Sentinel-1 Backscatter Analysis of Ratoon Rice Crops: Example from Ratooning Practice in the Philippines

Ratooning is a common rice crop management practice where the plant is left to regrow from post-harvest stubble, providing a low-input second crop. There is rising interest and use of rice ratooning in Asia to increase productivity on the same amount of land hence an accurate ratoon rice detection is important for monitoring rice production and productivity. Synthetic Aperture Radar (SAR) time series have been widely used for rice crop monitoring but there is little research on detecting ratoon rice practice in rice cropping systems. Hence, this study aims to (1) investigate the temporal SAR backscatter signatures of ratoon rice crops compared to those of the main rice crop and (2) determine if the ratoon rice signature is consistent in irrigated and rainfed rice systems. Farmers’ interviews and field surveys were conducted in four provinces of the Philippines, where rice ratooning was reported in the dry, wet, and very wet growing seasons of 2018-19. Four bands of backscatter information (VV, VH, VH/VV, and the radar vegetation index (RVI)) were obtained from the multi-temporal Sentinel-1A and B data with a six-day repeat cycle. We determined which band and which period of the season showed significant differences between the main rice and ratoon rice crops. Our results show that ratoon rice significantly differed from the main rice crop during the peak of the growing season in the VH, VH/VV, and RVI bands. We also found that the signature of ratoon rice was the same (no significant difference) for irrigated and rainfed rice systems. These findings suggest that Sentinel-1 time series data is suitable for detecting ratoon rice in lowland irrigated and rainfed rice systems. Given the increased interest in rice ratooning, detecting ratoon rice and its expansion is important for monitoring rice management practices and rice production

Understanding the effects of bacterial leaf blight disease on rice spectral signature

Bacterial leaf blight (BLB) caused by Xanthomonas oryzae pv oryzae is considered as one of the most economically important diseases in tropical and temperate rice-growing areas. To manage the disease before reaching damaging levels, it is important to detect its occurrence and assess its intensity in large areas in timely and efficient manner. Traditional disease detection and assessment rely on manual observations in the field, which is often time-consuming, costly, and dependent on the ability of data collectors to accurately identify the disease. Remote sensing is an emerging technology for plant disease detection at different spatial scales. Remote sensing-based disease detection can provide valuable information for operational applications, such as designing low-cost agricultural cameras, mapping disease epidemics at different administrative levels, and estimating yield losses. Previous studies have revealed several spectral changes at the canopy level in BLB-infected rice plants but have focused only on a single development stage, such as the grain filling and maturity stages, which limits the ability to use remote sensing for early disease detection. To address this knowledge gap, a field experiment was conducted in the Philippines during the wet season of 2023, when conditions are favorable to BLB. IR24, a highly susceptible variety, was grown in the field. Hyperspectral signals were measured at the canopy level of inoculated and non-inoculated (control) plots from tillering to heading stages. We will present the spectral bands that could discriminate between BLB-infected and healthy plants and evaluate the separability of these spectral bands to identify the most sensitive bands at each development stage. The results from this study can support early detection and monitoring of BLB andprevent yield loss

Discriminating transplanted and direct seeded rice using Sentinel-1 intensity data

Improved rice crop and water management practices that make the sustainable use of resources more efficient are important interventions towards a more food secure future. A remote sensing-based detection of different rice crop management practices, such as crop establishment method (transplanting or direct seeding), can provide timely and cost-effective information on which practices are used as well as their spread and change over time as different management practices are adopted. Establishment method cannot be easily observed since it is a rapid event, but it can be inferred from resulting observable differences in land surface characteristics (i.e. field condition) and crop development (i.e. delayed or prolonged stages) that take place over a longer time. To examine this, we used temporal information from Synthetic Aperture Radar (SAR) backscatter to detect differences in field condition and rice growth, then related those to crop establishment practices in Nueva Ecija (Philippines). Specifically, multi-temporal, dual-polarised, C-band backscatter data at 20m spatial resolution was acquired from Sentinel-1A every 12 days over the study area during the dry season, from November 2016 to May 2017. Farmer surveys and field observations were conducted in four selected municipalities across the study area in 2017, providing information on field boundaries and crop management practices for 61 fields. Mean backscatter values were generated per rice field per SAR acquisition date. We matched the SAR acquisition dates withthe reported dates for land management activities and with the estimated dates for when the crop growth stages occurred. The Mann-Whitney U test was used to identify significant differences in backscatter between the two practices during the land management activities and crop growth stages. Significant differences in cross-polarised, co-polarised and band ratio backscatter values were observed in the early growing season, specifically during land preparation, crop establishment, rice tillering and stem elongation. These findings indicate thepossibility to discriminate crop establishment methods by SAR at those stages, suggesting that there is more opportunity for discrimination than has been presented in previous studies. Further testing in a wider range of environments, seasons, and management practices should be done to determine how reliably rice establishment methods can be detected. The increased use of dry and wet direct seeding has implications for many remote sensing-based rice detection methods that rely on a strong water signal (typical of transplanting) during the early season

Spectral Signature Generalization and Expansion Can Improve the Accuracy of Satellite Image Classification

Conventional supervised classification of satellite images uses a single multi-band image and coincident ground observations to construct spectral signatures of land cover classes. We compared this approach with three alternatives that derive signatures from multiple images and time periods: (1) signature generalization: spectral signatures are derived from multiple images within one season, but perhaps from different years; (2) signature expansion: spectral signatures are created with data from images acquired during different seasons of the same year; and (3) combinations of expansion and generalization. Using data for northern Laos, we assessed the quality of these different signatures to (a) classify the images used to derive the signature, and (b) for use in temporal signature extension, i.e., applying a signature obtained from data of one or several years to images from other years. When applying signatures to the images they were derived from, signature expansion improved accuracy relative to the conventional method, and variability in accuracy declined markedly. In contrast, signature generalization did not improve classification. When applying signatures to images of other years (temporal extension), the conventional method, using a signature derived from a single image, resulted in very low classification accuracy. Signature expansion also performed poorly but multi-year signature generalization performed much better and this appears to be a promising approach in the temporal extension of spectral signatures for satellite image classification

