2.2.1. Spatial Data

We used Google Earth Engine (GEE) to gather and to calculate the spatial data for this analysis. GEE is an interactive platform that provides geospatial processing services that are powered by the Google Cloud Platform [30]. With Earth Engine, we can perform geospatial processing at a scale that is free of charge, and we can carry out high-impact, data-driven scientific research involving large geospatial datasets [31,32]. In this research, we adopted multi-remote sensing time series data from 2000 to 2020, to detect the impact of land use changes on the value of cropland resources. Landcover data were derived from images collected by the MODIS sensor (the MCD12Q1 V6 product), which provides global land cover types at yearly intervals (250 m × 250 m). The digital elevation models (DEMs) used Shuttle Radar Topography Mission (SRTM) data at a 30 m resolution. Additionally, we estimated the Landsat net primary production (NPP) using Landsat Surface Reflectance for CONUS (Landsat net primary production CONUS) [33]. Beyond these, we selected the GPM data (Monthly Global Precipitation Measurement v6) to revise the existing results of ecological value. Global Precipitation Measurement (GPM) is an international satellite mission that provides next-generation observations of rain and snow worldwide, every three hours. The Integrated Multi-Satellite Retrievals for GPM (IMERG) is a unified algorithm that provides rainfall estimates by combining data from all passive-microwave instruments in the GPM Constellation.
