Rice Paddy Area Research Articles

Evaluation of interregional consistency in associations between neonicotinoid insecticides and functions of benthic invertebrate communities in rivers in urban rice-paddy areas

Neonicotinoid insecticides pose risks that need to be managed for conservation of aquatic ecosystems. In this study, we evaluated the associations between the estimated environmental concentrations of two neonicotinoid insecticides (imidacloprid and dinotefuran) and the total abundances of seven functional feeding groups (FFGs) of benthic invertebrate communities in rivers in urban rice-paddy areas in four Japanese regions. Regional datasets of benthic invertebrate communities and environmental variables available for Japan were analyzed. The associations between neonicotinoid exposure and benthic functional groups in each region were evaluated by applying a partial redundancy analysis to each regional dataset. We then examined whether there was an interregionally consistent pattern in the observed associations to ascertain the general applicability of the associations. In two of the four regions, the associations of the total abundances of the seven FFGs with neonicotinoid concentrations were significant, suggesting negative effects of imidacloprid and dinotefuran on river ecosystem functions in these two Japanese regions. Moreover, although the associations in the remaining two regions were not significant, the pattern of associations of the total abundances of six of the FFGs (shredders, filter feeders, collectors, grazers, predators, and scavengers) with imidacloprid concentrations seemed to be consistent among the four regions. This implies broad-scale negative effects of imidacloprid on river ecosystem functions in urban rice-paddy areas. We did not, however, find any interregionally consistent patterns in the associations with dinotefuran concentrations. This may be related to the multicollinearity with the imidacloprid concentrations and/or the low maximum dinotefuran concentration relative to the toxicity values of this neonicotinoid. Therefore, the association between dinotefuran and river ecosystem functions requires additional investigation. We believe that this type of hypotheses-generating research using country-wide biomonitoring and exposure databases can be a great aid in future ecological risk assessment studies.

Identifying floods and flood-affected paddy rice fields in Bangladesh based on Sentinel-1 imagery and Google Earth Engine

Globally, flooding is the leading cause of natural disaster related deaths, especially in Bangladesh where approximately one third of national area gets flooded annually by overflowing rivers during the monsoon season, which drastically affects paddy rice agriculture and food security. However, existing studies on the pattern of floods and their impact on agriculture in Bangladesh are limited. Here we examined the spatiotemporal pattern of floods for the country during 2014–2018 using all the available Sentinel-1 Synthetic Aperture Radar (SAR) images and the Google Earth Engine (GEE) platform. We also identified the flood-affected paddy rice fields by integrating the flooding areas and remote sensing-based paddy rice planting areas. Our results indicate that flooding is frequent in northeastern Bangladesh and along the three major rivers, the Ganges, Brahmaputra, and Meghna. Between 2014 and 2018, the flood-affected paddy rice areas accounted for 1.61–18.17% of the total paddy rice area. The satellite-based detection of floods and flood-affected paddy rice fields advance our understanding of the annual dynamics of flooding in Bangladesh, which is essential for adaptation and mitigation strategies where severe annual floods threaten human lives, properties, and agricultural production.

Optimal MODIS data processing for accurate multi-year paddy rice area mapping in China

ABSTRACT Researchers, policy makers, and farmers currently rely on remote sensing technology to monitor crops. Although data processing methods can be different among different remote sensing methods, little work has been done on studying these differences. In order for potential users to have confidence in remote sensing products, an analysis of mapping accuracies and their associated uncertainties with different data processing methods is required. This study used the MOD09A1 and MYD09A1 products of the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite, from which the Enhanced Vegetation Index (EVI) and the two-band EVI (EVI2) images were obtained. The objective of this study was to analyze the accuracy of different data processing combinations for multi-year rice area mapping. Sixteen combinations of EVI and EVI2 with two cloudy pixel removal methods (QA/BLUE) and four pixel replacement methods (MO/MY/MOY/MYO) were investigated over the Jiangsu Province of southeast China from 2006 to 2016. Different accuracy results were obtained with different data processing combinations for multi-year rice field mapping. Based on a comparison of the relative performance of different MODIS products and processing method combinations, EVI2_BLUE_MYO was proposed to be the optimal processing method, and was applied to forecasting the rice-planted area of 2017. Study results from 2006 to 2017 were validated against reference data and showed accuracies of rice area extraction of greater than 95%. The mean absolute error of transplanting, heading, and maturity dates were 11.55, 8.10, and 7.78 days, respectively. In 2017, two sample regions (A and B) were selected from places where rice fractional cover was greater than 75%. Rice area extraction accuracies of 85.0% (A) and 92.3% (B) were obtained. These results demonstrated the complementarity of MOD09A1 and MYD09A1 datasets in enhancing pixel spatial coverage and improving rice area mapping when atmospheric influences are significant. The optimal data processing combination indentified in this study is promising for accurate multi-year and large-area paddy rice information extraction and forecasting.

