Drought monitoring is essential to detect the presence of drought, and the comprehensive change of drought conditions on a regional or global scale. This study used satellite precipitation data from the Tropical Rainfall Measuring Mission (TRMM), but refined the data for drought monitoring in Java, Indonesia. Firstly, drought analysis was conducted to establish the standardized precipitation index (SPI) of TRMM data for different durations. Time varying SPI spatial downscaling was conducted by selecting the environmental variables, normalized difference vegetation index (NDVI), and land surface temperature (LST) that were highly correlated with precipitation because meteorological drought was associated with vegetation and land drought. This study used time-dependent spatial regression to build the relation among original SPI, auxiliary variables, i.e., NDVI and LST. Results indicated that spatial downscaling was better than nonspatial downscaling (overall RMSEs: 0.25 and 0.46 in spatial and nonspatial downscaling). Spatial downscaling was more suitable for heterogeneous SPI, particularly in the transition time (R: 0.863 and 0.137 in June 2019 for spatial and nonspatial models). The fine resolution (1 km) SPI can be composed of the environmental data. The fine-resolution SPI captured a similar trend of the original SPI. Furthermore, the detailed SPI maps can be used to understand the spatio-temporal pattern of drought severity.

As the most populous island globally, Java Island has various vulnerabilities to disasters ranging from geological to hydro-meteorological. One of the most common hydro-meteorological disasters is the drought that occurs every year in the dry season. This disaster causes crop failure, land and forest fires, and clean water shortages. In this study, the Sea and Land Surface Temperature Radiometer (SLSTR) instrument onboard the Sentinel-3 platform was used to map drought using the Vegetation Temperature Condition Index (VTCI) algorithm based on the scattering space technique of Land Surface Temperature (LST) and Normalized Different Vegetation Index (NDVI). During 2018, the highest and lowest LST occurred on September 28 (309 oK), and February 1 (278 oK); the highest and lowest NDVI occurred on July 1 (0.466) and November 13 (0.221). In comparison, the driest conditions indicated the lowest VTCI (0.162) on July 17 and the wettest on August 28 (0.508). The driest and wettest situation co-occurred: at the end of the dry session and the rainy session, it shows that the rain greatly contributes to high VTCI. A regular drought mapping needs to be performed as an effort to disaster risk reduction. Drought maps are then used as a spatial recommendation in reforestation intervention to reduce drought in the future.

The Lumajang Urban Area (BWP) is the center of government and the economy in Lumajang Regency. The growth and development of cities in BWP Lumajang every year results in significant land changes. Thus, a map of land resources in Lumajang BWP is needed to determine the level of land use in the area. This research was conducted to make the Lumajang BWP Land Resource Map of Lumajang Regency using Geographic Information Systems (GIS). This study uses data processing methods with Object-Based Classification (OBIA). The data used for this study are the results of the Pleiades 1-A High-Resolution Satellite Image (CSRT) segmentation in 2016. The data used as an initial reserve (asset) is the 2009 RBI map. The data used for resource utilization (liability) is the result segmentation of Pleiades 1-A HighResolution Satellite Imagery (CSRT) in 2016. In processing OBIA using scale, shape, and compactness parameters. In this study, it can be seen that the scale parameters have the greatest role in the formation of OBIA segmentation. The smaller the scale value is given results in more accurate segmentation. The results of this study are that at a higher level of accuracy is the method of image segmentation method of 89,041% and the land cover that has the largest decrease in area is irrigated rice fields and the largest increase is a plantation.