Near-Surface and High Resolution Satellite Remote Sensing Time Series for Understanding Crop Phenological Responses to Climate Change

Authors: Geyang Li*, University of Illinois at Urbana Champaign, Chunyuan Diao, University of Illinois at Urbana Champaign
Topics: Remote Sensing
Keywords: Remote Sensing, Phenology, PhenoCam, Planet, Time Series
Session Type: Guided Poster
Day: 4/6/2019
Start / End Time: 8:00 AM / 9:40 AM
Room: Roosevelt 3.5, Marriott, Exhibition Level
Presentation File: No File Uploaded

Crop phenology plays a critical role in farming, as crops in different phenological stages need varying amounts of resources with different responses to climate changes. To date, remote crop phenological monitoring has been mostly conducted at coarse spatial resolutions. Despite the promising results of those studies, the phenology retrieved at such resolutions may not appropriately represent the crop phenological development at spatially heterogeneous or fragmented regions. Recent development of near-surface remote sensing (i.e., PhenoCam) and the newly available high spatial resolution satellite imagery (i.e., Planet) create unique opportunities to track the crop phenological developments at high spatial resolutions. Yet their feasibility and potentials in representing the crop growth stages requires exploration. The objective of this research is to investigate the time series of the near-surface and high-resolution satellite remote sensing imagery for understanding crop phenological responses to climate change. The study site contains four crop fields with PhenoCams, located at the University of Illinois. Images from PhenoCams from 2010 to 2017 were used to generate time series of green chromatic coordinate. Multiple curve-fitting based phenological approaches were used to extract critical phenological phases of crops. Planet Satellite imagery covering the same area was used to construct NDVI time series for phenological estimations. The phenological phases extracted were then compared to ground-based phenological observations, then analyzed with climate variables. The results accurately estimated the critical phenological transition dates of crops, showed great potential in complementing and validating conventional phenological analysis, and improved the understanding of crop phenological responses to climate change.

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