Fingerprint of rice paddies in spatial\u2013temporal dynamics of atmospheric methane concentration in monsoon Asia

Agriculture (e.g., rice paddies) has been considered one of the main emission sources responsible for the sudden rise of atmospheric methane concentration (XCH4) since 2007, but remains debated. Here we use satellite-based rice paddy and XCH4 data to investigate the spatial–temporal relationships between rice paddy area, rice plant growth, and XCH4 in monsoon Asia, which accounts for ~87% of the global rice area. We find strong spatial consistencies between rice paddy area and XCH4 and seasonal consistencies between rice plant growth and XCH4. Our results also show a decreasing trend in rice paddy area in monsoon Asia since 2007, which suggests that the change in rice paddy area could not be one of the major drivers for the renewed XCH4 growth, thus other sources and sinks should be further investigated. Our findings highlight the importance of satellite-based paddy rice datasets in understanding the spatial–temporal dynamics of XCH4 in monsoon Asia.

Remote Sensing Application in Mapping Agricultural Crop Areas and Monitoring Rice Maturity

Climate change has evolved in an unpredictable trend and droughts have occurred more and more severely in the central provinces of Vietnam. Determining the irrigated area and water requirement for various crops and the growth stage of each crop is an urgent need as water resources for irrigation are getting scarce year by year. This research examines the application of Sentinel-2 and Sentinel-1 images to map crop areas and identify the current development stage of paddy rice areas. The images are collected and pre-processed from 2017 to 2018 for Ha Tinh Province in Vietnam. The Maximus Likelihood method is used to interpret Sentinel-2 imagery for mapping agricultural crop distribution status. The research presents a new approach for identifying rice maturity using the Sentinel-1 image series. The Overall Accuracy (OA) and Kappa coefficient methods are used to evaluate the generated maps of the agricultural crop’s distribution status. This study shows the relationship between the Sentinel-1 VH band and the growth of rice. From the image bands, we could calculate the slope of the line correlating between the VH backscattering value and the growth time of rice. Along with the local planting schedule, rice life cycle, and simple deduction, we could determine the rice growth stage at each time of image acquisition. The results identifying the rice maturity progression are illustrated for Cam Hoa commune in Cam Xuyen district and Thach Hoi commune in Thach Ha district, Ha Tinh Province.

Cloud-based typhoon-derived paddy rice flooding and lodging detection using multi-temporal Sentinel-1&2

Rice production in China’s coastal areas is frequently affected by typhoons, since the associated severe storms, with heavy rain and the strong winds, lead directly to the rice plants becoming flooded or lodged. Long-term flooding and lodging can cause a substantial reduction in rice yield or even destroy the harvest completely. It is therefore urgent to obtain accurate information about paddy rice flooding and lodging as soon as possible after the passing of the storm. This paper proposes a workflow in Google Earth Engine (GEE) for mapping the flooding and lodging area of paddy rice in Wenzhou City, Zhejiang, following super typhoon Maria (Typhoon No.8 in 2018). First, paddy rice in the study area was detected by multi-temporal Sentinel-1 backscatter data combined with Sentinel-2-derived Normalized Difference Vegetation Index (NDVI) using the Random Forests (RFs) algorithm. High classification accuracies were achieved, whereby rice detection accuracy was calculated at 95% (VH + NDVI-based) and 87% (VV + NDVI-based). Secondly, Change Detection (CD) based Rice Normalized Difference Flooded Index (RNDFI) and Rice Normalized Difference Lodged Index (RNDLI) were proposed to detect flooding and lodged paddy rice. Both RNDFI and RNDLI were tested based on four different remote sensing data sets, including the Sentinel-1-derived VV and VH back-scattering coefficient, Sentinel-2-derived NDVI and Enhanced Vegetation Index (EVI). Overall agreement regarding detected area between the each two different data sets was obtained, with values of 79% to 93% in flood detection and 64% to 88% in lodging detection. The resulting flooded and lodged paddy rice maps have potential to reinforce disaster emergency assessment systems and provide an important resource for disaster reduction and emergency departments.

