Rice Crop Monitoring and Yield Estimation Through Cosmo Skymed and TerraSAR-X: A SAR-Based Experience in India

Abstract

ABSTRACT:Rice is the most important cereal crop governing food security in Asia. Reliable and regular information on the area under rice production is the basis of policy decisions related to imports, exports and prices which directly affect food security. Recent and planned launches of SAR sensors coupled with automated processing can provide sustainable solutions to the challenges on mapping and monitoring rice systems. High resolution (3m) Synthetic Aperture Radar (SAR) imageries were used to map and monitor rice growing areas in selected three sites in TamilNadu, India to determine rice cropping extent, track rice growth and estimate yields. A simple, robust, rule-based classification for mapping rice area with multi-temporal, X-band, HH polarized SAR imagery from COSMO Skymed and TerraSAR X and site specific parameters were used. The robustness of the approach is demonstrated on a very large dataset involving 30 images across 3 footprints obtained during 2013-14. A total of 318 in-season site visits were conducted across 60 monitoring locations for rice classification and 432 field observations were made for accuracy assessment. Rice area and Start of Season (SoS) maps were generated with classification accuracies ranging from 87-92 per cent. Using ORYZA2000, a weather driven process based crop growth simulation model; yield estimates were made with the inclusion of rice crop parameters derived from the remote sensing products viz., seasonal rice area, SoS and backscatter time series. Yield Simulation accuracy levels of 87 per cent at district level and 85-96 per cent at block level demonstrated the suitability of remote sensing products for policy decisions ensuring food security and reducing vulnerability of farmers in India.

Multi-Temporal Feature Extraction for Precise Maize Area Mapping Using Time-Series Sentinel 1a Sar Data

“…All the six-multi temporal features concisely characterized the key information in the maize signature. Hence, the values of multi-temporal features derived were used in classification of maize pixels in the study area and maize area map was generated which was in line with work reported by Pazhanivelan et al (2015).…”

“…The backscatter minimum was observed in the initial acquisition, indicating emerging condition of the crop and the backscatter increased in succeeding acquisitions indicating the growth of the crop. (Pazhanivelan et al, 2015) and groundnut (Deiveegan et al, 2017) under Tamil Nadu condition. The minimum of -21.26 and -14.05 under VH and VV polarization at emerge stage indicates the presence of favorable soil moisture conditions caused by sowing rains during D 1 to D 2 .…”

“…COSMO-SkyMed data are available from four X-band SAR satellites with 16 day revisit period for the same satellite with same incident angle. TerraSAR-X data is provided by one X-band SAR satellite with 11 day revisit period with same observation angle at strip map mode (3m resolution) with footprint of 30 x 50 km and ScanSAR mode (10m resolution) with footprint of 100 x 150 km (Pazhanivelan et al, 2015). With the latest launches Sentinel 1A and 1B data is available from European Space Agency (ESA) at C-band with the spatial resolution of 5m and 20m with a temporal resolution of 12 days individually and 6 days in combination.…”

Is there a market for multi-peril crop insurance in developing countries moving beyond subsidies? Evidence from India

“…This is a promising result, given that there have already been researchers and development practitioners working on the ground in India and other countries (largely in southeastern Asia) piloting remote sensing for rice yield prediction to eventually inform crop insurance programs. 5 The predictive accuracy of the remote sensing technologies has been rather encouraging, with predictive accuracy ranging between 85 percent and 96 percent across three sites in Tamil Nadu, India when predictions were made at the block (sub-district administrative unit), compared with accuracy of 87 percent when predictions were made at the district level (Pazhanivelan et al, 2015). Other recent research has found remote sensing yield prediction accuracy in rice as high as 95 percent in China (Huang et al, 2013).…”

AREA ESTIMATION OF COTTON AND MAIZE CROPS IN PERAMBALUR DISTRICT OF TAMIL NADU USING MULTI DATE SENTINEL-1A SAR DATA & OPTICAL DATA

“…Remote Sensing plays a very important role in achieving the United Nations Sustainable Development Goal 2: Zero Hunger. Crop monitoring provides timely information about the crop conditions at various stages and helps to estimate area and yield at national and even village levels (Pazhanivelan et al, 2015). Yield information is very useful in checking the price fluctuations of food crops and monitoring the food reserves.…”