Rice Area Research Articles

Ratoon Rice System of Production: A Rapid Growth Pattern of Multiple Cropping in China: A Review.

In this review, the significance of ratoon rice was introduced, and the research status and development trends of ratoon rice were also summarized. It is pointed out that mechanically harvested ratoon rice is the developing direction of future ratoon rice. On this basis, we analyzed the relationship between the yield of ratoon rice and many factors, such as variety characteristics, sowing date, water control, fertilizer, and many others. It is important to construct a comprehensive and practical evaluation system for rice regeneration that can provide a basis for high-yield cultivation of machine-harvested ratoon rice. At the same time, it is suggested that combining high-yield cultivation with the green ecological efficiency of rice can achieve better production and improve the quality of rice. Finally, some problems with ratoon rice development were put forward. An in-depth study on the rhizosphere biology and regulation techniques of ratoon rice and the effective ecological compensation mechanism increased the capacity and quality of ratoon rice. Further, the functioning of such research can enhance the planting area for ratoon rice and improve food security.

Stop the blame game: An analysis of blaming on Weibo during the early days of the COVID‐19 pandemic in rice and wheat areas in China

AbstractIn the pandemic era, social media has provided the public with a platform to make their voice heard. One of the most important public opinions online during a pandemic is blame. Blame can lead to stigma towards patients as well as potential patients and decrease social cooperation, which might impede prevention and control measures during epidemics. Thus, studying online blame during the early days of COVID‐19 can facilitate the management and control of future pandemics. By analyzing 3791 posts from one of the most popular social media sites in China (Weibo) over the 10 days immediately after COVID‐19 was declared to be a communicable disease, we found that there were four main agents blamed online: Individuals, corporations, institutions, and the media. Most of the blame targeted individual agents. We also found that there were regional‐cultural differences in the detailed types of blamed individual agents, that is, between rice‐ and wheat‐farming areas in China. After controlling influence of distance from the epicenter of Wuhan, there were still stable differences between regions: people in wheat areas had a higher probability of blaming agentic, harmful individuals, and people in rice areas had a higher probability of blaming individuals with low awareness of social norms for preventive health behavior. Findings have implications for preventing and predicting blame across cultures in future pandemics.

Comparison of Different Supervised Classification Algorithms for Mapping Paddy Rice Areas Using Landsat 9 Imageries

Rice is known to be one of the most essential crops in Turkey, as well as many other countries especially in Asia, whereas paddy rice cropping systems have a key role in many processes ranging from human nutrition to environment-related perspectives. Therefore, determination of cultivation area is still a hot topic among researchers from various disciplines, planners, and decision makers. In present study, it was aimed to evaluate performances of three classifications algorithms among most widely used ones, namely, maximum likelihood (ML), random forest (RF), and k-nearest neighborhood (KNN), for paddy rice mapping in a mixed cultivation area located in Biga District of Çanakkale Province, Turkey. Visual, near-infrared and shortwave infrared bands of Landsat 9 acquired in dry season of 2022 year was utilized. The classification scheme included six classes as dense vegetation (D), sparse vegetation (S), agricultural field (A), water surface (W), residential area – base soil (RB), and paddy rice (PR). The performances were tested using the same training samples and accuracy control points. The reliability of each classification was evaluated through accuracy assessments considering 150 equalized randomized control points. Accordingly, RF algorithym could identify PR areas with over 96.0% accuracy, and it was followed by KNN with 92.0%.

Importance of Red Rice (Deep Water Rice) Production and Its Potential of Export from Dhemaji District of Assam

Red kernelled rice is gaining importance in recent years due to its higher nutritive content as well as health advantages. It is rich in fibre, iron, Vitamin B1 & B2, zinc and calcium. Because of its importance and a good potential for export the present study was undertaken in Dhemaji district of Assam with the objective to find out the economics of production and scope of marketing of red kernelled rice. From the investigation it was found that red kernelled rice is cultivated in deep water areas of the district and 40% of the total rice area is under red kernelled rice. The production very low with an average production of 21q/ha. The benefit cost ratio was found to be 1.29 and only two marketing channels existed in the district. A company viz. Nature Bio Foods Ltd. (NBFL) was the main company which procured all the production of red kernelled rice from the producers in Dhemaji district which has its Office at Gogamukh. About 10000tons of red rice is being exported outside the district every year to other states and even other countries like USA, UK and European countries. The price spread was found to be high though there are few intermediaries in the marketing channel. From the investigation it can be concluded that there is much scope of increasing the area, production and productivity of red kernelled rice in the district provided processing centers are set up in the district so that the farmers can get a better price for their produce.