Analysis of the applicability of multi-temporal full polarimetric airborne L-band SAR scattering to paddy rice field mapping

ABSTRACTA Synthetic Aperture Radar (SAR) is an all-weather imaging system that is often used for mapping paddy rice fields and estimating the area. Fully polarimetric SAR is used to detect the microwave scattering property. In this study, a simple threshold analysis of fully polarimetric L-band SAR data was conducted to distinguish paddy rice fields from soybean and other fields. We analysed a set of ten airborne SAR L-band 2 (Pi-SAR-L2) images obtained during the paddy rice growing season (in June, August, and September) from 2012 to 2014 using polarimetric decomposition. Vector data for agricultural land use areas were overlaid on the analysed images and the mean value for each agricultural parcel computed. By quantitatively comparing our data with a reference dataset generated from optical sensor images, effective polarimetric parameters and the ideal observation season were revealed. Double bounce scattering and surface scattering component ratios, derived using a four-component decomposition algorithm, were key to extracting paddy rice fields when the plant stems are vertical with respect to the ground. The alpha angle was also an effective factor for extracting rice fields from an agricultural area. The data obtained during August show maximum agreement with the reference dataset of estimated paddy rice field areas.

Large increases of paddy rice area, gross primary production, and grain production in Northeast China during 2000–2017

China is the largest rice producer and consumer in the world. Accurate estimations of paddy rice planting area and rice grain production is important for feeding the increasing population in China. However, Southern China had substantial losses in paddy rice area over the last three decades in those regions where paddy rice has traditionally been produced. Several studies have shown increased paddy rice area in Northeast China. Here we document the annual dynamics of paddy rice area, gross primary production (GPP), and grain production in Northeast China (Heilongjiang, Jilin and Liaoning provinces) during 2000–2017 using agricultural statistical data, satellite images, and model simulations. Annual maps derived from satellite images show that paddy rice area in Northeast China has increased by 3.68 million ha from 2000 to 2017, which is more than the total combined paddy rice area of North Korea, South Korea, and Japan. Approximately 82% of paddy rice pixels had an increase in annual GPP during 2000–2017. The expansion of paddy rice area slowed down substantially since 2015. Annual GPP from those paddy rice fields cultivated continuously over the 18 years were moderately higher than that from other paddy rice fields, which suggested that improved management practices could increase grain production in the region. There was a strong linear relationship between annual GPP and annual rice grain production in Northeast China by province and year, which illustrates the potential of using satellite-based data-driven model to track and assess grain production of paddy rice in the region. Northeast China is clearly an emerging rice production base and plays an increasing role in crop production and food security in China. However, many challenges for the further expansion and sustainable cultivation of paddy rice in Northeast China remain.

Contribution of rice variety renewal and agronomic innovations to yield improvement and greenhouse gas mitigation in China

China’s rice agriculture, a primary source of greenhouse gases (GHGs), has experienced great changes in the last five decades due to changes in dominant varieties and farming practices. However, the impacts of these changes on GHG emissions have not been comprehensively assessed. While most earlier studies focused on the GHG emissions per unit area, recent research indicated that the yield-scaled impact may better reflect the reality. Through integrating the results from a meta-analysis, two multi-site field experiments and an expert survey, we quantify the integrated impacts of different practices on both area- and yield-scaled GHG emissions in China. Results from the expert survey showed that rice planting area has shifted northwards, and alternate water-saving (WS) irrigation has been applied in nearly 78% of Chinese rice paddy areas in both the rice nursery stage and post-transplanting period over the past five decades. The changes of these practices and replacement of new rice varieties have increased China’s rice yield by 131%. During the same period, cropping system adjustment, variety replacement, and implementation of WS irrigation have reduced total GHG emissions by 7%, 31%, and 42%, respectively. Also, the major decrease in GWP occurred prior to the 2000s, and the decreasing trend continued in the post-2000s period but at a slower rate. Our results have some limitations as these estimates ignore a number of important variations and interactions among management factors as well as climatic and edaphic conditions. Still, our findings illustrate that it is possible to enhance rice productivity at reduced environmental costs through screening for low emission varieties and agronomic practices. Future innovations should ensure that rice farming progressively adapts to climate change, while continuing to reduce GHG emissions.

