17 Remote Sens. 2021,between 13, 1971 3 and 6, indicating that this group probably represented forest vegetation.13 In of 18ad- dition, the area of G4 possibly consisted of forest or grassland because the LAI values of G4 were greater than 1 throughout the years (Figure not shown). Consequently, the values of G1, G2, andpossiblyon G3a transverse were followed characterized line, by was G2 beginningencompassed as rice in 2015cultivation by G3. or later.Group (Figure On 4 (G4) the otherand 13a–c). Group hand, A the5 significant (G5) peak consisted LAI ofde- crease in the theofvalue limited WS riceof areas all gradually groups near the increased lake.except The fromfor annual G3 approximately tranin latesition 2011 of 2011the migh average in G3.t have ThatLAI of is,been G5 while was caused DS mostly rice by wasbetween predominant 3 and 6, indicating in G1 and that G2, this WS group rice remained probably prevalent represented in G3forest even vegetation. though DS In rice ad- floods, as reported in Kamoshita and Ouk (2015) [17]. wasdition, introduced. the area of G4 possibly consisted of forest or grassland because the LAI values of G4 were greater than 1 throughout the years (Figure not shown). Consequently, the values of G1, G2, and G3 were characterized as rice cultivation (Figure 13a–c). A significant decrease in the value of all groups except for G3 in late 2011 might have been caused by floods, as reported in Kamoshita and Ouk (2015) [17].

Figure 12. FigureDistribution 12. Distribution of theFigure groups 12. classiﬁedDistributionof the groups by k-meansof the cl assifiedgroups clustering cl assifiedby fork-means LAI by k-means data clusteri covering clustering 2003–2019 forng for LAI LAI anddata data consisting covering covering of 2003– 2003– 1724 pixels2019 (clusters and 1 consisting and 22019 in Figures and of consisting1724 10 and pixels 11 of). 1724 (clu pixelssters (clu1 andsters 2 1in and Figures 2 in Figures 10 and 10 and 11). 11).

(a) (b)

Figure 13. Cont.

(a) (b)

Remote Sens. 2021, 13, 1971 14 of 18

Remote Sens. 20212021,, 1313,, 19711971 14 of 1718

(c) Figure 13. Annual transitions of the average LAI for three classified groups: (a) G1, (b) G2, and (c) G3. The data shown are the moving averages of 5 consecutive values.

The annual LAI transition in G1 featured double peaks beginning in 2003, while that in G2 and G3 featured a single peak by 2011 (Figures 13 and 14). In other words, DS cropping has probably been conducted in the area of G1 since 2003 and might have started in G2 and G3 in approximately 2011. Since the area of G1 mainly surrounded the Svay Doun Kaev and Svay Doun Dev Rivers,(c) double cropping was the predominant practice from an earlier year due to water accessibility. Water availability might be improved in G2 and G3 Figure 13. Annual transitions of the average LAI for three classified groups: (a) G1, (b) G2, and (c) G3. The data shown are Figure 13. Annual transitionsby of constructing the average LAI or forrepairing three classiﬁed irrigation groups: channels. (a) G1, (Similarb) G2, and tendencies (c) G3. The in data G1 shownand G3 are were the moving averages of 5 consecutive values. the moving averages of 5 consecutiveobserved values.in SDK and P5, respectively. The annual LAI transition in G1 featured double peaks beginning in 2003, while that in G2 and G3 featured a single peak by 2011 (Figures 13 and 14). In other words, DS cropping has probably been conducted in the area of G1 since 2003 and might have started in G2 and G3 in approximately 2011. Since the area of G1 mainly surrounded the Svay Doun Kaev and Svay Doun Dev Rivers, double cropping was the predominant practice from an earlier year due to water accessibility. Water availability might be improved in G2 and G3 by constructing or repairing irrigation channels. Similar tendencies in G1 and G3 were observed in SDK and P5, respectively.

