Nitrogen (N) and phosphorus (P) are primary indicators of soil nutrients in agriculture. Accurate management of these nutrients is essential for ensuring food security. High-resolution, multi-spectral remote sensing images can provide crucial information for mapping soil nutrients at the field scale. This study compares the capabilities of ZH-1 and Sentinel-2 satellite data, along with different spectral indices, in mapping soil nutrients (total N and Olsen-P) using two machine learning algorithms, random forest (RF) and XGBoost (XGB). Two agricultural fields in Suihua City were selected as the study areas for this investigation. The results showed that Sentinel-2 data performed best in computing the total N content in soil using the RF model (R² = 0.74, RMSE = 0.10 g/kg). However, for the soil Olsen-P content, the XGBoost model performed better with ZH-1 data (R² = 0.75, RMSE = 9.79 mg/kg) than the RF model. This study demonstrates that both ZH-1 and Sentinel-2 satellite data perform well in terms of accurately mapping soil total N and Olsen-P contents using machine learning. Due to its higher spectral and spatial resolution, ZH-1 remote sensing data provides more detailed information on soil nutrient content during Olsen-P inversion and exhibits comparable accuracy.

Precipitation plays an important role in the food production of Southern Africa. Understanding the spatial and temporal variations of precipitation is helpful for improving agricultural management and flood and drought risk assessment. However, a comprehensive precipitation pattern analysis is challenging in sparsely gauged and underdeveloped regions. To solve this problem, Version 7 Tropical Rainfall Measuring Mission (TRMM) precipitation products and Google Earth Engine (GEE) were adopted in this study for the analysis of spatiotemporal patterns of precipitation in the Zambezi River Basin. The Kendall’s correlation and sen’s Slop reducers in GEE were used to examine precipitation trends and magnitude, respectively, at annual, seasonal and monthly scales from 1998 to 2017. The results reveal that 10% of the Zambezi River basin showed a significant decreasing trend of annual precipitation, while only 1% showed a significant increasing trend. The rainy-season precipitation appeared to have a dominant impact on the annual precipitation pattern. The rainy-season precipitation was found to have larger spatial, temporal and magnitude variation than the dry-season precipitation. In terms of monthly precipitation, June to September during the dry season were dominated by a significant decreasing trend. However, areas presenting a significant decreasing trend were rare (<12% of study area) and scattered during the rainy-season months (November to April of the subsequent year). Spatially, the highest and lowest rainfall regions were shifted by year, with extreme precipitation events (highest and lowest rainfall) occurring preferentially over the northwest side rather than the northeast area of the Zambezi River Basin. A “dry gets dryer, wet gets wetter” (DGDWGW) pattern was also observed over the study area, and a suggestion on agriculture management according to precipitation patterns is provided in this study for the region. This is the first study to use long-term remote sensing data and GEE for precipitation analysis at various temporal scales in the Zambezi River Basin. The methodology proposed in this study is helpful for the spatiotemporal analysis of precipitation in developing countries with scarce gauge stations, limited analytic skills and insufficient computation resources. The approaches of this study can also be operationally applied to the analysis of other climate variables, such as temperature and solar radiation.

Land surface temperature (LST) is a vital parameter associated with the land–atmosphere interface. The Moderate Resolution Imaging Spectroradiometer (MODIS) LST product can provide precise LST with high time resolution, and is widely applied in various remote sensing temperature research. However, due to its inability to penetrate the cloud and fog, its quality is not able to meet the requirements of actual research. Hence, obtaining continuous and cloudless MODIS LST datasets remains challenging for researchers. The critical point is to reconstruct missing pixels. To compare the performance of different methods, first, three kinds of methods were used to reconstruct the missing pixels, namely, temporal, spatial, and spatiotemporal methods. The predicted values using these methods were validated by the automatic weather system data (AWS) in the Heihe river basin of China. The results demonstrated that, compared with other methods, linear temporal interpolation using Aqua data had the best performance in MODIS LST reconstruction in the Heihe river basin, with an RMSE of 7.13 K and an R² of 0.82, and the NSE and PBias were 0.78 and −0.76%, respectively. Furthermore, the interpolation method was improved using adaptive windows and robust regression. First, the international Geosphere–Biosphere Program (IGBP) classification was employed to distinguish the different land surface types. Then, the invalid LST values were reconstructed using adjacent days’ effective LST values combined with a robust regression. Finally, a mean filter was applied to eliminate outliers. The overall results combined with ERA5 data were validated by AWS, with an RMSE of 6.96 K and an R² of 0.79 and the NSE and PBias were 0.77 and −0.20%, respectively. The validation demonstrated that the scheme proposed in this paper is able to accurately reconstruct the missing values and improve the accuracy of the interpolation method to a certain extent when reconstructing MODIS LST.

The evaluation of the ecosystem service value (ESV) and its regionalization toward coordinating ecological protection and socioeconomic development is of great significance. In this study, we developed a classification method based on the Random Forest algorithm and a feature optimization method to identify grassland types. Then, we proposed an approach to quantitatively evaluate the ESV of the grassland ecosystem in Ethiopia, in which net primary production derived from remote sensing was used to evaluate organic matter production value (ESV1), promoting nutrient circulation value (ESV2), and gas regulation value (ESV3), the RUSLE model was used to evaluate soil conservation value (ESV4), and cumulative rainfall was used to calculate water conservation value (ESV5). By integrating the mean ESV under various influencing factors, the zoning map of grassland ecosystem service value was obtained. Our study found that more fine grassland types can be well classified with the overall accuracy of 86.52%. And the classification results are the basis of the ESV analysis. The total ESV of grassland ecosystems was found to be USD 105,221.72 million, of which ESV4 was the highest, accounting for 44.09% of the total ESV. The spatial analysis of ESV showed that the differences were due to the impacts of grassland types, elevation, slope, and rainfall. It was found that the grassland is suitable to grow in the elevation zone between approximately 1000 and 2000 m, and the larger the slope and rainfall are, the greater the mean ESV is. The zoning map was used to conclude that the areas from approximately the fourth to sixth level (only 34.78% of the total grassland area, but 65.94% of the total ESV) have better growth status and development potential. The results provide references and bases to support the local coordination and planning of various grassland resources and form reasonable resource utilization and protection measures.