Abstract: A high-resolution SPEI dataset development method based on a random forest regression model is provided. In the method, meteorological station data, GPM remote sensing precipitation data, MODIS land surface temperature data, ERA5-Land shortwave radiation data and SRTM digital elevation model data are combined; and a spatial pattern of SPEI index at different time scales of a target area is predicted by constructing a spatiotemporal relationship between the SPEI index and the precipitation, land surface temperature, shortwave radiation and elevation data. The method fully utilizes advantages that the random forest is high in precision and avoids overfitting in model prediction, and inputs station data and remote sensing and reanalysis data simultaneously into the model for training, which can solve problems of mismatch of an existing SPEI dataset with the station data and low spatial resolution, and the spatial resolution of SPEI dataset is effectively improved.

Abstract: A method for automatically identifying global solar photovoltaic (PV) panels based on a cloud platform by using remote sensing. Optical images in a study area for a whole specific year are collected based on the cloud platform, and preprocessing is performed to obtain a surface reflectance image. Seven time-series images are derived and constructed based on spectral features of a solar PV panel: a solar PV panel index image, a water index image, a vegetation index image, a difference image between a first shortwave infrared band and a second shortwave infrared band, a difference image between the first shortwave infrared band and a near-infrared band, a blue band image, and a first shortwave infrared band image. Data in the seven time-series images are synthesized and reconstructed to obtain input data required by a model. A remote sensing theoretical model for automatically identifying a solar PV panel is constructed.

Abstract: A method for automatically identifying global solar photovoltaic (PV) panels based on a cloud platform by using remote sensing. Optical images in a study area for a whole specific year are collected based on the cloud platform, and preprocessing is performed to obtain a surface reflectance image. Seven time-series images are derived and constructed based on spectral features of a solar PV panel: a solar PV panel index image, a water index image, a vegetation index image, a difference image between a first shortwave infrared band and a second shortwave infrared band, a difference image between the first shortwave infrared band and a near-infrared band, a blue band image, and a first shortwave infrared band image. Data in the seven time-series images are synthesized and reconstructed to obtain input data required by a model. A remote sensing theoretical model for automatically identifying a solar PV panel is constructed.

Abstract: A cloud platform-based garlic crop recognition method by coupling active and passive remote sensing images includes: firstly, obtaining an optical satellite remote sensing image based on phenological characteristics of garlic, and constructing a decision tree model for optical image recognition of the garlic by combining geographic coordinate information of the garlic, so as to obtain an optical distribution diagram of the garlic; secondly, obtaining radar image characteristics of the garlic and winter wheat based on a synthetic aperture radar satellite, and constructing a decision tree model for radar image recognition of the garlic by combining the geographic coordinate information of the garlic, so as to obtain a radar distribution diagram of the garlic; and finally, coupling the optical distribution diagram of the garlic with the radar distribution diagram of the garlic, i.e., selecting an intersection of the two distribution diagrams to complete remote sensing recognition drawing of the garlic.