Revealing the error components of satellite-only precipitation products (SPPs) can help algorithm developers and end-users understand their error features and improve retrieval algorithms. Here, two error decomposition schemes are employed to explore the error components of the IMERG-Late, GSMaP-MVK, and PERSIANN-CCS SPPs over different seasons, rainfall intensities, and topography classes. Global maps of the total bias (total mean squared error) and its three (two) independent components are depicted for the first time. The evaluation results for similar regions are discussed, and it is found that the evaluation results for one region cannot be extended to another similar region. Hit and/or false biases are the major components of the total bias in most overland regions globally. The systematic error contributes less than 20 % of the total error in most areas. Large systematic errors are primarily due to missed precipitation. It is found that the SPPs show different topographic patterns in terms of systematic and random errors. Notably, among the SPPs, GSMaP-MVK shows the strongest topographic dependency of the four bias scores. A novel metric, namely the normalized error component (NEC), is proposed as a means to isolate the impact of topography on the systematic and random errors. Potential methods of improving satellite precipitation retrievals and error adjustment models are discussed.

High-accuracy near-real-time satellite precipitation estimates (SPEs) provide an opportunity for hydrometeorologists to improve the forecasting of extreme events, such as flood, landslide, tropical cyclone and other extreme events, at the large scale. However, the currently operational near-real-time SPEs still have larger errors and uncertainties. In this study, we found that there exists a clear relationship of spatial plane function (SPF) between retrieval errors of SPEs and four crucial factors including topography, seasonality, climate type and rain rate. Based on this finding, we proposed a novel error adjustment method to correct the near-real-time hourly Global Satellite Mapping of Precipitation (GSMaP-NRT) estimates in real time. The new satellite precipitation dataset, namely ILSF-RT, was then inter-compared with the latest near-real-time GSMaP product suite (i.e., GSMaP-NRT and GSMaP-Gauge-NRT). Verification results show that the proposed method can effectively reduce the retrieval errors of GSMaP-NRT for various terrains and rain rates over different seasons and climate type areas. The new ILSF-RT even exhibits a general improvement over the GSMaP-Gauge-NRT estimates. Furthermore, one important merit of the new method is that it can perform rather well in validation even not much historical data were applied as training samples in calibration, for example, during the generation of ILSF-RT, only 45 data pairs of satellite retrievals and ground observations were used for winter season over Chinese arid areas. However, the results of bias score show that the current method seems unsuitable to adjust the rainfall events with higher rain rates (>= 1mm hr-1), which needs to be further improved.

Forests serve as vital carbon reservoirs, reducing atmospheric CO2 and mitigating climate change. Monitoring carbon stocks typically combines ground-based data with satellite remote sensing, yet accuracy remains a challenge. This study analyzes Huize County, China, using GF-1 WFV and Landsat TM images and introduces the Quantitative Remote Sensing Transformer (QRS-Trs), which leverages style transfer and attention mechanisms to enhance carbon stock estimation as an image-to-image translation task. QRS-Trs demonstrates three advantages: (1) Swin-Pix2Pix effectively reduces inter-domain discrepancies caused by sensor and lighting variations while excelling in de-clouding, outperforming Pix2Pix. (2) It incorporates a median filter to eliminate anomalies and a mask module to exclude non-target areas, achieving MAE = 16.29 Mg/ha, RMSE = 29.38 Mg/ha, R² = 0.71, and SSIM = 0.75. (3) Applied to multi-year data, from 2005 to 2020, 44.04% of the area showed increased carbon stock, 10.22% decreased, and 45.74% remained unchanged. While QRS-Trs performs well, its generalization to diverse ecological conditions depends on high-quality training data. Nevertheless, this study provides a robust approach for high-resolution carbon stock estimation, contributing to improved forest carbon sink management.

In this work, a three-step algorithm for the tomographic reconstruction of ionospheric electron density (IED) is proposed. In the new algorithm, the Taylor series expansion of the multiplicative algebraic reconstruction technique is first introduced to perform an adaptive adjustment for the relaxation parameter vector. Second, the horizontal and vertical constraints are imposed on the tomography system aiming to reduce the dependence on the initial iteration values for those voxels without any rays traversing them. Third, the uniformed voxel size is replaced with the variable voxel size by adopting the unequal grid interval in the altitudinal direction. The feasibility and superiority of the three-step algorithm are validated by devising a numerical simulation scheme. Finally, the new algorithm is successfully used to obtain the 3-D IED distribution under magnetically quiet conditions. The comparisons of IED profiles and the statistics of the reconstruction error of different algorithms further validate the superiority of the three-step algorithm.

This study investigates the application of a U-Net deep learning model for real-time error correction of satellite precipitation products, using IMERG-Late data over mainland China during 2015–2017. The performance of U-Net is systematically compared with five widely used machine learning approaches, including Decision Tree Regressor, Gradient Boosting Decision Tree, Multilayer Perceptron, Random Forest, and Extreme Gradient Boosting. The results indicate that U-Net consistently improves overall precipitation accuracy and is particularly effective in reducing false precipitation events, outperforming the comparative methods across most evaluation metrics. However, a relatively lower probability of detection (POD) is observed for U-Net, which is primarily associated with over-correction effects that suppress weak or marginal precipitation signals. Despite this limitation, the overall performance demonstrates that the U-Net-based approach provides a robust and reliable solution for real-time error correction of satellite precipitation products, with clear potential for hydrological and meteorological applications.