Cropland productivity index (CPI) in China, 2001_2020, 250m

1. OverviewThis dataset provides annual estimates of the cropland productivity index (CPI) for stable cropland areas in China from 2001 to 2020. The data is provided at a 250-meter spatial resolution.The data was created to overcome the limitations of traditional productivity assessments, which often lack spatial detail or temporal frequency. It is based on a remote sensing model called the Crop Growth Index (CGI), which uses satellite data to monitor crop growth dynamics over time.2. Methodology2.1 The Crop Growth Index (CGI) ModelThe CGI is a custom index designed to represent cropland productivity. It is calculated using this formula:CGI = L_cgs * EVI_meanL_cgs: The length (in days) of the "critical growth period" for crops.EVI_mean: The average Enhanced Vegetation Index (EVI) value during that period.The "critical growth period" is defined as the time when the EVI value is above a certain threshold. This threshold (75% of the 3-year moving average maximum EVI) was determined through a sensitivity analysis to achieve the best correlation with national grain yield statistics.2.2 Data Production WorkflowSatellite Data: The model uses the 250m EVI data from the MODIS (MOD13Q1.061) satellite product.Cropland Mask: A stable cropland map was created by fusing multiple land use datasets to ensure only consistent agricultural areas were analyzed.Processing: The entire workflow was implemented on the Google Earth Engine (GEE) platform. This included smoothing the raw EVI time-series data and calculating the annual CGI for every pixel from 2001 to 2020.3. Accuracy AssessmentThe accuracy of the CGI dataset was validated using two approaches:3.1 Direct ValidationThe CGI data was compared against ground-based measurements of annual gross primary productivity (AGPP) from 37 cropland monitoring sites across China.Result: The CGI showed a good correlation, explaining approximately 60% of the variance (average R-squared = 0.592) in the ground-observed productivity.3.2 Indirect ValidationThe CGI was compared with five other public productivity datasets by assessing their consistency with national statistics and agricultural survey data at county, provincial, and national scales.Result: The CGI demonstrated the best overall performance, especially in tracking the year-to-year changes (temporal trends) in productivity at both provincial and national levels. Its spatial patterns were also highly consistent with validation data.4. Key FindingsNational Trend: From 2001 to 2020, average cropland productivity in China increased at an annual rate of 2.11%. The growth was significantly faster in the first decade (2001-2010) than in the second.Regional Patterns: The Northeast Plain showed the highest average productivity, while the Huang-Huai-Hai Plain and Loess Plateau experienced the fastest growth rates.5. LimitationsThe model uses a single national threshold, which may reduce accuracy in some local areas.The relationship between CGI and actual yield may be non-linear.The validation relies on linear regression, which is a simplification of complex real-world relationships.