Crop response pattern to several drought timescales and its possible determinants: A global-scale analysis during the last decades

Crop response characteristics to different timescales of precipitation deficit may represent crop system resilience to drought characteristics. In this study, we assess the crop yield response of major crops to meteorological drought estimated by a standardized precipitation index with multiple timescales (1-12 months) during 1981-2016 all over the globe. We estimate that about one- to two-thirds of global harvested areas of maize, rice, soybean, and wheat, were significantly affected by various drought timescales. Soybean and wheat might respond to more prolonged droughts, while rice and maize responded to short-medium drought time scales. Using multiple machine learning models, we reveal that set of determinants could explain most variations of crop response to drought timescale with average accuracies between 45.7% - 56.0% (across models and crop types). Moreover, this study suggests that crops in warmer and higher water availability (precipitation minus potential evapotranspiration) might respond significantly to more short-term drought. The other factors (i.e., socioeconomic, fertilizer, soil, topography, production, irrigation) shows a complex and weaker effect on defining crop vulnerability to the various drought characteristics. This study attempts to fill the gaps in understanding global crop resistance to different drought characteristics. The future challenge in understanding the multifaceted effect of physical and socioeconomic factors on global crop vulnerability to drought may remain and should be addressed in further studies.

Detection of paddy rice drought stress with sentinel image vegetation index and the relation with productivity in Allatengae Village, Bantimurung District, Maros Regency

Maros Regency is one of the rice storage areas in South Sulawesi. According to the Central Bureau of Statistics for Maros Regency in 2020, the harvested area of rice decreased by 10.11% or 4,849.55 Ha compared to 2018 and rice production decreased by 16.96% or 37,719.68 tons. This causes the productivity of rice also decreased. One of the factors causing the decline in rice production in Maros Regency is drought. Sentinel 2 imagery is one of the remote sensing data that can be used to detect rice drought using the drought index method. This study aims to detect dryness of paddy rice in Allatengae Village using Sentinel-2A satellite imagery based on NDDI index analysis and determine the relationship between drought index and rice productivity. The method used is the Normalized Difference Drought Index (NDDI) method to determine the dryness level of paddy fields based on NDVI and NDWI parameters and uses regression and correlation analysis methods. From the results of this study, dry rice in Allatengae Village was detected in September and October 2021. In September (42 HST), the average NDDI value was 0.0254 and was in a mild drought condition while in October (62 HST), the average NDDI value is 0.2425 and is in moderate class drought conditions. From the results of the analysis of the relationship between the drought index and rice productivity, there is a strong (negative) correlation, where the higher the NDDI index value, the lower the rice productivity value.

The relation between vegetation indices of the generative stage with productivity of paddy fields

Maros regency is one of the agricultural areas in South Sulawesi which is a large rice area. Sourced from BPS data (Central Statistics Agency), both rice production and productivity in Maros Regency have decreased fluctuatingly over the last three years. Knowledge related to rice productivity is very important to know in order to support food security. Geographic information system is one of the modern technologies that can be used in estimating rice productivity which then utilizes Sentinel-2 Image data. In its use, a spectral transformation is used to display aspects of plant conditions such as vegetation index NDVI (Normalized Difference Vegettation Index), EVI (Enchanced Vegetation Index) and SAVI (Soil Adjusted Vegetation Index). The purpose of this study was to determine the relation between the vegetation index value of the generative stage and the productivity of paddy fields in Alatengae village. The method used is a simple linear regression method in estimating the relation between rice productivity and the vegetation index NDVI, EVI and SAVI. From the analysis of the relation between the vegetation index and rice productivity, there is a very strong (positive) correlation, where the higher the value of the vegetation index, it will be higher the plant productivity.

