Dry marginal agricultural land (DryMAL) potentially use as an alternative resource for crop production. DryMAL de-fined as land having low natural fertility due to its intrinsic properties and forming environmental factors. This study uses Sentinel-2A imagery to map the spatial extent, compare the result of the classification, and identify the change in DryMAL occupation. The area of study (461.9 km2) is part of Situbondo Regency and is located at the eastern part of East Java, In-donesia. Sentinel-2A image captured in dry-season of 2018 use for this study. Then, supervised image classification using a maximum likelihood algorithm use for image treatment and processing. Furthermore, 450 ground control points for train-ing areas collected during the field surveys. Five bands use in the classification process. The maps produced from the clas-sification process were then compared to the land-use map from the year 2000. The change in DryMAL occupation from 2000 to 2018 was calculated by comparing the classified and land-use map. Supervised classification yielded an overall accuracy of 95.8% and a kappa accuracy of 93.2%. The classification produced six (6) classes of land use: (1) forest, (2) pavement or built-up area, (3) irrigated paddy field, (4) non-irrigated rural area, (5) dry marginal land and (6) water body. Globally, during the last two decades, regional development led by the Regency occupied more DryMAL area for develop-ing plantation. The effort reduces the amount of non-irrigated and converting to the plantation, pavement areas, and irrigat-ed paddy-field.

Changes in land use as a result of human activities may generate the alteration of hydrometeorological disasters. Erosion, sedimentation, floods and landslides frequently occur in the Sanenrejo watershed (±292 km2), located in East Java, Indonesia. In this paper, the soil and water assessment tool (SWAT) model is used to evaluate the hydrological processes in this small watershed. The digital elevation model (DEM) is used as the primary input for deriving the topographic and physical properties of the watershed. Other input data used for the modelling processes include soil type, land use, observed discharge data and climate variables. These data are integrated into the SWAT to calculate discharge, erosion and sedimentation processes. The existing observed discharge data used to calibrate the SWAT output at the watershed outlet. The calibration results produce Nash–Sutcliffe efficiency ( NSE) of 0.62 and determination coefficient (R2) of 0.75, then the validation result of 0.5 (NSE) and 0.63 (R2). The middle area faced the highest erosion and sedimentation that potentially contribute to hydrometeorological disasters.