Rice Acreage Delineation in the Nile Delta Based on Thermal Signature

Rice is one of the world’s significant food crops, especially in Egypt. The distribution data of planted and harvested paddy rice fields are essential for food security and the management of water resources. The determined area within agricultural associations is often subjective, expensive, labor intensive, time-consuming, and prone to errors. Therefore, this study aimed to determine the paddy rice areas in the Nile Delta using a new methodology based on satellite data. The rice areas could be classified with good results at the beginning of the 1st flooding period applying the Normalized Difference Vegetation Index (NDVI). The temporal profiles of NDVI and Land Surface Temperature (LST) were investigated. The NDVI profiles of different targets were analyzed to differentiate between rice and other crops, but the results failed to distinguish between rice and other crops in all stages after full vegetation cover. However, the analyzed LST profiles indicated the discrimination capability between rice and other crops in all vegetation development stages. The accuracy was high as 97% and 74% for LST and NDVI, respectively. The LST-based classification indicated that the distributed rice fields throughout the study area were 32.7% of the total cultivated area. The accuracy of LST results had an advantage over NDVI.

Cooling Effect of Paddy on Land Surface Temperature in Cold China Based on MODIS Data: A Case Study in Northern Sanjiang Plain

Fast-growing crops have been evolved in North China, accompanied by intense paddy expansion, leading to dramatic impacts on the agricultural environment. Among these environmental issues, the impact of paddy expansion on land surface temperature is still unclear. In the present study, based on Landsat images and MODIS land surface temperature (LST) products, the crop pattern and monthly LST in the northern Sanjiang Plain are obtained. A 1 km scale grid unit is built to investigate the relationship between LST and paddy expansion. The results obtained from the study are as follows. Firstly, for crop patterns, cropland planting is given priority to paddy fields, accompanied by an aggregated pattern, while upland crops present a discrete pattern. Secondly, for LST changes during the growing season, the maximum LST occurs in June, and the lowest values occur in October across the whole region. In addition, the LST of paddy fields is lower compared with that of upland crops for the whole growing season. Thirdly, at the 1 km grid scale, the relationship between monthly LST and paddy field ratio is significantly negative, and better represented by a cubic function rather than a linear fit. Finally, LST decreases with the increased fraction of the rice paddy area more rapidly when rice paddy is aggregated and accounted for by more than 80% of each study grid. The findings of this study are important to guide agricultural production and to better understand the environmental effects of paddy expansion in cold regions.

Assessment of Wheat Straw Cover and Yield Performance in a Rice-Wheat Cropping System by Using Landsat Satellite Data

Proper straw cover information is one of the most important inputs for agroecosystem and environmental modeling, but the availability of accurate information remains limited. However, several remote-sensing (RS)-based studies have provided a residue cover estimation and provided spatial distribution mapping of paddy rice areas in a constant field condition. Despite this, the performance of rice crops with straw applications has received little attention. Furthermore, there are no methods currently available to quantify the wheat straw cover (WSC) percentage and its effect on rice crops in the rice-wheat cropping region on a large scale and a continuous basis. The novel approach proposed in this study demonstrates that the Landsat satellite data and seven RS-based indices, e.g., (i) normalized difference vegetation index (NDVI), (ii) Normalized difference senescent vegetation index (NDSVI), (iii) Normalized difference index 5 (NDI5), (iv) Normalized difference index 7 (NDI7), (v) Simple tillage index (STI), (vi) Normalized difference tillage index (NDTI), and (vii) Shortwave red normalized difference index (SRNDI), can be used to estimate the WSC percentage and determine the performance of rice crops over the study area in Changshu county, China. The regression model shows that the NDTI index performed better in differentiating the WSC at sampling points with a coefficient of determination (R2 = 0.80) and root mean squared difference (RMSD = 8.46%) compared to that of other indices, whereas the overall accuracy for mapping WSC was observed to be 84.61% and the kappa coefficient was κ = 0.76. Moreover, the rice yield model was established by correlating between the peak NDVI values and rice grain yield collected from ground census data, with R2 = 0.85. The finding also revealed that the highest estimated yield (8439.67 kg/ha) was recorded with 68% WCS in the study region. This study confirmed that the NDVI and NDTI algorithms are very effective and robust indicators. Also, it can be strongly concluded that multispectral Landsat satellite imagery is capable of measuring the WSC percentage and successively determines the impact of different WSC percentages on rice crop yield within fields or across large regions through remote sensing (RS) and geographical information system (GIS) techniques for the long-term planning of agriculture sustainability in rice-wheat cropping systems.

