Towards an Operational SAR-Based Rice Monitoring System in Asia: Examples from 13 Demonstration Sites across Asia in the RIICE Project

Abstract

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.

Exploitation of SAR and Optical Sentinel Data to Detect Rice Crop and Estimate Seasonal Dynamics of Leaf Area Index

“…The multi-temporal rule-based rice detection (MSRD) algorithm proposed by [44] for X-band HH SAR data was adapted to C-band VV/VH data and applied to the Sentinel-1A datasets over the three study areas. Processing has been carried out in MAPscape-RICE software.…”

Spatial Assessment of Heat Stress Impact on Rice Production in Two Districts of Andhra Pradesh, India

“…Further data analysis reveals that, in Nellore, relatively high production losses due to heat stress are more evident in blocks (administrative division below district level) with a medium-to-large total rice area (Figure 4). a Total rice area was estimated using rule-based algorithm processing of SAR data (Nelson et al, 2014). In this study, the SAR data used were from Sentinel S1A (provided by European Space Agency, ESA).…”

“…Heat stress-affected area per block (administrative division below district level, whereas district is administrative division below the state level) and estimated production penalty per block (bubble size) in Nellore district, Andhra Pradesh, India during 2017 rabi season. Total rice area was estimated using rule-based algorithm processing of SAR data [20]. Heat stress-impacted areas were assumed for rice subjected to high temperature during reproductive stage based on temperature trend during this rabi season in the area.…”

“…Transplanting date mapping was based on Synthetic Aperture Radar (SAR) data analysis [20]. The system detected transplanting date with Root Mean Squared Error, RMSE [21] of 14 days as compared to the in-situ ground verification data taken in West Godavari and Nellore districts.…”

Rapid Assessment of Crop Status: An Application of MODIS and SAR Data to Rice Areas in Leyte, Philippines Affected by Typhoon Haiyan

“…The study site is the northern part of Leyte, which was the most strongly affected by Typhoon Haiyan. Figure 4 shows the study site, the typhoon track and rice cultivated area during the 2013 wet season (July to December) (from [22]). The source and generation of the rice area map are described in Section 3.…”