The Role of Traditional Institution (Keujreun Blang) in Realizing the Agricultural Industrialization in Nagan Raya, Aceh Province, Indonesia

Keujruen Blang is a traditional institution engaged in agriculture to assist farmers in managing rice areas, community development, and solving problems of agricultural businesses. It also involved a village apparatus as a functional technical implementing element that regulates rice field management at the village level geographically in an agricultural area. Therefore, the role and function of Keujreng Blang in the scope of government is crucial to be optimized to increase community participation in village development of Nagan Raya Regency, precisely in Beutong District, Babah Krueng Village. This area is one of the agricultural villages that must realize industrialization in food independence. Using BPS data, rice production in Nagan Raya Regency declined from 37,457.89 tons to 21,467.69 tons. This study was carried out qualitatively with descriptive analysis. Interview techniques, documentation, and focus group discussion were used as data collection methods. Data analysis was employed for data collection, reduction, presentation, and verification or conclusion. The results of this study indicated that the position of traditional institutions is still fragile, nominally and instrumentally. Also, this study showed that Keujruen Blang could not be implemented comprehensively with the provisions of the applicable law.

Identification of suitable genotypes of lentil (Lens culinaris) for improved adaptation to rice fallow areas of Gangetic Alluvial Zone

Rice fallows (RF) are the low lying kharif sown rice (Oryza sativa L.) areas that remain uncropped due to dearth of suitable cultivars of winter (rabi) pulses. A panel of 30 promising high yielding lentil (Lens culinaris Medik.) genotypes of diverse origin were assessed at both no till RF and with till condition at the Regional Research Substation (RRSS), Chakdah under the aegis of Bidhan Chandra Krishi Viswavidyalaya, Nadia, West Bengal for two years (2019–20 and 2020–21). Multi-trait performance (earliness, biomass and grain yield) of each genotype was considered during recommendation of suitable genotype for specific ecology deploying GGE biplot. The presentstudy recommended IC 560183 for no till RF ecology and Moitree, IC 559996, ILL 7978 and L 1112-19 for with till ecology having specific adaptation. Additionally, 2011S-56212-2 and ILL 8006 were identified as ideal and desirable genotypes for both the ecologies and therefore, recommended for commercial cultivation across the areas of Gangetic alluvial zone for augmenting lentil production and productivity.

Building soil to reduce climate change impacts on global crop yield

Improving soil health and resilience is fundamental for sustainable food production, however the role of soil in maintaining or improving global crop productivity under climate warming is not well identified and quantified. Here, we examined the impact of soil on yield response to climate warming for four major crops (i.e., maize, wheat, rice and soybean), using global-scale datasets and random forest method. We found that each °C of warming reduced global yields of maize by 3.4%, wheat by 2.4%, rice by 0.3% and soybean by 5.0%, which were spatially heterogeneous with possible positive impacts. The random forest modeling analyses further showed that soil organic carbon (SOC), as an indicator of soil quality, dominantly explained the spatial heterogeneity of yield responses to warming and would regulate the negative warming responses. Improving SOC under the medium SOC sequestration scenario would reduce the warming-induced yield loss of maize, wheat, rice and soybean to 0.1% °C−1, 2.7% °C−1, 3.4% °C−1 and − 0.6% °C−1, respectively, avoiding an average of 3%–5% °C−1 of global yield loss. These yield benefits would occur on 53.2%, 67.8%, 51.8% and 71.6% of maize, wheat, rice and soybean planting areas, respectively, with particularly pronounced benefits in the regions with negative warming responses. With improved soil carbon, food systems are predicted to provide additional 20 to over 130 million tonnes of food that would otherwise lose due to future warming. Our findings highlight the critical role of soil in alleviating negative warming impacts on food security, especially for developing regions, given that sustainable actions on soil improvement could be taken broadly.