FigureFigure 14. 14. PeakPeak value value of the LAI in the wet season (WS)(WS) andand thethe drydry seasonseason (DS) (DS) for for Groups Groups 1, 1, 2, 2, and and3 (G1, 3 (G1, 2, and 2, and G3). G3). 4. Discussion Hierarchical analysis was conducted on the annual LAI transitions of G1, G2, and G3. The resultsThe MODIS reveal LAIthat datathe transitions had some changed fluctuations from in approximately values because 2011 of clouds; or 2012 however, in these groups.the fluctuations In particular, became the smoothed trends of bythe the WS moving and DS average rice were of fivedifferent consecutive among values.the three In addition, MODIS data are affected by the mixture of components, such as houses and roads, because of their resolution. For example, some LAI values obtained in this study were relatively low compared with those measured directly in the farmers’ fields [18]. FigureFurthermore, 14. Peak changesvalue of the in LAI in values the wet were season difficult (WS) and to quantifythe dry season in pixels, (DS) for which Groups had 1, low2, proportionsand 3 (G1, 2, and of paddy G3). fields. Thus, there are limitations associated with using MODIS data to analyze the crop growth in a field or at a specific time point. This study quantified long-termHierarchical regional analysis changes was in riceconducted production on the by annual clustering LAI andtransitions averaging of G1, the G2, LAI and values. G3. The results reveal that some the significant transitions patterns changed of from shifts approximately in the annual transition2011 or 2012 of LAI;in these for groups.example, In the particular, peak moved the tren fromds November of the WS toand September, DS rice were the peakdifferent value among increased the bythree at

Remote Sens. 2021, 13, 1971 15 of 17

most twice, and the cultivation of DS rice was implemented. These changes seemed to occur from the late 2000s to the early 2010s and can probably be attributed to the introduction of cultivars with traits related to earlier maturity and increasing the use of chemical fertilizers [19]. Additionally, the interview survey supported the results by confirming that changes in cultivars had been made in approximately 2010. These significant changes possibly reflect the socioeconomic development and transformation from subsistence to commercial agriculture that began in the late 2000s, which included access to new cultivars and agrochemicals and the use of irrigation water [5]. Some later years were grouped into the earlier cluster and some earlier years were grouped into the later cluster due to the yearly variation in planting time and rice growth, which might be affected by the timing of rain and the availability of water. In addition, each pixel might contain several paddy fields with temporal variations and different growth characteristics in rice cultivation. Geographic variations in rice production and their changes were observed on the transverse line. The results suggest that earlier-maturing cultivars have become common in the WS at P2, P3, P4, P5, and P6, while double cropping may have been implemented only at P5. Furthermore, the risk of flood damage in the WS was indicated at P7, and farmers might adopt countermeasures to flooding, such as growing earlier-maturing cultivars or cultivating DS rice. The area-level analysis revealed three types of double cropping areas. First, at G3, where irrigation channels were constructed or repaired, DS cropping began in approximately 2011. For example, the Japan International Cooperation Agency (JICA) implemented a project to construct irrigation channels from 2009 to 2014 in this area [20,21]. The results indicate that the production of DS rice increased slightly, while that of WS rice improved from 2011 to 2015. Second, in G1 (a riverside area), double cropping consisting of early WS rice and DS rice began earlier than 2003. The interview survey revealed that only the farmers in this area grew an early fragrant cultivar in the WS and sowed DS rice in September, earlier than in the other areas. Cropping seems to be an adaptation to floods in the WS and the utilization of river water in the DS. The production of DS rice improved in 2011, while that of WS rice was almost constant. These results also suggest that the improvement of WS rice production in the area is restricted by the flood risk. Finally, G2 was identified as a location where rice growth in both the WS and the DS was more unstable than in other areas due to floods or poor water availability. Farmers sometimes suffered from floods in the late WS (i.e., in September and October) and from water shortages in the DS. This study quantitatively reveals the improvement in rice production in the WS and DS. Although irrigation influenced this improvement, the effect was somewhat limited in the WS and, at times, only apparent in the DS. Furthermore, most interviewed farmers mentioned that there was insufficient irrigation water. Climate change may affect the amount of available water in both seasons by increasing intensive rain patterns and drought [22]. The Cambodian government promoted the development of irrigation canals [8], but the risk of competition for water will arise if water resources are not evaluated correctly. In addition, many farmers require a pump for irrigation, which leads to costs associated with owning or renting the pump and fuel [23]. This is likely another major constraint on rice production. Additionally, labor shortages have become more serious in recent years due to migration to urban areas, forcing farmers to adopt broadcasting methods rather than transplanting [16]. The yield of broadcasted rice tends to be lower than that of transplanted rice [18]. This kind of partially extensive cropping might be one of the factors limiting rice production due to inadequate cultivation management (e.g., herbicides and hand weeding). Another expectation is the stagnation of improvement. The results indicate that a major change occurred in approximately 2011; however, no obvious change has occurred since then. The peak LAI value also shows that the improvement became almost stagnant in approximately 2015. Thus, the development of crop and pest management strategies is recommended for the further improvement of rice production [24]. Adaptive techniques, such as water-saving irrigation, will also be required to enhance crop productivity and Remote Sens. 2021, 13, 1971 16 of 17

increase grain yield considering future extreme climate events and competition for water resources [25].