Evaluation of MOD16 Algorithm over Irrigated Rice Paddy Using Flux Tower Measurements in Southern Brazil

Evapotranspiration (ET) is an important component of the hydrological cycle. Understanding the ET process has become of fundamental importance given the scenario of global change and increasing water use, especially in the agricultural sector. Determining ET over large agricultural areas is a limiting factor due to observational data availability. In this regard, remote sensing data has been used to estimate ET. In this study, we evaluated the Moderate-Resolution Imaging Spectroradiometer (MODIS) land surface ET product estimates (hereafter MOD16 ET – MODIS Global Terrestrial Evapotranspiration Product) over two rice paddy areas in Southern Brazil, through the ET measured using the eddy covariance technique (hereafter EC). The energy balance components were evaluated during fallow and flooded seasons showing latent heat flux dominates in both seasons. The results showed that MOD16 ET underestimated EC measurements. Overall, the RMSE (root mean square error) ranged between 13.40 and 16.35 mm 8-day−1 and percent bias (PBIAS) ranged between −33.7% and −38.7%. We also assessed the ET (measured and estimated) main drivers, with EC yielding higher correlation against observed net radiation (Rn) and global radiation (Rg), followed by air temperature (Temp) and vapor pressure deficit (VPD), whilst MOD16 ET estimates yielded higher correlation against leaf area index (LAI) and fraction of photosynthetically active radiation (fPAR). The MOD16 algorithm was forced with meteorological measurements but the results did not improve as expected, suggesting a low sensitivity to meteorological inputs. Our results indicated when a water layer was present over the soil surface without vegetation (LAI around zero), the largest differences between EC measurements and MOD16 ET were found. In this period, the expected domain of soil evaporation was not observed in MOD16 ET physical processes partition, indicating the algorithm was not able to detect areas with high soil moisture. In general, the MOD16 ET product presented low accuracy when compared against experimental measurements over flooded rice paddy, suggesting more studies are necessary, in order to reduce uncertainties associated to the land cover conditions.

Rapid Assessment of Flood Inundation and Damaged Rice Area in Red River Delta from Sentinel 1A Imagery

The Red River Delta (RRD), including 11 provinces, is one of the four largest rice-growing areas in Vietnam. Tropical storms often occur and cause serious flooding from May to October annually in the RRD, which strongly affects the productivity of the summer–autumn rice, one of two main rice crops. Therefore, the rapid assessment of damaged rice area by flooding inundation is critical for farmers and the government. In this study, we proposed a methodology for quick estimation of rice areas damaged by flooding using Sentinel 1A (S1A) imagery. Firstly, the latest rice map was produced. Then, a Near Real-Time (NRT) flood map, which is estimated from S1A images at the closest time to a flooding event, was generated by excluding the yearly permanent map from the temporal water map. Our experiment was conducted for the assessment of damaged rice area by flooding from the tropical storm named Son-Tinh, which happened on 19–21 July 2018. A Support Vector Machine (SVM) classifier was applied on time-series of S1A VV with VH data (VVVH) to obtain a rice map for the winter-spring season of 2018 with 90.5% Overall Accuracy (OA) and 2.37% difference (12,544 ha) from the General Statistics Office (GSO) of Vietnam’s reports for the whole region. Then, the Otsu thresholding method was applied for permanent water surface extraction and NRT flood mapping. The estimated damaged area was compared to available provincial and communal statistics for validation and further analysis. Right after the Son-Tinh storm, the estimation of inundated rice was approximately 50% of the total rice area in the RRD (271,092 ha). As a result, rice damage level strongly corresponds to the inundation period. In addition, the rice-flooding frequency map over the RRD was estimated to show rice fields suffering a high risk of flooding during the rainy season in the RRD. Our experiment’s results highlight the potential of using Synthetic-Aperture Radar (SAR) imagery for fast monitoring and assessment of paddy rice areas affected by flooding at a large scale in the RRD region.

