An accurate paddy rice map is crucial for ensuring food
security, particularly for Southeast and Northeast Asia. MODIS satellite
data are useful for mapping paddy rice at continental scales but have a
mixed-pixel problem caused by the coarse spatial resolution. To reduce the
mixed pixels, we designed a rule-based method for mapping paddy rice by
integrating time series Sentinel-1 and MODIS data. We demonstrated the
method by generating annual paddy rice maps for Southeast and Northeast Asia in 2017–2019 (NESEA-Rice10). We compared the resultant paddy rice maps with
available agricultural statistics at subnational levels and existing rice
maps for some countries. The results demonstrated that the linear
coefficient of determination (
Rice is one of the world's main food sources, accounting for approximately 12 % of the global cropland area (Zhang et al., 2018; Singha et al., 2019). Approximately 90 % of the world's rice is produced in Asian countries (Chen et al., 2012; Yeom et al., 2021). Rice provides food for over 50 %
of the world's population (Minasny et al., 2019). The consumption of rice
increases as the world's population increases. Additionally, approximately
Many methods for mapping rice have been developed based on different remote
sensing data, including (1) machine learning classifiers (e.g., random
forest and support vector machines), (2) phenology-based classifiers, (3) rule-based algorithms, and (4) the time series algorithm approach (Dong et al., 2016a, b; Bazzi et al., 2019; Dong and Xiao, 2016; Luo et al., 2020b, a; Nelson et al., 2014; Phung et al., 2020; Minasny et al., 2019; Shew and Ghosh, 2019; Xiao et al., 2006; Zhan et al., 2021). Satellite
image sources include MODIS, Landsat, Sentinel, RADARSAT, and PALSAR (Dong
and Xiao, 2016; Shao et al., 2001; Singha et al., 2019; Zhou et al., 2016).
Many studies have demonstrated that phenology-based classifiers using MODIS
data are useful for mapping paddy rice at continental scales (Dong et al., 2016b; Xiao et al., 2006; Zhang et al., 2020). The transplanting period of
rice is a distinct characteristic used for distinguishing rice from other
crops or land use types. For example, Xiao et al. (2006) mapped paddy rice
at continental scales (South Asia and Southeast Asia, SE Asia) using the
phenological characteristics in the period of flooding and transplanting.
Additionally, this method was successfully applied in other large regions
(Xin et al., 2020; Zhang et al., 2017, 2020). The International Rice
Research Institute (IRRI) extracted the distribution of paddy rice for Asia
(Nelson and Gumma, 2015). However, the paddy rice maps generated using MODIS
data contain a large number of mixed pixels caused by the coarse spatial
resolution (500 m) (Dong et al., 2015, 2016b; Shew and Ghosh, 2019),
particularly in hilly areas (Z. Liu et al., 2019). The mixed land cover types
within MODIS pixels can affect the accuracy of the rice map (Sun et al., 2009). Fine spatial resolution images, including Landsat TM/ETM
Optical remote sensing images and SAR data have complementary information (Park et al., 2018; Wang et al., 2015). The combination of optical and SAR images can provide opportunities for mapping paddy rice with a few mixed pixels and a high spatial resolution at continental scales. MODIS data have the advantage of high temporal resolution, which reduces cloud problems and provides valuable spectral information for identifying paddy rice. Sentinel-1 SAR data with a high spatial resolution (10 m) provide backscatter information for different land types. Therefore, the integration of MODIS and SAR images may solve the mixed-pixel issue to a great degree and enable the production of more reliable paddy rice maps than those based only on MODIS images (Dong and Xiao, 2016; Park et al., 2018; Torbick et al., 2010; Wang et al., 2015). We take advantage of both MODIS and SAR strengths to map paddy rice fields at a large scale.
Thus, we aim to improve the MODIS-based method for mapping paddy rice fields by integrating Sentinel-1 SAR data to reduce mixed-pixel effects. Then we use the method to generate paddy rice maps in 2017–2019 for SE Asia and Northeast Asia (NE Asia). The map products will be useful for scientific communities and stakeholders for many purposes.
