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Accurate monitoring of irrigated paddy field area and irrigation timing are of a great concern in agricultural water management due to the substantial consumption of fresh water when irrigating paddy fields. Spectral threshold methods (Xiao et al., 2005) have been widely applied to detect irrigated paddy fields and irrigation timing using Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI). These methods applied constant additive threshold values (T) to LSWI and compared it to EVI to detect the irrigated paddy fields. In this study, we developed Variable Threshold Models that utilized different pixel-based threshold values depending on sub-pixel land cover heterogeneity and hence, improve detection performance on distributed small-scale paddy fields. Non-irrigated sub-pixels were quantified with irrigation maps produced by Synthetic Aperture Radar (SAR) microwave images. Significant positive correlation between EVI and the sub-pixel numbers of non-irrigated area were found (r=0.87), which resulted in higher T for MODIS pixels with more non-irrigated sub-pixels. Accordingly, a Variable Threshold Model, i.e. a regression model between T and EVI, was developed. With the Variable Threshold Model, agreement rates between MODIS and SAR-based irrigated small-scale paddy field classification doubled compared with that from a fixed threshold value. In comparison with field observations, the Variable Threshold Models showed a mean error of +0.9 days, an improvement over the mean error of +2.8 days from a fixed threshold model. Combined utilization of SAR and MODIS images provides a useful tool for developing a Variable Threshold Model that can enhance accurate monitoring of irrigation dates across heterogeneous paddy field regions.