5. Conclusions In this study, LAI data from MODIS were used to analyze rice production changes from 2003 to 2019. The data had some fluctuations or outliers; however, clustering and averaging processes can be used as powerful tools to quantify crop growth changes at broader temporal and spatial scales. This method provides a novel utilization of the consecutive MODIS LAI data obtained every 8 days since 2003. This result implies that a group of small-sized paddy fields can be analyzed. This method will be helpful in quantifying crop growth changes in other countries and regions. The WS rice productivity improved due to changes in cultivars and fertilization, and double cropping was implemented in some areas. However, the stagnation of the improvement of rice production and the limitation of water resources are expected. Since detailed statistical data and field investigation data are quite limited in Cambodia, the analysis of MODIS LAI data is an effective method for classifying the area and characterizing the changes in and constraints on rice production. The information obtained by this analysis will contribute to developing strategies that meet the requirements for further improvement in each area.

Supplementary Materials: The following are available online at https://www.mdpi.com/article/10 .3390/rs13101971/s1, Figure S1, Annual LAI transitions for each year at P8 and P9. Author Contributions: Conceptualization, K.H.; Data curation, R.Y.; Formal analysis, Y.I.; Investiga- tion, S.K., K.Y., S.H. and K.S.; Supervision, K.H.; Validation, S.K. and K.Y.; Writing—original draft, Y.I.; Writing—review & editing, K.H. All authors have read and agreed to the published version of the manuscript. Funding: This work was partly supported by JSPS KAKENHI, Grant Numbers 15H05144, 19H00559 and 19H03069. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Publicly available datasets were analyzed in this study. This data can be found here: [https://search.earthdata.nasa.gov/, accessed on 12 April 2021]. The interviewed data is available on request from the corresponding author. Acknowledgments: We are grateful to the students of the Royal University of Agriculture for helping us interview the local farmers. Conﬂicts of Interest: The authors declare no conﬂict of interest.

Abbreviations The following abbreviations are used in this manuscript.

DS Dry season DSRI Dry season rice index EVI Enhanced Vegetation Index FPAR Fraction of photosynthetically active radiation GPS Global Positioning Systems LAI Leaf area index MODIS Moderate resolution imaging spectroradiometer NDVI Normalized Difference Vegetation Index WS Wet season Remote Sens. 2021, 13, 1971 17 of 17