The study areas were NE and SE Asia. NE Asia is composed of Northeast China (Liaoning, Jilin, and Heilongjiang province), the Democratic People's Republic of Korea, the Republic of Korea, and Japan (Dong et al., 2016b; Yeom et al., 2021). The main paddy-rice-producing regions in NE Asia are concentrated in the plain in Northeast China, the western plain of the Korean Peninsula, and the alluvial plains around the Japanese islands. In SE Asia, the countries where rice is planted intensively include Indonesia, Thailand, Vietnam, Myanmar, the Philippines, Malaysia, and Myanmar. SE Asia cultivates approximately 30 % of the world's rice (Bridhikitti and Overcamp, 2012; Huke and Huke, 1997). The dense planting areas of rice in SE Asia are located in valleys and deltas, such as the Red River delta in northern Vietnam and the Mekong River delta in southern Vietnam (Clauss et al., 2018a; Phung et al., 2020). The Mekong delta produces more than half the rice in Vietnam (Bouvet and Le Toan, 2011). The main rice cropping system in NE Asia is single rice (Dong et al., 2016b). By contrast, three rice cropping systems are dominant in SE Asia: single rice, double rice, and triple rice (Laborte et al., 2017). Because climate and crop calendars vary across SE and NE Asia, the study area was classified into eight refined agroecological zones based on temperature, seasonal precipitation, and farming practices from previous studies (Oliphant et al., 2019; Suepa et al., 2016). The zones were further subdivided into 41 regions for classification (Fig. 1).
Agroecological zones and 100 m radius sample blocks in SE and NE Asia.
We acquired the time series MOD09A1 images from the Google Earth Engine
(GEE) data catalog (
We generated digital elevation model (DEM) data from the Shuttle Radar
Topography Mission (SRTM) Version 4 (Reuter et al., 2007). The spatial
resolution of the DEM was 90 m
Detailed information about the data used in this study.
We extracted the forest land mask from the Global PALSAR Forest Map in 2017 (Table 1). The Global PALSAR Forest Map (25 m spatial resolution) was generated by the Japan Aerospace Exploration Agency (JAXA) (Shimada et al., 2014). Pixels with a forest area larger than 0.5 ha and forests covering over 10 % of the pixel area were defined as forest pixels (Shimada et al., 2014).
We extracted the distribution of wetland from the GlobeLand30 dataset in
2020. GlobeLand30 is available from the National Geomatics Center of China
(Table 1). This product at 30 m spatial resolution with high accuracy was
generated using Landsat, Chinese HJ-1, and GF-1 satellite images (
Finally, we resampled all the raster data to 10 m to match the spatial resolution of Sentinel-1.
We collected annual rice planting area census data at the subnational level (state, province, city, prefecture, or county) from the available statistical yearbooks of various countries. The agricultural statistics were provided by agricultural statistical offices. The areas in the statistics data were converted into hectares (ha). Detailed information about the collected agricultural statistics in this study is presented in Table 1.
We collected the existing publicly available rice maps from three sources: (1) the 500 m spatial resolution paddy rice map with high accuracy in Southern China in 2017 that was generated using the phenology- and pixel-based algorithm from MODIS data (Xin et al., 2020), (2) the High-Resolution Land Use and Land Cover (HRLULC) map for Vietnam in 2017 (Hashimoto et al., 2014) with 10 m spatial resolution generated using multiple remote sensing data sources, and (3) the 500 m resolution rice maps of Asia obtained from the IRRI (Nelson and Gumma, 2015), which were mainly derived from MODIS data. We compared these existing products with our paddy rice maps.