Mapping high temperature damaged area of paddy rice along the Yangtze River using Moderate Resolution Imaging Spectroradiometer data

ABSTRACT The subtropical high temperature anomaly of 2013 in China, adversely affected paddy rice physiology, especially at the heading, flowering and grain filling stages, and left untold damage and significant rice yield reductions in that year. Considering the importance of data on crop failure induced by abnormal meteorological events, in understanding the potential impacts of climate change on agriculture and food security, and in support of crop insurance policies, the current study employs satellite remote sensing data to map and estimate the areal extent of high-temperature-induced damage on paddy rice in 2013, along the Yangtze River, a major rice producing area in China. In this regard, the daily mean and maximum air temperatures were first estimated using the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) products, MODIS Vegetation Indices (VI) products, altitude information from a Digital Elevation Model (DEM), and air temperature measurements at meteorological stations located within the Yangtze River Basin from 2000 to 2016, to derive fully covered mean and maximum daily air temperatures. Paddy rice cultivated areas were then identified based on the seasonal profiles of MODIS Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) in 2013, and map errors at provincial scales were no more than 8.7%. Finally, paddy rice growth characteristics were combined with MODIS VI products to identify heading and flowering stages, and the time series of daily mean and maximum air temperatures, paddy rice phenology and cultivated area, were integrated to map and estimate the rice crop area damaged by the anomalous high temperature. Our results show that all provinces along the Yangtze River, this study investigated, recorded a substantial damaged rice crop area, following the 2013 subtropical temperature high, and Anhui Province was the hardest hit with a damaged rice area of 63.6%. For the entire study area, a damage extent of about 5,656,000 ha, accounting for 35.9% of total paddy rice planted area, was recorded. This indicates a significant agro-meteorological disaster, and in an era of increasing climate variability and weather extremes, studies of this nature would greatly benefit global food security planning and management policies.

Evaluating the Effectiveness of Spatially Reconfiguring Erosion Hot Spots to Reduce Stream Sediment Load in an Upland Agricultural Catchment of South Korea

Upland agricultural expansion and intensification cause soil erosion, which has a negative impact on the environment and socioeconomic factors by degrading the quality of both nutrient-rich surface soil and water. The Haean catchment is a well-known upland agricultural area in South Korea, which generates a large amount of sediment from its cropland. The transportation of nutrient-rich sediment to the stream adversely affects the water quality of the Han River watershed, which supports over twenty million people. In this paper, we suggest a spatially explicit mitigation method to reduce the amount of sediment yield to the stream of the catchment by converting soil erosion hot spots into forest. To evaluate the effectiveness of this reconfiguration, we estimated the sediment redistribution rate and assessed the soil erosion risk in the Haean catchment using the daily based Morgan–Morgan–Finney (DMMF) model. We found that dry crop fields located in the steep hill-slope suffer from severe soil erosion, and the rice paddy, orchard, and urban area, which are located in a comparatively lower and flatter area, suffer less from erosion. Although located in the steep hill-slope, the forest exhibits high sediment trapping capabilities in this model. When the erosion-prone crop lands were managed by sequentially reconfiguring their land use and land cover (LULC) to the forest from the area with the most severe erosion to the area with the least severe erosion, the result showed a strong reduction in sediment yield flowing to the stream. A change of 3% of the catchment’s crop lands of the catchment into forest reduced the sediment yield entering into the stream by approximately 10% and a change of 10% of crop lands potentially resulted in a sediment yield reduction by approximately 50%. According to these results, identifying erosion hot spots and managing them by reconfiguring their LULC is effective in reducing terrestrial sediment yield entering into the stream.

Mapping Paddy Rice Using Sentinel-1 SAR Time Series in Camargue, France

This study proposes an effective method to map rice crops using the Sentinel-1 SAR (Synthetic Aperture Radar) time series over the Camargue region, Southern France. First, the temporal behavior of the SAR backscattering coefficient over 832 plots containing different crop types was analyzed. Through this analysis, the rice cultivation was identified using metrics derived from the Gaussian profile of the VV/VH time series (3 metrics), the variance of the VV/VH time series (one metric), and the slope of the linear regression of the VH time series (one metric). Using the derived metrics, rice plots were mapped through two different approaches: decision tree and Random Forest (RF). To validate the accuracy of each approach, the classified rice map was compared to the available national data. Similar high overall accuracy was obtained using both approaches. The overall accuracy obtained using a simple decision tree reached 96.3%, whereas an overall accuracy of 96.6% was obtained using the RF classifier. The approach, therefore, provides a simple yet precise and powerful tool to map paddy rice areas.

