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Satellite-based quantitative precipitation estimates (QPE) with a fine quality are of great importance to global water cycle and matter and energy exchange research. In this study, we firstly apply various statistical indicators to evaluate and compare the main current satellite-based precipitation products from Chinese Fengyun (FY)-2 and the Global Precipitation Measurement (GPM), respectively, over mainland China in summer, 2018. We find that (1) FY-2G QPE and Integrated Multi-satellitE Retrievals for GPM (IMERG) perform significantly better than FY-2E QPE, using rain gauge data, with correlation coefficients (CC) varying from 0.65 to 0.90, 0.80 to 0.90, and 0.40 to 0.53, respectively; (2) IMERG agrees well with rain gauge data at monthly scale, while it performs worse than FY-2G QPE at hourly and daily scales, which may be caused by its algorithms; (3) FY-2G QPE underestimates the precipitation in summer, while FY-2E QPE and IMERG generally overestimate the precipitation; (4) there is an interesting error phenomenon in that both FY-based and GPM-based precipitation products perform more poorly during the period from 06:00 to 10:00 UTC than other periods at diurnal scale; and (5) FY-2G QPE agrees well with IMERG in terms of spatial patterns and consistency (CC of~0.81). These findings can provide valuable preliminary references for improving next generation satellite-based QPE retrieval algorithms and instructions for applying these data in various practical fields.Collecting precipitation information from ground rain gauge stations is the traditional and common method of measurement. However, the limitations are obvious due to the uneven spatial distribution of the stations. The measurements of ground stations are usually very sparse over some regions of the earth (e.g., the Tibetan plateau), which are meteorologically important [6,7]. As for ground-based weather radars, they have certain superiorities when observing precipitation in local areas. Nevertheless, due to the limitations of the scope of observation and the huge cost of equipment acquisition and maintenance, ground-based weather radars are not the first choice for large-scale precipitation observations. However, precipitation information obtained from satellites does not meet such limitations. Satellite-based precipitation datasets can depict the spatial and temporal variability of precipitation with a considerable accuracy over regions that have few ground stations [5,8]. Over the last four decades, the progress of meteorological satellites has made it possible for scientists to acquire reliable and cost-effective precipitation datasets through a variety of sensors and inversion algorithms [9][10][11][12][13]. Therefore, obtaining high-resolution and accurate precipitation estimates derived from sensors on satellites at a regional or global scale has become a highly-efficient research method at present [4,14,15].Satellite-based precipitation products provided by several institutions and organizations from all over the world are different in terms o...

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Precipitation estimates with high accuracy and fine spatial resolution play an important role in the field of meteorology, hydrology, and ecology. In this study, support vector machine (SVM) and back-propagation neural network (BPNN) machine learning algorithms were used to downscale the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM) (IMERG) data at daily scale through four events selected from 2017 and 2018 by establishing the relationships between precipitation and six environmental variables over Zhejiang, Southeastern China. The downscaled results were validated by ground observations, and we found that (1) generally, the SVMbased products had better performance and finer spatial textures than the BPNN-based products, the multiple linear regression (MLR)-based products, and the original IMERG;(2) all downscaled products decreased the degree of overestimation of the original IMERG at heavy-precipitation regions to a certain extent; (3) for heavy-precipitation events in the plum rain season, the downscaled products based on SVM and BPNN both improved prediction accuracy compared to the MLR-based products and the original IMERG considering the validations against ground observations. R 2 maximally increased from 0.344 to 0.615 for the SVM-based products and from 0.344 to 0.435 for the BPNN-based products compared to the original IMERG; and (4) for typhoon precipitation events, the SVM-based products still showed better accuracy with R 2 maximally increased from 0.492 to 0.615 compared to the original IMERG. In contrast, the performance of BPNN-based products was not satisfying and showed no significant differences with the performance of MLR-based products. This study provided a potential solution for generating downscaled satellite-based precipitation products at meteorological scales with finer accuracy and spatial resolutions.

Soil moisture (SM) is a crucial driving variable for the global land surface-atmosphere water and energy cycle. There are now many satellite-based SM products available internationally and it is necessary to consider all available SM products under the same context for comprehensive assessment and inter-comparisons at the global scale. Moreover, product performances varying with dynamic environmental factors, especially those closely related to retrieval algorithms, were less investigated. Therefore, this study evaluated and identified the relative strengths of nine mainstream satellite-based SM products derived from the Advanced Microwave Scanning Radiometer 2 (AMSR2), Chinese Fengyun-3B (FY3B), the Soil Moisture Active Passive (SMAP), the Soil Moisture and Ocean Salinity (SMOS), and the European Space Agency (ESA) Climate Change Initiative (CCI) by using the Pearson correlation coefficient (R), R of SM seasonal anomalies (Ranom), unbiased Root Mean Square Error (ubRMSE), and bias metrics against ground observations from the International Soil Moisture Network (ISMN), as well as the Global Land Data Assimilation System (GLDAS) Noah model simulations, overall and under three dynamic (Land Surface Temperature (LST), SM, and Vegetation Optical Depth (VOD)) conditions. Results showed that the SMOS-INRA-CESBIO (IC) product outperformed the SMOSL3 product in most cases, especially in Australia, but it exhibited greater variability and higher random errors in Asia. ESA CCI products outperformed other products in capturing the spatial dynamics of SM seasonal anomalies and produced significantly high accuracy in croplands. Although the Chinese FY3B presented poor skills in most cases, it had a good ability to capture the temporal dynamics of the original SM and SM seasonal anomalies in most regions of central Africa. Under various land cover types, with the changes in LST, SM, and VOD, different products exhibited distinctly dynamic error characteristics. Generally, all products tended to overestimate the low in-situ SM content but underestimate the high in-situ SM content. It is expected that these findings can provide guidance and references for product improvement and application promotions in water exchange and land surface energy cycle.

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