Differences in Fe toxicity response index and associated growth characteristics among rice genotypes

ABSTRACT Acid sulfate soils in tropical coastal rice areas contain high Fe levels, and excess ferrous (Fe2+) ions are released during the early growth stage in the wet season. In such environments, Fe toxicity can cause significant yield losses. The objectives of this study were to evaluate the seedling growth of diverse rice germplasm (from japonica, aus and indica groups) under high-Fe conditions in hydroponic culture and to elucidate the growth characteristics of Fe toxicity tolerant genotypes. We calculated an Fe toxicity response index (FTRI) as a selection criterion that reflects both the response of shoot biomass to high Fe and the amount of Fe-induced leaf bronzing damage: higher FTRI values indicate greater tolerance to high Fe levels. We first compared Fe levels to determine the appropriate conditions for genotype selection, and then screened 2- to 3-week-old seedlings grown for 2 more weeks in hydroponic solution containing 500 mg Fe2+ L−1. Sixteen genotypes were screened, and for five of them, the differences in FTRI corresponded with previously reported yield performance in high-Fe fields. We identified Taichung65 (temperate japonica) as a promising donor, as it showed little bronzing damage and high relative (stress/control) biomass under high-Fe conditions, resulting in a high FTRI. Plant nutrient analyses of Taichung65 suggested that maintenance of low Fe concentration and low total Fe content in shoots despite high Fe concentration in the rhizosphere, through prevention of either root Fe uptake or Fe translocation from the root to the shoot, should be an important mechanism of Fe toxicity tolerance.

High-Resolution Ratoon Rice Monitoring under Cloudy Conditions with Fused Time-Series Optical Dataset and Threshold Model

Ratoon rice, an effective rice cultivation system, allows paddy rice to be harvested twice from the same stubble, playing an important role in ensuring food security and adapting to climate change with its unique growth characteristics. However, there is an absence of research related to remote-sensing monitoring of ratoon rice, and the presence of other rice cropping systems (e.g., double-season rice) with similar characteristics poses a hindrance to the accurate identification of ratoon rice. Furthermore, cloudy and rainy regions have limited available remote-sensing images, meaning that remote-sensing monitoring is limited. To address this issue, taking Yongchuan District, a typical cloud-prone region in Chongqing, China, as an example, this study proposed the construction of a time-series optical dataset using the Modified Neighborhood Similar Pixel Interpolator (MNSPI) method for cloud-removal interpolation and the Flexible Spatiotemporal DAta Fusion (FSDAF) model for fusing multi-source optical remote-sensing data, in combination with vegetation index features and phenological information to build a threshold model to map ratoon rice at high-resolution (10 m). The mapping performance of ratoon rice was evaluated using independent field samples to obtain the overall accuracy and kappa coefficient. The findings indicate that the combination of the MNSPI method and FSDAF model had a stable and effective performance, characterized by high correlation coefficient (r) values and low root mean square error (RMSE) values between the restored/predicted images and the true images. Notably, it was possible to effectively capture the distinct characteristics of ratoon rice in cloudy and rainy regions using the proposed threshold model. Specifically, the identified area of ratoon rice in the study region was 194.17 km2, which was close to the official data (158–180 km2), and the overall accuracy and kappa coefficient of ratoon rice identification result were 90.73% and 0.81, respectively. These results demonstrate that our proposed threshold model can effectively distinguish ratoon rice during vital phenological stages from other crop types, enrich the technical system of rice remote-sensing monitoring, and provide a reference for agricultural remote-sensing applications in cloudy and rainy regions.