There are three growing stages for paddy rice: transplanting, growing, and
post-harvest periods (Singha et al., 2019). Flooding signals in the
transplanting period are unique characteristics that distinguish paddy rice
from other crops (Clauss et al., 2016; Dong et al., 2016b; Sun et al., 2009). The color combination of MODIS images (R/G/B
Temporal profile analysis of EVI, LSWI, VV, VH, and VH/VV ratio from
three typical paddy rice sites with different latitudes during 2017–2020:
The backscatter coefficients change as paddy rice grows and develops. Paddy rice fields appear as a black area in the VH image on the transplanting date (Fig. S2) because the water (flood) in the transplanting period decreases the VH backscatter coefficient values (Dineshkumar et al., 2019; Torbick et al., 2017). The VH and VV backscatter coefficients have a local minimum value during the transplanting period in all reference paddy rice fields (Fig. 2). After transplanting, the VH backscatter coefficients increase as the paddy rice grows and reaches a peak at the heading stage (Zhan et al., 2021; Zhang et al., 2018). The VH backscatter coefficients decrease after the rice harvest stage (Phung et al., 2020; Singha et al., 2019; Torbick et al., 2017). Additionally, paddy rice has consistent temporal behavior in the VH/VV ratio and VH. The profiles of the dynamic backscatter coefficients of some land cover types (e.g., water, urban, and forest) are different from those of paddy rice (Fig. S4). Therefore, color gradations and the time series of backscatter coefficients are useful for identifying paddy rice phenology information (Yulianto et al., 2019; Phung et al., 2020; Zhan et al., 2021).
Paddy rice in SE and NE Asia is cultivated using diverse cropping systems because of the climate and other natural conditions (Dong et al., 2016a; Laborte et al., 2017; Nelson et al., 2014; Shew and Ghosh, 2019). With reference to previous studies (Clauss et al., 2016; Gumma et al., 2014; C. Liu et al., 2020; Phan et al., 2019; Phung et al., 2020), we acquired information about the flooding signal period and length of the growing season for each subzone using sampling-based information. We selected sample blocks that were distributed over the different rice-growing zones across SE and NE Asia. Each block was a polygon with a radius of 100 m. We collected the sample blocks according to multiple rules (Clauss et al., 2016; Dong et al., 2016a; Fikriyah et al., 2019; Singha et al., 2019). First, the time series of the backscatter coefficients and vegetation index of the mean values from all pixels in each sample block were consistent with the phenological characteristics of paddy rice (Sect. 2.3.1 and 2.3.2). Second, the sample blocks were also digitized using Google Earth or Sentinel-1/2 images using visual interpretation referring to previous studies (Dong et al., 2016a; Zhang et al., 2015). Third, we also used existing rice maps and calendar information as complementary information (Laborte et al., 2017; Maclean et al., 2013). Note that not all Google Earth or Sentinel-2 images were available throughout SE and NE Asia. We collected a total of 438 sample blocks and 504 sample blocks using the above rules for SE and NE Asia, respectively (Fig. 1). These blocks covered most paddy rice fields in the study areas. We generated mean backscatter coefficients and vegetation index time series profiles for each block. Following this, we manually extracted the paddy rice growth and phenological parameters based on the backscatter time series characteristics. Finally, we obtained the phenological information for each subzone from the sample blocks (Gumma et al., 2014; C. Liu et al., 2020). Although there may be some limitations in extracting phenological parameters for zones using random samples, it may be one of the most effective approaches currently available (Clauss et al., 2016; Gumma et al., 2014; Han et al., 2021c; Li et al., 2020; Phan et al., 2019; Phung et al., 2020).
Flowchart for mapping paddy rice in SE and NE Asia using multiple data.
We used a rule-based method to map paddy rice and produce annual paddy rice maps for SE and NE Asia in 2017–2019 at 10 m resolution (NESEA-Rice10) using the phenological features of paddy rice (Fig. 3). The steps for generating the paddy rice maps are as follows.
In addition to the optical MODIS-based LSWI–EVI relationship approach, we
also applied the minimum value of VH data in the transplanting stage to
identify flooding signals, as suggested in previous studies (Clauss et al., 2018b). VH has a higher sensitivity in paddy rice growth stages than VV
polarization (Inoue et al., 2020; Nguyen et al., 2016; Wakabayashi et al., 2019). However, the minimum value of VH in different regions is different
because Sentinel-1 data are affected by the incidence angle (ranging from
approximately 30 to 45
The single cropping system for paddy rice identification is not ideal because of the difference in paddy rice cultivation time in some regions of SE Asia (Fikriyah et al., 2019; Shew and Ghosh, 2019). Therefore, we combined all paddy rice fields identified at different times into the annual map. We applied the improved method to generate the annual paddy rice maps for SE and NE Asia in 2017–2019. Please note that the method we improved may not extract rice fields (e.g., rain-fed paddy rice and upland rice) if flooding signals are not available (Xiao et al., 2006; Zhang et al., 2017).
