LAUSR

Abstract This study investigates the robustness of an advanced classification algorithm to spatially map heterogeneous, fragmented croplands using multi-temporal synthetic aperture radar (SAR) datasets. Four parameters derived from Sentinel-1 (backscatters in dual-polarization: σ V H o , σ V V o , cross-ratio ( σ V H o σ V V o ) , and radar vegetation index (RVI)) were considered to develop temporal patterns and correlate with un-classified time-series satellite imagery using time-weighted dynamic time warping (TWDTW) algorithm. Pixel and parcel based classifications were considered to identify four crop varieties (paddy, sugarcane, cotton and vegetables) subjected to two water limiting conditions (low stress-LS, high stress-HS). In-situ data were split-sampled (30:70 ratio) between training (to develop temporal patterns) and testing (to validate classification output). Overall accuracy of pixel and parcel based classifications were 63% and 76% with a Kappa coefficient of 0.58 and 0.73 respectively. In conclusion, parcel based TWDTW algorithm conditioned by temporal signatures of RVI has effectively delineated croplands with varying irrigation treatments for yield and damage assessment modeling studies.