Limited potential of harvest index improvement to reduce methane emissions from rice paddies.

Rice is a staple food for nearly half of the world's population, but rice paddies constitute a major source of anthropogenic CH4 emissions. Root exudates from growing rice plants are an important substrate for methane-producing microorganisms. Therefore, breeding efforts optimizing rice plant photosynthate allocation to grains, i.e., increasing harvest index (HI), are widely expected to reduce CH4 emissions with higher yield. Here we show, by combining a series of experiments, meta-analyses and an expert survey, that the potential of CH4 mitigation from rice paddies through HI improvement is in fact small. Whereas HI improvement reduced CH4 emissions under continuously flooded (CF) irrigation, it did not affect CH4 emissions in systems with intermittent irrigation (II). We estimate that future plant breeding efforts aimed at HI improvement to the theoretical maximum value will reduce CH4 emissions in CF systems by 4.4%. However, CF systems currently make up only a small fraction of the total rice growing area (i.e., 27% of the Chinese rice paddy area). Thus, to achieve substantial CH4 mitigation from rice agriculture, alternative plant breeding strategies may be needed, along with alternative management.

Mapping paddy rice in Jiangsu Province, China, based on phenological parameters and a decision tree model

Timely and accurate mapping of rice planting areas is crucial under China’s current cropping structure. This study proposes a new paddy rice mapping method by combining phenological parameters and a decision tree model. Six phenological parameters were developed to identify paddy rice areas based on the analysis of the Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) time series and the Land Surface Water Index (LSWI) time series. The six phenological parameters considered the performance of different land cover types during specific phenological phases (EVI1 and EVI2), one-half of or the entire rice growing cycle (LSWI1 and LSWI2), and the shape of the LSWI time series (KurtosisLSWI and SkewnessLSWI). A hierarchical decision tree model was designed to classify paddy rice areas according to the potential separability of different land cover types in paired phenological parameter spaces. Results showed that the decision tree model was more sensitive to LSWI1, LSWI2, and SkewnessLSWI than the other phenological parameters. A paddy rice map of Jiangsu Province for 2015 was generated with an optimal threshold set of (0.4, 0.42, 9, 19, 1.5, –1.7, 0.0) with a total accuracy of 93.9%. The MODIS-derived paddy rice map generally agreed with the paddy land fraction map from the National Land Cover Dataset project, but there were regional discrepancies because of their different definitions of land use and the inability of MODIS to map paddy rice at a fragmental level. The MODIS-derived paddy rice map showed high correlation (R2= 0.85) with county-level agricultural statistics. The results of this study indicate that the phenological parameter-based paddy rice mapping algorithm could be applied at larger spatial scales.

Estimating rice production in the Mekong Delta, Vietnam, utilizing time series of Sentinel-1 SAR data

Rice is the most important food crop in Asia and rice exports can significantly contribute to a country's GDP. Vietnam is the third largest exporter and fifth largest producer of rice, the majority of which is grown in the Mekong Delta. The cultivation of rice plants is important, not only in the context of food security, but also contributes to greenhouse gas emissions, provides man-made wetlands as an ecosystem, sustains smallholders in Asia and influences water resource planning and run-off water management. Rice growth can be monitored with Synthetic Aperture Radar (SAR) time series due to the agronomic flooding followed by rapid biomass increase affecting the backscatter signal. With the advent of Sentinel-1 a wealth of free and open SAR data is available to monitor rice on regional or larger scales and limited data availability should not be an issue from 2015 onwards. We used Sentinel-1 SAR time series to estimate rice production in the Mekong Delta, Vietnam, for three rice seasons centered on the year 2015. Rice production for each growing season was estimated by first classifying paddy rice area using superpixel segmentation and a phenology based decision tree, followed by yield estimation using random forest regression models trained on in situ yield data collected by surveying 357 rice farms. The estimated rice production for the three rice growing seasons 2015 correlates well with data at the district level collected from the province statistics offices with R2s of 0.93 for the Winter–Spring, 0.86 for the Summer–Autumn and 0.87 for the Autumn–Winter season.