Remotely Sensed Hybrid Phenology Matching Model to Estimate Crop Growing Stages

Chunyuan Diao,  University of Illinois at Urbana-Champaign,  chunyuan@illinois.edu (Presenter)
Zijun Yang,  University of Illinois at Urbana-Champaign,  zijuny2@illinois.edu
Feng Gao,  USDA-ARS HRSL,  feng.gao@ars.usda.gov
Xiaoyang Zhang,  South Dakota State University,  xiaoyang.zhang@sdstate.edu
Zhengwei Yang,  USDA-NASS, Research & Development Division,  zhengwei.yang@nass.usda.gov
Geyang Li,  University of Illinois at Urbana-Champaign,  gli19@illinois.edu

Crop phenology regulates seasonal agroecosystem carbon, water, and energy exchanges, and is a key component in empirical and process-based crop models for simulating biogeochemical cycles of farmlands and forecasting the crop yield. The advances in phenology matching models provide a feasible means to monitor crop phenological progress using remote sensing observations, with a priori information of reference shapes and reference phenological transition dates. Yet the underlying geometrical scaling assumption of models, together with the challenge in defining phenological references, hinders the applicability of phenology matching in crop phenological studies. The objective of this study is to develop a novel hybrid phenology matching model to robustly retrieve a diverse spectrum of crop growth stages using satellite time series. The devised hybrid model leverages the complementary strengths of phenometric extraction methods and phenology matching models, and can characterize key phenological stages of crop cycles, ranging from farming practice-relevant stages to crop development stages. To systematically evaluate the influence of phenological references on phenology matching, four representative phenological reference scenarios under varying levels of phenological calibrations are further designed with publicly accessible phenological information. The results indicate that the hybrid phenology matching model can achieve high accuracies for estimating corn and soybean phenological growth stages in Illinois, particularly with the year- and region-adjusted phenological reference (R-squared higher than 0.9 and RMSE less than 5 days for most phenological stages). The inter-annual and regional phenological patterns characterized by the hybrid model correspond well with those in the crop progress reports (CPRs) from the USDA National Agricultural Statistics Service (NASS). This innovative hybrid phenology matching model, together with CPR-enabled phenological reference calibrations, holds considerable promise in revealing spatio-temporal patterns of crop phenology over extended geographical regions.

Poster: Poster_Diao_1-44_76_35.pdf 

Poster Location ID: 1-44

Presentation Type: Poster

Session: Poster Session 1

Session Date: Tue (May 9) 5:00-7:00 PM

CCE Program: Other