Mapping Rice Area in the Cauvery Delta Zone of Tamil Nadu Using Sentinel 1A Synthetic Aperture Radar (SAR) Data

Since remote sensing based crop inventory provides accurate and timely information as compared to the conventional survey methods of estimating area, Multi-temporal Sentinel 1A Synthetic Aperture Radar data was used for the estimation of rice area during Samba season 2022 in the Cauvery delta zone comprising Thanjavur, Thiruvaru, Mayiladuthurai, and Nagapatnam districts of Tamil Nadu. SAR data was preferred over optical satellite data due to excess cloud cover during cropping the major season in Tamil Nadu. Temporal back-scatter (dB) signature of rice crop was generated from the multi-temporal processed SAR data utilizing the modules of a fully automated MAPscape software aiding the discriminating of the crop from others. The signatures revealed that the dB levels to be the lowest during agronomic floods, reached the highest during maximum tillering stage and started declining thereafter. Multi-temporal feature extraction module of Mapscape was used to estimate rice area and validated for accuracy using ground truth data collected during survey. A total of 3.05 lakh ha of rice area was estimated with an overall accuracy of 90.8 % and 0.82 kappa coefficient. Largest area of 1.12 lakh ha was recorded in Thanjavur followed by Thiruvarur and Mayiladuthurai with 0.95 and 0.51 lakh ha respectively.

Crop establishment and diversification strategies for intensification of rice-based cropping systems in rice-fallow areas in Odisha

Context or problemIn the Indian state of Odisha, rice-based system productivity is poor due to: (i) low rice yield in the monsoon (wet) season (2–4 t ha−1 compared to 6–8 t ha−1 in Punjab or Haryana); and (ii) limited cropping during the post-monsoon (dry) season (59% of the wet season rice area is left fallow in the dry season). ObjectiveOur study identifies strategies for increasing rice-based system productivity through: (i) alternative crop establishment methods in the wet season (Dry-Direct Seeded Rice or DSR, and mechanical puddled transplanted rice or PTR-M) to traditional methods such as broadcasting followed by post-emergence tillage (locally known as beushening) and manual random puddled transplanted rice (PTR-R); (ii) to identify rice-fallow areas suitable for pulse and oilseed cultivation in the dry season; and (iii) to evaluate the performance of short-duration pulses (green gram, Vigna radiata; black gram, Vigna mungo), and oilseeds (Brassica rapa var. toria, Helianthus annuus) in rice-fallow areas in the dry season. MethodsOn-farm experiments were conducted between 2017 and 2019 in three districts of Odisha (Bhadrak, Cuttack and Mayurbhanj) to evaluate DSR compared to beushening and PTR-R; and PTR-M compared to PTR-R and manual line puddled transplanted rice (PTR-L) in the wet season. The data from Landsat-8 Operational Land Imager (OLI) and Sentinel-1satellite sensors was used to identify rice-fallow areas, and the daily SMAP (Soil Moisture Active Passive) L-band soil moisture was used for mapping suitable rice-fallow areas for growing pulses and oilseeds. Short duration crops were evaluated in suitable rice-fallow areas. ResultsIn the wet season, DSR (range −4 to + 53%) had a significant effect on rice yield over beushening. Similarly, PTR-M consistently increased rice yield by 16–26% over PTR-R, and by 5–23% over PTR-L. In the dry season, pulse crops (green gram and black gram) performed well compared to Indian mustard under rainfed cultivation. However, under irrigated conditions, dry-season rice yield was more productive than the rice equivalent yield of green gram, black gram and sunflower. We found that 1.03 M ha (i.e., ∼50%) of total rice-fallow areas of 2.1 M ha were suitable for growing short duration green gram and black gram in the dry season. ConclusionsWe conclude that system productivity and cropping intensity can be increased by adoption of DSR and PTR-M in the wet season, and growing of green gram and black gram in the dry season. ImplicationsOdisha state can potentially produce an additional 0.67 million tonnes pulses if suitable rice-fallow areas are brought under green gram and black gram cultivation in the dry the season.

