Towards remote sensing modeling framework for crop phenological characterization

Keywords: Remote sensing, Time series, Crop progress, Phenology, Agriculture
Session Type: Virtual Paper Abstract
Day: Friday
Session Start / End Time: 2/25/2022 02:00 PM (Eastern Time (US & Canada)) - 2/25/2022 03:20 PM (Eastern Time (US & Canada))
Room: Virtual 7

Abstract

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. During the past decade, time series of remotely sensed imagery has been increasingly employed to monitor the seasonal growing dynamics of crops. Yet our ability in connecting the remotely sensed modeled seasonal trajectory with specific crop growth stages of important physiological implications is limited. In this presentation, I will introduce our efforts towards building a remote sensing modeling framework for characterizing a diverse range of crop phenological stages. Specifically, the framework includes a collection of curve fitting-based phenological models, a newly developed hybrid phenology matching model, and a pheno-network model. With corn and soybean in Illinois as a case study, the framework can estimate key phenological stages of crop cycles, ranging from farming practice-relevant stages (e.g., planted and harvested) to crop development stages (e.g., emerged and mature). It exhibits marked potential to advance phenological monitoring in complex agricultural diversified and intensified systems.