Parcel-Based Mapping Framework of Corn Harvest Progress by Combining Optical and Radar Remote Sensing Imagery

Abstract

Timely and accurate monitoring of large-scale crop harvest progress (CHP) is crucial for optimizing agricultural management. Remote sensing shows great potential, but most studies focus on small-scale analysis with limited features and single data sources, lacking large-area mapping. To address this, we developed a regional-scale CHP monitoring framework combining optical and radar data. We analyzed the response of features to corn harvest at Youyi Farm. We developed new indices and algorithms, achieving large-scale CHP mapping. Our findings: First, all 15 features respond to harvest, with shortwave infrared reflectance and the vegetation tillage index (VTI) being the most sensitive. Second, the current binary classification algorithm for mature corn (MC) and crushed straw (CS) is unsuitable for regions with plowed soil (PS). It often misclassifies PS as MC due to similar optical characteristics, causing inaccurate corn harvest monitoring. The new VTI and simple VTI solve this by better distinguishing MC, CS, and PS. Third, corn harvest progress monitoring shows high accuracy, with an overall accuracy of 0.99 for optical data and 0.95 for radar data. Fourth, combining Sentinel-1 and Sentinel-2 data increased the monitoring frequency from 6–8 days to an average of 2–3 days. Fifth, a time-series misclassification correction strategy was developed and applied, correcting an average area of 3.3% of the study area. Sixth, an earlier harvest start time in 2021, aligned with drought occurrences, confirms the feasibility of the CHP mapping framework. This research provides an effective tool for monitoring CHP, which can support agricultural management.

Keywords

• Corn
• harvest progress
• mapping framework
• Sentinel-1
• Sentinel-2