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Continuous evapotranspiration (ET) data with high spatial resolution are crucial for water resources management in irrigated agricultural areas in arid regions. Many global ET products are available now but with a coarse spatial resolution. Spatial-temporal fusion methods, such as the spatial and temporal adaptive reflectance fusion model (STARFM), can help to downscale coarse spatial resolution ET products. In this paper, the STARFM model is improved by incorporating the temperature vegetation dryness index (TVDI) into the data fusion process, and we propose a spatial and temporal adaptive evapotranspiration downscaling method (STAEDM). The modified method STAEDM was applied to the 1 km SSEBOP ET product to derive a downscaled 30 m ET for irrigated agricultural fields of Northwest China. The STAEDM exhibits a significant improvement compared to the original STARFM method for downscaling SSEBOP ET on Landsat-unavailable dates, with an increase in the squared correlation coefficients (r2) from 0.68 to 0.77 and a decrease in the root mean square error (RMSE) from 10.28 mm/10 d to 8.48 mm/10 d. The ET based on the STAEDM additionally preserves more spatial details than STARFM for heterogeneous agricultural fields and can better capture the ET seasonal dynamics. The STAEDM ET can better capture the temporal variation of 10-day ET during the whole crop growing season than SSEBOP. [ABSTRACT FROM AUTHOR]

Highly accurate near-real-time satellite precipitation estimates (SPEs) are important for hydrological forecasting and disaster warning. The near-real quantitative precipitation estimates (REGC) of the recently developed Chinese geostationary meteorological satellite Fengyun 4A (FY4A) have the advantage of high spatial and temporal resolution, but there are errors and uncertainties to some extent. In this paper, a self-adaptive ill-posed least squares scheme based on sequential processing (SISP) is proposed and practiced in mainland China to correct the real-time biases of REGC hour by hour. Specifically, the scheme adaptively acquires sample data by setting temporal and spatial windows and constructs an error-correction model based on the ill-posed least squares method from the perspectives of climate regions, topography, and rainfall intensity. The model adopts the sequential idea to update satellite precipitation data within time windows on an hour-by-hour basis and can correct the biases of real-time satellite precipitation data using dynamically changing parameters, fully taking into account the influence of precipitation spatial and temporal variability. Only short-term historical data are needed to accurately rate the parameters. The results show that the SISP algorithm can significantly reduce the biases of the original REGC, in which the values of relative bias (RB) in mainland China are reduced from 11.2% to 3.3%, and the values of root mean square error (RMSE) are also reduced by about 17%. The SISP algorithm has a better correction in humid and semi-humid regions than in arid and semi-arid regions and is effective in reducing the negative biases of precipitation in each climate region. In terms of rain intensity, the SISP algorithm can improve the overestimation of satellite precipitation estimates for low rain intensity (0.2–1 mm/h), but the correction for high rain intensity (>1 mm/h) needs further improvement. The error component analysis shows that the SISP algorithm can effectively correct the hit bias. This study serves as a valuable reference for real-time bias correction using short-term accumulated precipitation data. [ABSTRACT FROM AUTHOR]

Numerous methods have been used in the spatial prediction of soil salinity. However, the most suitable method is still unknown in arid irrigation regions. In this paper, 78 locations were sampled in salt-affected land caused by irrigation in an arid area in northern China. The geostatistical characteristics of the soil pH, Sodium Adsorption Ratio (SAR), Total Salt Content (TSC), and Soil Organic Matter (SOM) of the surface (0–20 cm) and subsurface (20–40 cm) layers were analyzed. The abilities of the Inverse Distance Weighting (IDW), Ordinary Kriging (OK), and CoKriging (CK) interpolation methods were compared, and the Root Mean Square Error (RMSE) was used to justify the results of the methods. The results showed that the spatial distributions of the soil properties obtained using the different interpolation methods were similar. However, the surface layer exhibits more spatial heterogeneity than the subsurface layer. Based on the RSME, the nugget/sill value and range significantly affected which method was the most suitable. Lower nugget/sill values and lower ranges can be fitted using the IDW method, but higher nugget/sill values and higher ranges can be fitted using the OK method. These results provide a valuable reference for the prediction of soil salinity. [ABSTRACT FROM AUTHOR]