References 1. Cambodia Rice Export Potential and Strategies, CAMBODIA AGRIBUSINESS SERIES-No. 4; International Finance Corporation (IFC): Phnom Penh, Cambodia, 2015. 2. Food and Agriculture Organization of the United Nations FAOSTAT. Available online: http://www.fao.org/faostat/en/#data/ QC (accessed on 29 September 2020). 3. Yu, B.; Diao, X. Cambodia’s Agricultural Strategy: Future Development Options for the Rice Sector; A Policy Discussion Paper Special Report 9; Cambodia Development Resource Institute (CDRI), Council for Agricultural and Rural Development (CARD), and International Food Policy Research Institute (IFPRI): Phnom Penh, Cambodia, 2011. 4. Ishikawa, K. The Marketing and Trading of Rice in Cambodia: The Rice Market System and Price Changes Under a Market- Oriented Economy. Forum Int. Dev. S 2010, 39, 99–120. 5. Yagura, K. Transformation of Agriculture and Its Mechanism in Pursat Province, Cambodia: Utilization of Untapped Resources and Funding from External Sources. Jpn. J. S. Asian Stud. 2021, 59. in press. 6. Sothy, E.; Sokcheng, S.; Chhum, C.; Pirom, K. Rice Policy Study: Implications of Rice Policy Changes in Vietnam for Cambodia’s Rice Policy and Rice Producers in South-Eastern Cambodia, CDRI Working Paper Series No. 113; Cambodia Development Resource Institute (CDRI): Phnom Penh, Cambodia, 2017. 7. Cambodia: Northwest Irrigation Sector Project, Completion Report; Asian Development Bank (ADB): Manila, Philippines, 2014. 8. FAO AQUASTAT Country Profile—Cambodia; Food and Agriculture Organization of the United Nations (FAO): Rome, Italy, 2011. 9. Son, N.T.; Chen, C.F.; Chen, C.R.; Chang, L.Y.; Duc, H.N.; Nguyen, L.D. Prediction of rice crop yield using MODIS EVI-LAI data in the Mekong Delta, Vietnam. Int. J. Remote Sens. 2013, 34, 7275–7292. [CrossRef] 10. Tsujimoto, K.; Ohta, T.; Hirooka, Y.; Homma, K. Estimation of Planting date in paddy fields by time-series MODIS data for basin-scale rice production modeling. Paddy Water Environ. 2019, 17, 83–90. [CrossRef] 11. Wang, H.; Ghosh, A.; Linquist, B.A.; Hijmans, R.J. Satellite-Based Observations Reveal Effects of Weather Variation on Rice Phenology. Remote Sens. 2020, 12, 1522. [CrossRef] 12. Song, Y.; Wang, J.; Yu, Q.; Huang, J. Using MODIS LAI Data to Monitor Spatio-Temporal Changes of Winter Wheat Phenology in Response to Climate Warming. Remote Sens. 2020, 12, 786. [CrossRef] 13. Nadar, W.; Ouk, M.; Elsner, J.; Brendel, T.; Schubbert, R. The DNA fingerprint in food forensics part II: The Jasmine rice case. Agr. Food Ind. Technol. 2020, 31, 4–8. 14. Kamoshita, A. “Nettai-ajia no inasaku” [Rice production in tropical asia]. In “Saishin Nogyo Gijutsu, Sakumotsu” [The Latest Agricultural Techniques]; Rural Culture Association, Ed.; Rural Culture Association: Tokyo, Japan, 2009; Volume 1, pp. 107–120. (In Japanese) 15. Ouk, M.; Sakhan, S.; Men, S. Rice breeding methods for Cambodia. Breed. Res. 2009, 11, 65–71. (In Japanese) 16. Kodo, T.; Homma, K.; Kobayashi, S.; Yagura, K.; Sanara, H.; Soben, K. Effects of irrigation facilities development on cultivation management and productivity of rice in Cambodia. Jpn. J. S. Asian Stud. 2021, 59. in press. 17. Kamoshita, A.; Ouk, M. Field level damage of deepwater rice by the 2011 Southeast Asian Flood in a flood plain of Tonle Sap Lake, Northwest Cambodia. Paddy Water Environ. 2015, 13, 455–463. [CrossRef] 18. Hirooka, Y.; Homma, K.; Kodo, T.; Shiraiwa, T.; Soben, K.; Chann, M.; Tsujimoto, K.; Tamagawa, K.; Koike, T. Evaluation of cultivation environment and management based on LAI measurement in farmers’ paddy fields in Pursat province, Cambodia. Field Crops Res. 2016, 199, 150–155. [CrossRef] 19. Johnston, R.; Try, T.; Silva, S. Agriculture Water Management Planning in Cambodia; Issue Brief #1; The International Water Management Institute (IWMI): Colombo, Sri Lanka, 2013. 20. Special Assistance for Project Formation for West Tonle Sap Irrigation and Drainage Rehabilitation and Improvement Project in the Kingdom of Cambodia; Final Report, Volume-I, Main Report; Japan International Cooperation Agency (JICA), Nippon Koei Co., Ltd.: Tokyo, Japan, 2009. 21. Project for Improvement of Agricultural River Basin Management and Development (TSC 3); Summary of Terminal Evaluation; Japan International Cooperative Agency (JICA): Tokyo, Japan, 2014. 22. Chhinh, N.; Millington, A. Drought Monitoring for Rice Production in Cambodia. Climate 2015, 3, 792–811. [CrossRef] 23. Sophea, K. The Rice Situation in Cambodia. Technical Assistance Consultant’s Report; Asian Development Bank (ADB): Manila, Philippines, 2012. 24. Castilla, N.P.; Stuart, A.M.; Makara, O.; Sathya, K.; Somany, S.; Kumar, V.; Hadi, B.A.R. Characterization of cropping practices, pest constraints, and yield variation in irrigated lowland rice of Cambodia. Crop Prot. 2020, 135, 10496. [CrossRef] 25. Yang, J.; Liu, K.; Wang, Z.; Du, Y.; Zhang, J. Water-Saving and High-Yielding Irrigation for Lowland Rice by Controlling Limiting Values of Soil Water Potential. J. Integr. Plant Biol. 2007, 49, 1445–1454. [CrossRef]