High-resolution remote-sensing earth observation provide us with effective technical support for objectively inverting the surface patterns-process from the dimensions of space and time. This paper follows the research idea of space-temporal collaboration, and based on the high-resolution remote sensing images, we explored two typical problems in the agricultural remote sensing field: (1) proposed a division control and stratified extraction method for geo-parcel based on visual characteristics of images. Based on the division of DEM, we have designed different geo-parcel extraction models based on the differences in geometric and texture features in the division regions; (2) proposed a method for crop growth parameters inversing at the geo-parcel scale. Geo-parcel is the basic unit to perform physical parameter inversion under the constraints of space-time-attribute combination. The study taking geo-parcels extraction in in Xixiu District of Anshun City in Guizhou Province and Fusui County in Guangxi as examples for the division control and stratified extraction method, and taking inversion of sugarcane leaf area index in Fusui County of Guangxi Province as examples for the method of crop growth parameters inversing at the geo-parcel scale. For the extraction of cultivated land in Xixiu District, The number of geo-parcels with morphological accuracy (IoU) greater than 0.7 accounts for more than 60%, and the accuracy of the types of regular geo-parcels, terraces, forests and grasslands exceeded 80%; also, for the inversion results of sugarcane leaf area index in Fusui County, the results can accurately reflect the difference between base sugarcane and non-base sugarcane, and the base sugarcane is superior in quality to non-base sugarcane. It shows that Spatial-temporal collaboration use of multi-source high-resolution data is an effective way to achieve accurate agricultural remote sensing research.