MODIS NDVI Modified Z-score for Evaluating Drought Incidence of Rice Areas in the Mekong Delta

Extreme climate events such as flood, drought, and high temperature are expected to increase in frequency and intensity with climate change. Mapping and characterization of food production areas at risk can help in better targeting innovations and in enhancing the resilience of affected communities. In this study, we used two decades of the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI)[1][2] from 2003 to 2022 to map drought incidence in rice areas in the Mekong Delta, a densely populated region and an important source of rice for domestic and export markets

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

PhenoRice:A method for automatic extraction of spatio-temporal information on rice crops using satellite data time series

Agricultural monitoring systems require spatio-temporal information on widely cultivated staple crops like rice. More emphasis has been made on area estimation and crop detection than on the temporal aspects of crop cultivation, but seasonal and temporal information such as i) crop duration, ii) date of crop establishment and iii) cropping intensity are as important as area for understanding crop production. Rice cropping systems are diverse because genetic, environmental and management factors (G × E × M combinations) influence the spatio-temporal patterns of cultivation. We present a rule based algorithm called PhenoRice for automatic extraction of temporal information on the rice crop using moderate resolution hypertemporal optical imagery from MODIS. Performance of PhenoRice against spatially and temporally explicit reference information was tested in three diverse sites: rice-fallow (Italy), rice-other crop (India) and rice-rice (Philippines) systems. Regional product accuracy assessments showed that PhenoRice made a conservative, spatially representative and robust detection of rice cultivation in all sites (r2 between 0.75 and 0.92) and crop establishment dates were in close agreement with the reference data (r2 = 0.98, Mean Error = 4.07 days, Mean Absolute Error = 9.95 days, p < 0.01). Variability in algorithm performance in different conditions in each site (irrigated vs rainfed, direct seeding vs transplanting, fragmented vs clustered rice landscapes and the impact of cloud contamination) was analysed and discussed. Analysis of the maps revealed that cropping intensity and season length per site matched well with local information on agro-practices and cultivated varieties. The results show that PhenoRice is robust for deriving essential temporal descriptions of rice systems in both temperate and tropical regions at a level of spatial and temporal detail that is suitable for regional crop monitoring on a seasonal basis

Monitoring, reporting, and verification system for rice production aligned with Paris Agreement transparency guidelines

This critical review takes a novel approach to assessing the existing Monitoring, Reporting and Verification (MRV) methodology and tools and provides expert-based recommendations for adjusted MRV standards that adapt current guidelines as a promising way forward to deliver transparency in meeting the Nationally Determined Contributions of Vietnam. Additionally, this is a timely proposition given the necessity to define an MRV framework for NAMAs for the rice sector. We are recommending a multi-pronged approach using several tools that can support and validate each other to achieve a robust mechanism for MRV estimations in the rice sector. Examples from the country will be used as a case study given their government’s strong commitment to mitigation in the rice sector

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

Mapping the suitability of selected crops in the Ganges Delta

Assessing the suitability of different crops in specific geographic locations is crucial for optimizing crop productivity, promoting climate resilience, and guiding land use decisions. This study assessed the biophysical suitability of rice, watermelon and maize in the Ganges Delta, one of the most densely populated deltas in the world and also extremely vulnerable to climate change. This delta is expected to increasingly experience more frequent and intense extreme weather events, sea level rise and food insecurity. The suitability maps could be used in targeting alternative cropping systems, adjusting crop calendar and recommending crop management practices to increase productivity and improve the resilience of the Ganges Delta