Discrimination of irrigated and rainfed rice in a tropical agricultural system using SPOT VEGETATION NDVI and rainfall data

Abstract The classification of irrigated crops by remote sensing requires the use of time series data, since the timing, cropping intensity and duration of cropping is quite variable over the course of a year. Rice is the dominant irrigated crop in tropical and sub‐tropical Asia, where rainfall is high, but is seasonal and often uni‐modal. Existing crop classification methods for rice are not able to distinguish between rainfed and irrigated crops, leading to errors in classification and estimated irrigated area. This paper describes a technique, a 'peak detector algorithm', to successfully discriminate between rainfed and irrigated rice crops in Suphanburi province, Thailand. The methodology uses a three‐year time series of Satellite pour l'Observation de la Terre (SPOT) VEGETATION S10 Normalized Difference Vegetation Index (NDVI) data (10 day composites) to identify cropping intensity (number, timing and peak values). Peak NDVI is then lag‐correlated with long term average rainfall data. There is a high correlation at a 40–50 day lag, between a peak rainfall and a 'single' peak NDVI of rainfed rice. In irrigated areas, there are multiple peaks, and multiple correlations with low values for at least 90 days after peak rainfall. The methodology currently uses a mask to remove un‐cropped and non‐rice areas, which is derived from existing Geographical Information Systems (GIS). The method achieves a classification accuracy of 89% or better against independent groundtruth data. The procedure is designed as a second level of analysis to refine classifications using other techniques of mapping irrigated area at global and regional scales. Acknowledgments We are indebted to the Royal Thai Government for a scholarship granted to the first author for doctoral study in the Space Technology Applications and Research (STAR) programme, School of Advanced Technologies, Asian Institute of Technology. Thammasat University is also acknowledged for on‐leave permission to the first author. This research is partially funded by the Comprehensive Assessment, a programme of the International Water Management Institute. We also wish to thank people from the Office of Agricultural Economics, the Royal Irrigation Department and the Land Development Department for supplying data for this research.