Rice is the most important food security crop in Asia. Information on its seasonal extent forms part of the national accounting of many Asian countries. Synthetic Aperture Radar (SAR) imagery is highly suitable for detecting lowland rice, especially in tropical and subtropical regions, where pervasive cloud cover in the rainy seasons precludes the use of optical imagery. Here, we present a simple, robust, rule-based classification for mapping rice area with regularly acquired, multi-temporal, X-band, HH-polarized SAR imagery and site-specific parameters for classification. The rules for rice detection are based on the well-studied temporal signature of rice from SAR backscatter and its relationship with crop stages. We also present a procedure for estimating the parameters based on "temporal feature descriptors" that concisely characterize the key information in the rice signatures in monitored field locations within each site. We demonstrate the robustness of the approach on a very large dataset. A total of 127 images across 13 footprints in six countries in Asia were obtained between October 2012, and April 2014, covering 4.78 m ha. More than 1900 in-season site visits were conducted across 228 monitoring locations in the footprints for classification purposes, and more than 1300 field observations were made for accuracy assessment. Some 1.6 m ha of rice were mapped with classification accuracies from 85% to 95% based on the parameters that were closely related to the observed temporal feature descriptors derived for Remote Sens. 2014, 6 10775 each site. The 13 sites capture much of the diversity in water management, crop establishment and maturity in South and Southeast Asia. The study demonstrates the feasibility of rice detection at the national scale using multi-temporal SAR imagery with robust classification methods and parameters that are based on the knowledge of the temporal dynamics of the rice crop. We highlight the need for the development of an open-access library of temporal signatures, further investigation into temporal feature descriptors and better ancillary data to reduce the risk of misclassification with surfaces that have temporal backscatter dynamics similar to those of rice. We conclude with observations on the need to define appropriate SAR acquisition plans to support policies and decisions related to food security.

No abstract

Asian countries strongly depend on rice production for food security. The major rice-growing season (June to October) is highly exposed to the risk of tropical storm related damage. Unbiased and transparent approaches to assess the risk of rice crop damage are essential to support mitigation and disaster response strategies in the region. This study describes and demonstrates a method for rapid, pre-event crop status assessment. The ex-post test case is Typhoon Haiyan and its impact on the rice crop in Leyte Province in the Philippines. A synthetic aperture radar (SAR) derived rice area map was used to delineate the area at risk while crop status at the moment of typhoon landfall was estimated from specific time series analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) data. A spatially explicit indicator of risk of standing crop loss was calculated as the time OPEN ACCESSRemote Sens. 2015, 7 6536 between estimated heading date and typhoon occurrence. Results of the analysis of pre-and post-event SAR images showed that 6500 ha were flooded in northeastern Leyte. This area was also the region most at risk to storm related crop damage due to late establishment of rice. Estimates highlight that about 700 ha of rice (71% of which was in northeastern Leyte) had not reached maturity at the time of the typhoon event and a further 8400 ha (84% of which was in northeastern Leyte) were likely to be not yet harvested. We demonstrated that the proposed approach can provide pre-event, in-season information on the status of rice and other field crops and the risk of damage posed by tropical storms.

Abstract. Knowing where and when rice is grown is essential for planning and decision-making in relation to food security, as well as in research wherein crop area and calendar are important inputs in crop production simulations, assessment of biotic and abiotic stresses, and analysis of the effect of climate change on crop production, among others. Remote sensing allows for efficient mapping and characterization of rice areas. In this study, we derived the rice planting window in all rice growing regions in the Philippines from 2016 to 2018 using multi-temporal Synthetic Aperture Radar (SAR), specifically TerraSAR-X and Sentinel-1. Using a rule-based method, rice area and Start of Season (SoS) were mapped based on the unique backscatter behaviour of rice corresponding to the initial deliberate agronomic flooding followed by rapid biomass increase. We defined the planting window per year and semester as the 15th and 85th percentile and the peak of planting as the dominant planting date. The accuracy of the rice map was 93% and the SoS was strongly correlated with the actual planting dates reported by farmers (R2 = 0.71) based on 482 ground observations in the Philippines in 2018 Semester 1. From this analysis, the planting window in the Philippines for the Semester 2 (wet season) is April-August (peak in June-July), and for Semester 1 (dry season) is September-February (peak in November-December) with large differences across regions. In majority of the regions, the planting window spans more than 100 days, which can have implications on incidence of pests and diseases.

Occurrence of pests and diseases are influenced by several factors including weather, landscape and field-level factors such as crop management practices including crop establishment method. In this paper, we adopted and applied a method using Sentinel-1A (S-1A) Synthetic Aperture Radar (SAR) intensity to discriminate between rice fields that are transplanted and direct seeded to come up with a robust method for automated classification of crop establishment method. Multi-temporal S-1A C-band dual polarization images at 20m resolution covering the wet cropping season over four provinces in the Philippines were acquired from March to November 2018. Field measurements, observations and interviews were conducted on 186 sample fields and mean backscatter values for each of the sampled fields were generated from S-1A data acquired during the season. The reported dates of land preparation and estimated dates of crop growth stages were matched with the corresponding SAR acquisition dates. We used the Mann-Whitney U test to identify growth stages for which there are significant differences in backscatter values between transplanted and direct seeded rice. The results are generally consistent with the findings of a previous study conducted in one province in the Philippines in the dry season of 2017. We found, however, some inconsistencies in terms of the polarization where the significant differences were observed. These findings demonstrate the possibility of discriminating transplanted from direct seeded rice using SAR temporal data but suggests further fine tuning in the methodology is needed for different locations and seasons.