Report Summarizes Insurance Study Findings from University of Illinois (From Satellite-based Phenological Metrics To Crop Planting Dates: Deriving Field-level Planting Dates for Corn and Soybean In the Us Midwest): Insurance

2024 OCT 07 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Research findings on Insurance are discussed in a new report. According to news reporting out of Urbana, Illinois, by NewsRx editors, research stated, “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.”

Funders for this research include USDA Risk Management Agency, National Science Foundation (NSF), NASA FINESST award, NASA Early Career Investigator Program in Earth Science, USDA National Institute of Food and Agriculture (NIFA) Rapid Response, USDA NIFA Artificial Intelligence for Future Agricultural Resilience, Management, and Sustainability (AI-FARMS) project.

Our news journalists obtained a quote from the research from the University of Illinois, “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 similar to 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 (R-2=0.90, MAE=2.7 days) and soybeans (R-2=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).”

According to the news editors, the research concluded: “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.”

This research has been peer-reviewed.

For more information on this research see: From Satellite-based Phenological Metrics To Crop Planting Dates: Deriving Field-level Planting Dates for Corn and Soybean In the Us Midwest. ISPRS Journal of Photogrammetry and Remote Sensing, 2024;216:259-273. ISPRS Journal of Photogrammetry and Remote Sensing can be contacted at: Elsevier, Radarweg 29, 1043 Nx Amsterdam, Netherlands. (Elsevier - www.elsevier.com; ISPRS Journal of Photogrammetry and Remote Sensing - http://www.journals.elsevier.com/isprs-journal-of-photogrammetry-and-remote-sensing/)

Our news journalists report that additional information may be obtained by contacting Kaiyu Guan, University of Illinois, Inst Sustainabil Energy & Environm, Agroecosyst Sustainabil Ctr, Urbana, IL 61801, United States. Additional authors for this research include Qu Zhou, Sheng Wang, Zhangliang Chen and James Hipple.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.isprsjprs.2024.07.031. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

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