Rice-Fallow Targeting for Cropping Intensification through Geospatial Technologies in the Rice Belt of Northeast India

Rice-fallow areas have significant potential to sustainably increase agricultural intensification to address growing global food demands while simultaneously increasing farmers’ income by harnessing the residual soil moisture in rainfed ecologies. Assam is the largest rice-growing belt in northeast India during kharif; however, for the next rabi season, an average of 58% of the rice areas remain uncultivated and are described as rice-fallow (Kharif, rabi and zaid are the crop seasons in the study area. The kharif season refers to the monsoon/rainy season and corresponds to the major crop season in the region extending from June to October. The rabi season refers to the winter season extending from November to April, and the zaid season is the summer crop season from April to June). Unutilized rice-fallow areas with optimum soil moisture for a second crop were identified over three consecutive years using multiple satellite data (optical and radar) for the state of Assam and an average accuracy of 92.6%. The reasons governing the existence of rice-fallow areas were analyzed, and an average of 0.88 million ha of suitable rice-fallow areas, based on soil moisture availability, were identified. Targeted interventions were carried out in selected locations to demonstrate the potential of sustainable cropping intensification. Maize, with best management practices, and a yield between 5.5 and 6 t/ha, was demonstrated as a successful second crop during the rabi season in selected areas with optimum residual soil moisture after the kharif paddy harvest. This study highlights the significance of geospatial technology to effectively identify and target suitable rice-fallow areas for cropping intensification and to enhance productivity and profitability.

Quantifying irrigation water demand and supply gap using remote sensing and GIS in Multan, Pakistan.

Human interventions and rapid changes in land use adversely affect the adequate distribution of water resources. A research study was conducted to quantify the gap between demand and supply for irrigation water in Multan, Pakistan, which may lead to sustainable water management. Two remotely sensed images (Landsat 8 OLI and Landsat 5 TM) were downloaded for the years 2010 and 2020, and supervised classification method was performed for the selected land use land cover (LULC) classes and basic framework. During the evaluation, the kappa coefficient was found in the ranges of 0.83-0.85, and overall accuracy was found to be more than 80% which indicated a substantial agreement between the classified maps and the ground truth data for both years and seasons. The LULC maps showed that urbanization has increased by 49% during the last decade (2010-2020). Reduction in planting areas for wheat (9%), cotton (24%), and orchards (46%) was observed. An increase in planting areas for rice (92%) and sugarcane (63%) was observed. The changing LULC pattern may be related to variation in water demand and supply for irrigation. The irrigation water demand has decreased by 370.2 Mm3 from 2010 to 2020, due to the reduction in agricultural land and an increase in urbanization. Available irrigation water supply (canals/rainfall) was estimated as 2432 Mm3 for the year 2020 which was 26% less than that of total irrigation water demand (3281 Mm3). The findings also provide the database for sustainable water management and equitable distribution of water in the region.

MAPPING PADDY RICE EXTENT AND CROPPING PATTERN IN IADA BARAT LAUT SELANGOR USING INTEGRATION OF SENTINEL-1 AND 2 DATA

The Malaysian government, under the 12th Malaysia Plan (RMK-12), has set a goal to achieve a self-sufficient level (SSL) of 70% for rice production. Accurate and timely spatiotemporal information on harvested rice extent is required to measure progress towards achieving the SSL for rice. Remote sensing technology has been widely used to provide information on rice extent in large areas rapidly. The aim of this study is to create maps of rice extent and cropping patterns in the IADA Barat Laut Selangor (BLS) using a combination of Synthetic Aperture Radar (SAR) imagery data from Sentinel-1, optical imagery data at level-2A or Surface Reflectance (SR) from Sentinel-2, the unsupervised K-means clustering method, and the Google Earth Engine (GEE) cloud-based computing platform. The clustering results were then classified into rice and non-rice groups, and used to generate rice cropping patterns based on their representative profiles of temporal composite values of VH backscatter of Sentinel-1 data and NDVI of Sentinel-2 data. The accuracy of the map products was evaluated using a confusion matrix based on visual interpretation using very high-resolution imagery in Google Earth (GE) and compared with the existing data from IADA BLS. The rice extent map generated at 10-meter resolution exhibited excellent accuracy with an overall accuracy of 98% and a kappa coefficient of 0.95. The estimated rice parcel area in IADA BLS for 2021 was 17,864 ha, which is close to the existing data of 18,785 ha. The comparison of results in the irrigation block also indicated that the rice field area agreed well with the statistical data, with an R2 of 0.95, RMSE of 357 ha, and relative discrepancy of 4.9%. The cropping pattern also showed satisfactory results as compared to the existing data. These findings demonstrate that the proposed methodology can provide high-accuracy rice extent map products and has the potential to be applied to rice fields across Malaysia and other tropical regions to address food security issues.

