CropSow: a novel modeling framework to estimate field-level crop sowing date with multi-scale satellite time series
Topics: Agricultural Geography
, Remote Sensing
, Spatial Analysis & Modeling
Keywords: Sowing date, Multi-scale satellite time series, Phenology, Remote sensing, Agriculture
Session Type: Virtual Guided Poster Abstract
Day: Sunday
Session Start / End Time: 2/27/2022 09:40 AM (Eastern Time (US & Canada)) - 2/27/2022 11:00 AM (Eastern Time (US & Canada))
Room: Virtual 1
Authors:
YIN LIU, University of Illinois at Urbana-Champaign
Chunyuan Diao, University of Illinois at Urbana-Champaign
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Abstract
Crop sowing date plays an important role in regulating the climatic and environmental conditions the crop experiences throughout the growing season. As the essential parameter of crop models, it is also crucial to the estimation of dry matter accumulation and subsequent crop yields. Obtaining accurate sowing date information is thus a key component for characterizing crop growing dynamics under varying farming practices, as well as facilitating proactive adaptation of agricultural systems to climate change. The typical methods to estimate sowing dates include remotely sensed phenological models and climate-driven models. However, it is still challenging to effectively estimate the crop sowing dates at the field scale, due to the lack of the appropriate modeling design as well as the dearth of ground sowing reference data. In our study, we developed a novel CropSow modeling framework to estimate field-level sowing date by integrating the climate-driven model and remotely sensed phenological model using the multi-scale satellite time series. With corn in Illinois as a case study, CropSow first estimated the crop emergence date from satellite time series and located the sowing window based on agro-meteorological knowledge. Within the sowing window, it then predicted the field-tailored sowing date by taking into account the field workability and crop growing conditions. Results showed that our developed modeling framework outperformed the benchmarking models in crop sowing date estimation at the field level and held considerable promise to extrapolate over space and time for predicting the timing of crop sowing of individual farm fields at large scales.
CropSow: a novel modeling framework to estimate field-level crop sowing date with multi-scale satellite time series
Category
Virtual Guided Poster Abstract
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