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Rice is one of the major crops cultivated predominantly in flooded paddies, thus a large amount of water is consumed during its growing season. Accurate paddy rice maps are therefore important inputs for improved estimates of actual evapotranspiration in the agricultural landscape. The main objective of this study was to obtain flooded paddy rice maps using multi-temporal images of Moderate Resolution Imaging Spectroradiometer (MODIS) in the Krishna River Basin, India. First, ground-based spectral samples collected by a field spectroradiometer, CROPSCAN, were used to demonstrate unique contrasts between the Normalized Difference Vegetation Index (NDVI) and the Land Surface Water Index (LSWI) observed during the transplanting season of rice. The contrast between Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) from MODIS time series data was then used to generate classification decision rules to map flooded rice paddies, for the transplanting seasons of Kharif and Rabi rice crops in the Krishna River Basin. Consistent with ground spectral observations, the relationship of the MODIS EVI vs. LSWI of paddy rice fields showed distinct features from other crops during the transplanting seasons. The MODIS-derived maps were validated against extensive reference data collected from multiple land use field surveys. The accuracy of the paddy rice maps, when determined using field plot data, was approximately 78%. The MODIS-derived rice crop areas were also compared with the areas reported by Department of Agriculture (DOA), Government of India (Government Statistics). The estimated root mean square difference (RMSD) of rice area estimated using MODIS and those reported by the Department of Agriculture over 10 districts varied between 3.4% and 6.6% during 10 years of our study period. Some of the major factors responsible for this difference include high noise of the MODIS images during the prolonged monsoon seasons (typically June-October) and the coarse spatial resolution (500 m) of the MODIS images compared to the small crop fields in the basin. However, this study demonstrates, based on multi-year analysis, that MODIS images can still provide robust and consistent flooded paddy rice extent and areas over a highly heterogeneous large river basin. [ABSTRACT FROM AUTHOR]

[Display omitted] • In asynchronous climates, the forest growth response to rainfall is not well known. • Lagged relationship of forest growth to antecedent rainfall was explored. • Vegetation in the asynchronous climate have a long memory to the rainfall. • The memory of vegetation to rain could indicate soil/rooting depth across the landscape. The out-of-phase rainfall and temperature and deep root system make the sequential connection between past rainfall events, soil water storage, and forest growth response complicated and temporally extended in asynchronous climates with Mediterranean-type settings. Unfortunately, these location-specific deep-soil water stores are rarely measured due to logistic and financial constraints, especially in the forest. Therefore, at a large spatio-temporal scale, forest growth relationship to growth drivers is still unknown in these ecosystems, limiting our knowledge to understand the functioning of these forests and their links with hydrological processes. Although process-based water balance models can analyze vegetation growth response to the input climate forcing, they rely upon some significant assumptions regarding plants access to deep soil water storage. Thus, this study aims to understand how the out-of-phase rainfall events affect the current ecosystem growth response, represented by the observed Normalized Difference Vegetation Index (NDVI), across the landscape. We have used an empirical approach on long term observed data without any assumption on access to deep-soil water stores. We estimated time lags between forest growth and rainfall events using a lagged correlation analysis applied to monthly anomalies of NDVI and rainfall against their climatological averages over 2002–2018. The study found that the forests in asynchronous climates exhibit unexpectedly long (10–25 months) memory to rain, and this memory has a systematic pattern across the landscape, which we contend has highlighted three things: 1) the forest in the middle aridity (∼3–4.5) range are relatively more sensitive to changes in the short-term rainfall than the forest in lower (<3) and higher (>5) aridity regions, could be due to rapid depletion of relatively small soil water storage in between the storms, 2) the variable memory of forest to rain across the landscape can be an indicator of soil depth/rooting depth, and 3) the variable sized location specific antecedent rainfall windows can explain significant variability in forest growth status in asynchronous climates, thus these rainfall windows can be employed to forecast forest growth with a lead time (>4 months). [ABSTRACT FROM AUTHOR]