Global Rice Atlas: Disaggregated seasonal crop calendar and production

Purpose: Rice is an important staple crop cultivated in more than 163 million ha globally. Although information on the distribution of global rice production is available by country and, at times, at subnational level, information on its distribution within a year is often lacking in different rice growing regions. Knowing when and where rice is planted and harvested and the associated production is crucial to policy and decision making on food security. To examine seasonal and geographic variations in food supply, we developed a detailed rice crop calendar and linked it with disaggregated production data. Approach and methods used: We compiled from various sources detailed data on rice production, and planting and harvesting dates by growing season. To standardize the production data to the same period, we adjusted the production values so that the totals for each country will be the same as those of FAO for 2010-2012. We then linked data on rice production with the corresponding crop calendar information to estimate production at harvest time by month then we calculated totals for each country and region. Key results: The bulk of global annual harvests of rice is from September to November, corresponding with the harvest of the wet season rice in Asia and Africa. Total rough rice production during those peak months exceed 381 million tons, which account for about half of annual global rice output. Production is lowest in January with only 11 million tons in total. Regional production is lowest in Asia in January, Americas in December, Africa in July and rest of the world in May. Synthesis and Applications: A globally complete and spatially detailed rice crop calendar is important to crop growth simulation modelling and assessment of vulnerability of rice areas to biotic and abiotic stresses. Linked to production estimates, it can be used in analyzing spatial and seasonal production trends to better assess and predict price fluctuations , and to mitigate potential significant shortfalls in food production at certain times of the year

Big data, small explanatory and predictive power: Lessons from random forest modeling of on-farm yield variability and implications for data-driven agronomy

Context Collection and analysis of large volumes of on-farm production data are widely seen as key to understanding yield variability among farmers and improving resource-use efficiency. Objective The aim of this study was to assess the performance of statistical and machine learning methods to explain and predict crop yield across thousands of farmers’ fields in contrasting farming systems worldwide. Methods A large database of 10,940 field-year combinations from three countries in different stages of agricultural intensification was analyzed. Random effects models were used to partition crop yield variability and random forest models were used to explain and predict crop yield within a cross-validation scheme with data re-sampling over space and time. Results Yield variability in relative terms was smallest for wheat and barley in the Netherlands and for wheat in Ethiopia, intermediate for rice in the Philippines, and greatest for maize in Ethiopia. Random forest models comprising a total of 87 variables explained a maximum of 65 % of cereal yield variability in the Netherlands and less than 45 % of cereal yield variability in Ethiopia and in the Philippines. Crop management related variables were important to explain and predict cereal yields in Ethiopia, while predictive (i.e., known before the growing season) climatic variables and explanatory (i.e., known during or after the growing season) climatic variables were most important to explain and predict cereal yield variability in the Philippines and in the Netherlands, respectively. Finally, model cross-validation for regions or years not seen during model training reduced the R2 considerably for most crop x country combinations, while for wheat in the Netherlands this was model dependent. Conclusion Big data from farmers’ fields is useful to explain on-farm yield variability to some extent, but not to predict it across time and space. Significance The results call for moderate expectations towards big data and machine learning in agronomic studies, particularly for smallholder farms in the tropics where model performance was poorest independently of the variables considered and the cross-validation scheme used

Helping feed the world with rice innovations: CGIAR research adoption and socioeconomic impact on farmers

Rice production has increased significantly with the efforts of international research centers and national governments in the past five decades. Nonetheless, productivity improvement still needs to accelerate in the coming years to feed the growing population that depends on rice for calories and nutrients. This challenge is compounded by the increasing scarcity of natural resources such as water and farmland. This article reviews 17 ex-post impact assessment studies published from 2016 to 2021 on rice varieties, agronomic practices, institutional arrangements, information and communication technologies, and post-harvest technologies used by rice farmers. From the review of these selected studies, we found that stress-tolerant varieties in Asia and Africa significantly increased rice yield and income. Additionally, institutional innovations, training, and natural resource management practices, such as direct-seeded rice, rodent control, and iron-toxicity removal, have had a considerable positive effect on smallholder rice farmers’ economic well-being (income and rice yield). Additional positive impacts are expected from the important uptake of stress-tolerant varieties documented in several Asian, Latin American, and African countries

Southeast Asia must narrow down the yield gap to continue to be a major rice bowl

Southeast Asia is a major rice-producing region with a high level of internal consumption and accounting for 40% of global rice exports. Limited land resources, climate change and yield stagnation during recent years have once again raised concerns about the capacity of the region to remain as a large net exporter. Here we use a modelling approach to map rice yield gaps and assess production potential and net exports by 2040. We find that the average yield gap represents 48% of the yield potential estimate for the region, but there are substantial differences among countries. Exploitable yield gaps are relatively large in Cambodia, Myanmar, Philippines and Thailand but comparably smaller in Indonesia and Vietnam. Continuation of current yield trends will not allow Indonesia and Philippines to meet their domestic rice demand. In contrast, closing the exploitable yield gap by half would drastically reduce the need for rice imports with an aggregated annual rice surplus of 54 million tons available for export. Our study provides insights for increasing regional production on existing cropland by narrowing existing yield gaps