DOI: 10.6084/m9.figshare.30618923.v1
title: Cropland productivity index (CPI) in China, 2001_2020, 250m

description: <b>1. Overview</b>This dataset provides annual estimates of the <b>cropland productivity index (CPI</b><b>) </b>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 <b>Crop Growth Index (CGI)</b>, which uses satellite data to monitor crop growth dynamics over time.<b>2. Methodology</b><b>2.1 The Crop Growth Index (CGI) Model</b>The CGI is a custom index designed to represent cropland productivity. It is calculated using this formula:<b>CGI = L_cgs * EVI_mean</b><b>L_cgs:</b> The length (in days) of the "critical growth period" for crops.<b>EVI_mean:</b> 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.<b>2.2 Data Production Workflow</b><b>Satellite Data:</b> The model uses the 250m EVI data from the MODIS (MOD13Q1.061) satellite product.<b>Cropland Mask:</b> A stable cropland map was created by fusing multiple land use datasets to ensure only consistent agricultural areas were analyzed.<b>Processing:</b> 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.<b>3. Accuracy Assessment</b>The accuracy of the CGI dataset was validated using two approaches:<b>3.1 Direct Validation</b>The CGI data was compared against ground-based measurements of annual gross primary productivity (AGPP) from 37 cropland monitoring sites across China.<b>Result:</b> The CGI showed a good correlation, explaining approximately <b>60%</b> of the variance (average R-squared = 0.592) in the ground-observed productivity.<b>3.2 Indirect Validation</b>The 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.<b>Result:</b> The CGI demonstrated the <b>best overall performance</b>, 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.<b>4. Key Findings</b><b>National Trend:</b> From 2001 to 2020, average cropland productivity in China increased at an annual rate of <b>2.11%</b>. The growth was significantly faster in the first decade (2001-2010) than in the second.<b>Regional Patterns:</b> The Northeast Plain showed the highest average productivity, while the Huang-Huai-Hai Plain and Loess Plateau experienced the fastest growth rates.<b>5. Limitations</b>The 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.