Artificial intelligence-based approach to study the impact of climate change and human interventions on groundwater fluctuations

Abstract Water resource management is highly impacted by variations in rainfall, maximum and minimum temperature, and potential evapotranspiration. The rice area is also a key aspect for groundwater declination due to high-water consuming crop. Groundwater in Central Punjab has declined at an alarming rate over the last two decades. The decisions regarding water resource management need accurate information for the groundwater level. Therefore, to explore the main reason for the depletion of groundwater, it is essential that the most influential factors responsible for groundwater depletion should be addressed. A study was conducted in Central Punjab by using artificial neural network (ANN) and multiple linear regression (MLR) models during 1998–2018 to forecast the groundwater depth. ANN performed better than MLR. The sensitivity analysis showed that tubewell density, rice area, and rainfall are highly responsible for groundwater fluctuation.

Sample-free automated mapping of double-season rice in China using Sentinel-1 SAR imagery

Introduction: Timely and accurately mapping the spatial distribution of rice is of great significance for estimating crop yield, ensuring food security and freshwater resources, and studying climate change. Double-season rice is a dominant rice planting system in China, but it is challenging to map it from remote sensing data due to its complex temporal profiles that requires high-frequency observations.Methods: We used an automated rice mapping method based on the Synthetic Aperture Radar (SAR)-based Rice Mapping Index (SPRI), that requires no samples to identify double-season rice. We used the Sentinel-1 SAR time series data to capture the growth of rice from transplanting to maturity in 2018, and calculated the SPRI of each pixel by adaptive parameters using cloud-free Sentinel-2 imagery. We extensively evaluated the methods performance at pixel and regional scales.Results and discussion: The results showed that even without any training samples, SPRI was able to provide satisfactory classification results, with the average overall accuracy of early and late rice in the main producing provinces of 84.38% and 84.43%, respectively. The estimated area of double-season rice showed a good agreement with county-level agricultural census data. Our results showed that the SPRI method can be used to automatically map the distribution of rice with high accuracy at large scales.

Evaluation of Methane Emission Reduction Potential of Water Management and Chinese Milk Vetch Planting in Hunan Paddy Rice Fields

In order to explore the methane reduction potential with two scenarios of water management and Chinese Milk Vetch return, we calculated the methane emissions of Hunan Province rice fields in 2019 using the SECTOR tool based on Excel and released by the International Rice Research Institute. Thus, we preliminarily established an agricultural carbon emissions monitoring, reporting, and verification (MRV) system. The results showed that: (1) There was significant spatial variation in methane emissions in Hunan rice fields, with higher emissions in both the south and north and lower emissions in the east and west. Late rice was the main contributor to methane emissions, and the cities of Changde, Hengyang, Yueyang, and Shaoyang were high-emission areas due to differences in rice planting types and areas. Compared with flooding (1275.75 Gg), optimized water management measures (mid-drainage and AWD irrigation) reduced methane emissions by 29~45% (905.79 and 701.66 Gg, respectively). (2) Under the same nitrogen input conditions, compared with a solely straw return (375.24 Gg), combining green manure with straw return could partially reduce methane emissions from Hunan super hybrid rice (327.63 Gg). Compared with the control fertilizers (404.28 Gg), the reduction rates of winter-planted Chinese Milk Vetch, the return of rice straw, and the incorporation of both Chinese Milk Vetch and straw were 7.19%, 13.01%, and 18.96%, respectively. Based on scientific accounting tools, a preliminary MRV system for rice field carbon emissions was established. Under the national demand for reducing fertilizer use and increasing efficiency, equal nitrogen organic amendments could effectively contribute to the development of green, low-carbon, and high-quality agriculture.