Synergistic Use of Radar and Optical Satellite Data for Improved Monsoon Cropland Mapping in India

Abdul Qadir,Pinki Mondal

doi:10.3390/rs12030522

Abstract

Monsoon crops play a critical role in Indian agriculture, hence, monitoring these crops is vital for supporting economic growth and food security for the country. However, monitoring these crops is challenging due to limited availability of optical satellite data due to cloud cover during crop growth stages, landscape heterogeneity, and small field sizes. In this paper, our objective is to develop a robust methodology for high-resolution (10 m) monsoon cropland mapping appropriate for different agro-ecological regions (AER) in India. We adapted a synergistic approach of combining Sentinel-1 Synthetic Aperture Radar (SAR) data with Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 optical data using the Google Earth Engine platform. We developed a new technique, Radar Optical cross Masking (ROM), for separating cropland from non-cropland by masking out forest, plantation, and other non-dynamic features. The methodology was tested for five different AERs in India, representing a wide diversity in agriculture, soil, and climatic variations. Our findings indicate that the overall accuracy obtained by using the SAR-only approach is 90%, whereas that of the combined approach is 93%. Our proposed methodology is particularly effective in regions with cropland mixed with tree plantation/mixed forest, typical of smallholder dominated tropical countries. The proposed agriculture mask, ROM, has high potential to support the global agriculture monitoring missions of Geo Global Agriculture Monitoring (GEOGLAM) and Sentinel-2 for Agriculture (S2Agri) project for constructing a dynamic monsoon cropland mask.

Highlights

India is a primarily agrarian economy with 17% of the national Gross Domestic Product (GDP) contributed by agriculture and approximately 50% of the population supported by agricultural activities [1]
The details regarding the agro-ecological regions (AER) considered for this study and the major monsoon crops grown according to the latest government statistics available are listed in Table 1 [61]
We further calculated the F-score to determine the degree of discrimination among the five land use land cover (LULC) classes obtained from the S1-derived classification and the binary crop vs non-crop classification obtained from S1 only and S1+S2 combined derived classification

Summary

Introduction

India is a primarily agrarian economy with 17% of the national Gross Domestic Product (GDP) contributed by agriculture and approximately 50% of the population supported by agricultural activities [1]. The importance of the rainfed monsoon crop can be gauged from the fact that it contributes to 40% of the country’s food production [7]. During the rainfed monsoon crop growing season, both water intensive and dryland crops are grown throughout the country. Monsoon crop statistics are documented by different government agencies through agriculture census across India [10,11]. These crop statistics are typically aspatial and are collected by sample surveys at either the national or state level [12]. These surveys are expensive, time consuming, and labor intensive. And largely bias-free monsoon cropland statistics as a whole, instead of focusing only on major crops at a spatial scale finer than the district level, could considerably improve targeted intervention for providing government welfare schemes at the village or farm level [14]

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Discussion

Conclusion