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.

Nowadays, spectral index has become popular as a tool to identify fire-burned areas. However, the use of a single index may not be universally applicable to region with diverse landscape and vegetation as peatlands. Here, we propose to develop a procedure that integrates multiple spectral indices with an adaptive thresholding method to enhance the performance of burned area detection. We combined the Normalized Difference Vegetation Index (NDVI) and the Normalized Burn Ratio (NBR) using MODIS imagery from 2002 to 2022 to calculate (Confirmed Burned Pixel) by filtering dNDVI and dNBR. The mean and standard deviation of serve as inputs for image thresholding. We tested our approach in Sebangau peatland, Central Kalimantan, where fires occur annually. The results showed that the model performed well with overall accuracy > of 91%, indicating that the model is effective and reliable for identifying burned areas. The findings also revealed that the frequency of fire is below 2 times/year, with the southeastern is the most fire prone regions. Further, our findings provide an alternative approach for identifying burned areas in locations with diverse vegetation cover and different geographical regions.

In recent years, use of the stochastic model has been growing due to the high complexity and dynamics of the atmosphere, especially the rainfall process. Various concepts have been applied to rainfall modeling, ranging from simplistic approaches to more complex models. It is important to understand different stochastic rainfall modeling approaches, as well as their advantages and limitations. This paper determines the development of the latest stochastic rainfall models in the Asia region, where different concepts of stochastic rainfall models were highlighted. It reviews the different methodologies used, including rainfall forecasting, spatio-temporal analysis, and extreme event. We select 30 articles from 1571 literature published between 2013–2022 from the Scopus database. The results show that the stochastic models often used in the literature consist of Markov chain, weather generator, probability distribution, ARIMA, and the Bayesian model. In the recent development in Asia, stochastic models in rainfall modeling research are widely used to generate the occurrence and amount of rainfall data, statistical downscaling, future rainfall trends, and estimation of extreme values. The difference in spatio-temporal, climate conditions, and the parameters model can cause the performance of each model to be different.