From satellite-based phenological metrics to crop planting dates: Deriving field-level planting dates for corn and soybean in the U.S. Midwest

Information on planting dates is crucial for modeling crop development, analyzing crop yield, and evaluating the effectiveness of policy-driven planting windows. Despite their high importance, field-level planting date datasets are scarce. Satellite remote sensing provides accurate and cost-effective solutions for detecting crop phenology from moderate to high resolutions, but remote sensing-based crop planting date detection is rare. Here, we aimed to generate field-level crop planting date maps by taking advantage of satellite remote sensing-derived phenological metrics and proposed a two-step framework to predict crop planting dates from these metrics using required growing degree dates (RGDD) as a bridge. Specifically, we modeled RGDD from the planting date to the spring inflection date (derived from phenological metrics) and then predicted the crop planting dates based on phenological metrics, RGDD, and environmental variables. The ∼3-day and 30-m Harmonized Landsat and Sentinel-2 (HLS) products were used to derive crop phenological metrics for corn and soybean fields in the U.S. Midwest from 2016 to 2021, and the ground truth of field-level planting dates from USDA Risk Management Agency (RMA) reports were used for the development and validation of our proposed two-step framework. The results indicated that our framework could accurately predict field-level planting dates from HLS-derived phenological metrics, capturing 77 % field-level variations for corn (mean absolute error, MAE=4.6 days) and 71 % for soybean (MAE=5.4 days). We also evaluated the predicted planting dates with USDA National Agricultural Statistics Service (NASS) state-level crop progress reports, achieving strong consistency with median planting dates for corn (R²=0.90, MAE=2.7 days) and soybeans (R²=0.87, MAE=2.5 days). The model's performance degraded slightly when predicting planting dates for fields with irrigation (MAE=5.4 days for corn, MAE=6.1 days for soybean) and cover cropping (MAE=5.4 days for corn, MAE=5.6 days for soybean). The USDA RMA Common Crop Insurance Policy (CCIP) provides county- or sub-county-level crop planting windows, which drive producers’ decisions on when to plant. Within the CCIP-driven planting windows, higher prediction accuracies were achieved (MAE for corn: 4.5 days, soybean: 5.2 days). Our proposed two-step framework (phenological metrics-RGDD-planting dates) also outperformed the traditional one-step model (phenological metrics-planting dates). The proposed framework can be beneficial for deriving planting dates from current and future phenological products and contribute to studies related to planting dates such as the analysis of yield gaps, management practices, and government policies.