Soil organic matter (SOM) is an essential nutrient for crop growth and development. Hyperspectral satellite images with comprehensive spectral band coverage and high spectral resolution can be used to estimate and draw a spatial distribution map of SOM content in the region, which can provide a scientific management basis for precision agriculture. This study takes Xinzheng City, Henan Province’s agricultural area, as the research object. Based on ZY1-02D hyperspectral satellite image data, the first derivative of reflectance (FDR) was processed on the original reflectance (OR). The SOM characteristic spectral bands were extracted using the correlation coefficient (CC) and least absolute shrinkage and selection operator (Lasso) methods. The prediction model of SOM content was established by multiple linear regression (MLR), partial least squares regression (PLSR), and random forest (RF) algorithms. The results showed that: (1) FDR processing can enhance SOM spectral features and reduce noise; (2) the Lasso feature band extraction method can reduce the model’s input variables and raise the estimation precision; (3) the SOM content prediction ability of the RF model was significantly better than that of the MLR and PLSR models. The FDR-Lasso-RF model was the best SOM content prediction model, and the validation set R2 = 0.921, MAEV = 0.512 g/kg, RMSEV = 0.645 g/kg; (4) compared with laboratory hyperspectral data-SOM prediction methods, hyperspectral satellite data can achieve accurate, rapid, and large-scale SOM content prediction and mapping. This study provides an efficient, accurate, and feasible method for predicting and mapping SOM content in an agricultural region.

With the development of artificial intelligence (AI) in recent years, meteorological departments have also begun to improve algorithms and revise short-term forecasts via AI, expecting to timely capture meteorological clues in massive weather data, to "prevent meteorological disasters", and "calculate precipitation faster and more accurately". At present, AI has been initially applied to the meteorological field, especially to the analysis of massive meteorological data. For instance, the AI-based data analysis technology can rapidly judge the cloud type and the meteorological prototype in satellite images. The AI-based data fusion technology contributes to more three-dimensional and refined atmosphere data, which improves the temporal and spatial resolutions of precipitation data. If the big data in AI are used to analyze typhoons and identify the typhoon track and source, the errors resulting from the naked-eye observation of images by meteorologists can be avoided, thus considerably improving the scientificity and accuracy of weather forecasts. During data fusion, the severe convective weather characteristics reflected by massive historical precipitation data can be learned through machine learning methods to predict the evolution trend of disastrous weather within the future 1 to 2 h. Furthermore, precipitation data errors are corrected through AI data analysis, and a daily precipitation fusion dataset with a spatial resolution of 1 km is obtained.