Landsat-8 imagery contains a time series thermal image from 2013 to the present, which could be utilized to identify changes in land surface temperature (LST). Google Earth Engine (GEE) has provided straightforward access to the Landsat-8 thermal time series image. The LST alterations in urban areas are influenced by urban physical development and the impact of climate change, which has a detrimental effect on environmental conditions. This leads to the Urban Heat Island (UHI) phenomenon. This research aims to ascertain the spatial distribution of the Urban Heat Island (UHI) phenomenon in Bogor City, Indonesia, utilizing Landsat-8 OLI/TIRS satellite imagery in Google Earth Engine (GEE). Landsat-8 OLI/TIRS images were captured between 2013 and 2023. The variable that influences UHI is Land Surface Temperature (LST), with the impact of vegetation and building density. UHI was analyzed by comparing the temperature of the city with that of the surrounding area. LST was extracted from Landsat-8 thermal imagery in band 10 using the mono-window algorithm in GEE. Vegetation density and building density were extracted from remote sensing indices, specifically the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Building Index (NDBI) through GEE. A correlation analysis was performed between LST, NDVI, and NDBI. The phenomenon of urban heat island (UHI) is evident in Bogor city, where the temperature is 5-10 °C higher than in the surrounding area. The expansion of the high-temperature area has been observed in 2018 and 2023, resulting in a larger heat island than that observed in 2013. The heat island is not limited to the city center but has also been observed in the outer city, particularly in the northeast area. Over the past 10 years, the temperature in this area has increased by 2.4 °C. The surface temperature exhibits a strong correlation with vegetation and building conditions, with a correlation coefficient exceeding 0.8.

Intergovernmental Panel on Climate Change (IPCC) guidelines have been standardized and widely used to calculate methane (CH₄) emissions from paddy fields. The emission factor (EF) is a key parameter in these guidelines, and it is different for each location globally and regionally. However, limited studies have been conducted to measure locally specific EFs (EFlocal) through on-site assessments and modeling their spatial distribution effectively. This study aims to investigate the potential of multisensor satellite data to develop a spatial model of CH₄ emission estimation on rice paddy fields under different water management practices, i.e., continuous flooding (CF) and alternate wetting and drying (AWD) in Subang, West Java, Indonesia. The model employed the national EF (EFnational) and EFlocal using the IPCC guidelines. In this study, we employed the multisensor satellite data to derive the key parameters for estimating CH₄ emission, i.e., rice cultivation area, rice age, and EF. Optical high-resolution images were used to delineate the rice cultivation area, Sentinel-1 SAR imagery was used for identifying transplanting and harvesting dates for rice age estimation, and ALOS-2/PALSAR-2 was used to map the water regime for determining the scaling factor of the EF. The closed-chamber method has been used to measure the daily CH₄ flux rate on the local sites. The results revealed spatial variability in CH₄ emissions, ranging from 1–5 kg/crop/season to 20–30 kg/crop/season, depending on the water regime. Fields under CF exhibited higher CH₄ emissions than those under AWD, underscoring the critical role of water management in mitigating CH₄ emissions. This study demonstrates the feasibility of combining remote sensing data with the IPCC model to spatially estimate CH₄ emissions, providing a robust framework for sustainable rice cultivation and greenhouse gas (GHG) mitigation strategies.

Water-level monitoring in rice paddies supports sustainable farming, responsible water management, and greenhouse gas emission mitigation. SAR-based remote sensing is an effective alternative for estimating water levels, especially in regions where optical observations are limited. This study evaluates ten ALOS-2/PALSAR-2 L-band SAR-derived polarimetric parameters for their contribution and effectiveness in water-level estimation across rice-growing phases using random forest regression in the Subang District, which is one of the largest rice-yield areas in West Java, Indonesia. Overall, L-band polarimetric information is clearly related to water-level dynamics throughout the rice-growing cycle, confirming its strong potential for quantitative water-level retrieval. The highest estimation accuracy was achieved by integrating all polarimetric parameter groups (MAE = 1.37 cm, RMSE = 1.79 cm, R² = 0.52, r = 0.73), indicating that no single group can adequately represent the complex scattering mechanisms governing water-level variability across an entire cropping season. Variable importance analysis shows a relatively uniform contribution (7.63–12.90%), suggesting synergies across parameters in water-level estimation. Phase-specific evaluation further reveals that Phase 2, corresponding to the vegetative-to-generative transition, is the optimal temporal window for L-band SAR-based water-level retrieval due to enhanced double-bounce scattering and reduced signal saturation. While Phase 2 data maximizes physical sensitivity and correlation, whole-phase modeling provides greater robustness and lower absolute errors, making it more suitable for L-band SAR-based operational water-level monitoring applications.