It is challenging to evaluate the accuracy of the classification at
continental scales (Xiao et al., 2006; Zhang et al., 2020). We used two
strategies to evaluate the paddy rice maps as accurately as possible. First,
we compared the available agricultural statistics on a subnational level in
some countries (Table 1). Referring to the study of Xiao et al. (2006), we
calculated the annual area of paddy rice based on paddy intensity. The paddy
intensities of countries in NE Asia, Myanmar, Vietnam, and the Philippines
were 1, 1.4, 2.2, and 2, respectively. Second, we compared the spatial
consistency between our classification results and existing rice maps (Table 1). We used the coefficient of determination (
Comparison of the resultant annual paddy rice areas and the agricultural statistics at the subnational level in different countries from 2017 to 2019. The marginal kernel density plot above or to the right of each scatterplot shows the distribution of the data in one dimension.
The paddy rice maps in SE and NE Asia in 2017–2019 are presented in Figs. S8 and S9, respectively. We calculated the annual paddy rice area using the
pixel number approach for each administrative unit. The estimated annual
rice paddy areas were significantly correlated with the agricultural
statistics at subnational levels. The resultant paddy rice maps and the
agricultural statistics had relatively high correlations in Northeast China
(
Comparison of the annual paddy rice area between our classification and existing datasets at the subnational level in Northeast China
We further compared the resultant rice maps with existing rice maps at the
subnational level. The annually available datasets included the MODIS-based
rice paddy map with 500 m resolution for Northeast China in 2017 and the
JAXA-derived rice map with a 10 m resolution for Vietnam in 2017 (Sect. 2.2.6). The paddy rice area statistics from our maps and existing products significantly correlated with
Comparisons between the paddy rice area in our study and IRRI
dataset in SE Asia at the
The composite paddy rice map is a mosaic of rice planting areas in 3 years (2017–2019) where rice has been detected in 1 or more years. We
compared the composite paddy rice areas with IRRI products at the national
and subnational levels in SE and NE Asia. The results demonstrated that the
correlations between them were significant at both levels (
In NE Asia, paddy rice fields are primarily cultivated in the longitude
range from 123 to 134
Spatial distribution of classified composite paddy rice with a 10 m spatial resolution in NE Asia during 2017–2019. The curves represent the relative change rate in the distribution of the number of paddy rice pixels along the longitude and latitude gradients.
Paddy rice is generally planted in the plains and deltas of rivers in SE
Asia in the
latitude range from 10 to 21
Spatial distribution of classified composite paddy rice with 10 m spatial resolution in SE Asia during 2017–2019. The curves represent the relative change rate in the distribution of the number of paddy rice pixels along the longitude and latitude gradients.
Visual comparison of our paddy rice maps and existing products in
typical regions in 2017:
MODIS data were useful for mapping paddy rice at continental scales using combined EVI and LSWI analysis. Most paddy rice fields were fragmented in Asia (Li et al., 2020; Lowder et al., 2016). Therefore, it is difficult to solve the intra-class temporal variability of paddy rice pixels caused by the coarse resolution of 500 m (Dong et al., 2016b; Xiao et al., 2006). Mixed pixels may cause an overestimation of the rice cultivation areas (Nelson and Gumma, 2015). We improved the MODIS-based approach by incorporating Sentinel-1 data and used the approach to identify paddy rice fields in SE and NE Asia for 2017–2019. Reducing the mixed-pixel problem is the key point of the improved paddy rice mapping method. Compared with the paddy rice maps acquired from existing MODIS-based products, our classification provides more information about field details with a higher spatial resolution (10 m) (Fig. 9). Therefore, the integration of MODIS and Sentinel-1 data makes it possible to improve the accuracy of mapping paddy rice at continental scales.
Although our paddy rice maps are consistent with existing products, some uncertainty sources still affect the mapping results. First, identifying small paddy rice fields in hilly regions is challenging for MODIS data, which will lead to an underestimation of the area of paddy rice fields (Dong and Xiao, 2016; Zhang et al., 2015). For example, the rice planting area is smaller than the agricultural statistics in the mountainous provinces of the Republic of Korea (Fig. 10). The classification method relies on rice paddies containing irrigation water during transplanting stages. Therefore, rain-fed paddy rice and upland rice may not be detected because of the unavailability of flooding signals (Zhang et al., 2017). The main reason for the underestimation of the rice area in eastern Thailand may be that the flooding signal of rice was not detected, which has also been mentioned in previous studies (Bridhikitti and Overcamp, 2012; Guo et al., 2019; Zhang et al., 2020). Although MODIS data with a high temporal resolution was used in our method, the accuracy of rice maps is still affected by cloud contamination (Fig. 11a–b) (Dong and Xiao, 2016). Missing observations in Sentinel-1 data would lead to noteworthy omission errors (Fig. 11c–d). In addition, both the thresholds of different indicators and phenological information extracted by sample blocks may affect the accuracy of the classification (Dirgahayu and Parsa, 2019; Jeong et al., 2012; Li et al., 2020; Yeom et al., 2021). Finally, uncertainties in other land cover products used in this study may also affect the accuracy of the classification.
Estimated distribution of paddy rice in 2017 in mountainous
regions in South Korea:
Spatial distribution map of good-quality observation numbers
during 2017 to 2019 for
Under the combined effects of climate change and human activities, such as frequent extreme disasters, population growth, and urban expansion, knowing the spatial distribution of paddy rice is important for food security. The potential applications of the dataset include (1) improving paddy rice yield prediction accuracy, as crop masks are the basis for paddy rice yield prediction, and previous studies have demonstrated that the accuracy of crop masks affects the accuracy of yield prediction (J. Liu et al., 2019; Zhang et al., 2019); (2) assessing damage to agriculture from extreme hazards as floods are one of the major natural disasters in Southeast Asia, and high-resolution paddy rice maps will improve the accuracy of the area and yield loss estimates for flooded farmland (Phan et al., 2019); and (3) estimating greenhouse-relevant methane emissions. Paddy rice is an important source of methane in the atmosphere (Redeker et al., 2000). Accurate paddy rice maps and crop intensity maps facilitate the estimation of methane emissions (Zhang et al., 2020). In addition, paddy maps are helpful in making land-use decisions for the government.
Recently, as more Sentinel-2 images with higher resolutions have become available,
combining Sentinel-2 and other satellite images have improved the temporal
resolution of the data. For example, the Harmonized Landsat and Sentinel-2
(HLS) project provide images with 2–3 d at 30 m spatial resolution by
combining Landsat 8 satellite and Sentinel-2 satellite data (
The datasets of the paddy rice maps for SE and NE Asia from 2017 to 2019 are available on a public repository. A small example of the data can be found at the following DOI:
We constructed a paddy rice map database for SE and NE Asia for 3 years (2017–2019) at a 10 m spatial resolution (NESEA-Rice10) by integrating MODIS and Sentinel-1 data. The paddy rice planting areas in our database were significantly correlated with those from the official statistics. The distribution of paddy rice in the maps was consistent with existing data products. Additionally, our method reduced the effects of mixed pixels and provided more detailed spatial information than MODIS-based paddy rice maps. We demonstrated that multi-sensor data integration has the advantages of improving the spatial resolution of rice maps and reducing mixed pixels. To summarize, we provided more accurate paddy rice maps at continental scales using the improved method for paddy rice mapping.
The supplement related to this article is available online at:
ZZ and JH designed the study. JH, YL, and JC collected datasets. JH and YL implemented the research. JH, ZZ, YL, and JC wrote the original manuscript. JH, ZZ, YL, JC, LZ, FC, HZ, FT, and JZ revised the manuscript.
The contact author has declared that neither they nor their co-authors have any competing interests.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
We thank the editors and anonymous reviewers for
their valuable comments. We thank Maxine Garcia from Liwen Bianji (Edanz) (
This research has been supported by the National Natural Science Foundation of China (grant no. 42061144003).
This paper was edited by David Carlson and reviewed by two anonymous referees.