Near-Real-Time Monitoring of Crop Progress at Field Scales by Fusing Observations from Both Polar-Orbiting and Geostationary Satellites

Author

Yu Shen, South Dakota State UniversityFollow

Document Type

Dissertation - Open Access

Award Date

2024

Degree Name

Doctor of Philosophy (PhD)

Department / School

Geography and Geospatial Sciences

First Advisor

Xiaoyang Zhang

Abstract

Accurate and timely information on crop progress is crucial for effective crop management and precise modeling of crop yield and productivity. Crop phenology derived from historical satellite data has been widely used to monitor crop progress across various spatial and temporal scales. Conversely, Near-Real-Time (NRT) monitoring of crop progress has received little attention and is challenging due to inadequate clear-sky satellite observations, the absence of future potential crop development information, and the lack of field-scale in-season crop maps. To address these challenges, this dissertation proposes a novel framework for monitoring field-scale crop progress in NRT by integrating observations from polar-orbiting and geostationary satellites. In Chapter 2, a novel Spatiotemporal Shape-Matching Model (SSMM) is introduced to fuse high spatial resolution (30 m) Harmonized Landsat and Sentinel-2 data and high temporal resolution (daily) Visible Infrared Imaging Radiometer Suite (VIIRS) observations (HLS-VIIRS). The results suggest that SSMM effectively facilitates highquality field-scale crop phenometrics monitoring, and HLS-VIIRS corn and soybean phenometrics show significant correlations with National Agricultural Statistics Service (NASS) reported crop progress at various stages. In Chapter 3, the capability of fused time series HLS and Advanced Baseline Imager (ABI) geostationary satellite observations (HLS-ABI) to monitor field-scale land surface phenology in cloud-frequent regions is investigated. It demonstrates that the synthesized time series of HLS-ABI is nearly gap-free and expected to enhance HLS-VIIRS crop progress monitoring in cloudfrequent regions. Chapter 4 presents an algorithm for Near-Real-Time (NRT) monitoring of crop progress at field scales using historical and timely available HLS and ABI observations. The findings indicate that the HLS-ABI real-time prediction of crop phenometrics closely tracks NASS crop progress, exhibiting minor time shifts (≤5 days) and significant correlations (R2 > 0.85, P < 0.001) across various phenological stages of corn and soybean. Chapter 5 introduces an algorithm for mapping corn and soybean in Near-Real-Time (NRT) using the NRT crop phenometrics and real-time crop greenness and canopy water content indices that are produced by fusing ABI, VIIRS, and HLS time series and their probability density that are generated with Gaussian Mixture Model (GMM) across the US Corn Belt. The results demonstrate the robustness of the proposed method in mapping corn and soybean across diverse croplands, achieving an Overall Accuracy (OA) of approximately 90% by the end of July. Chapter 6 provides a summary of the research and offers recommendations for future studies. In conclusion, this dissertation presents a comprehensive and systematic approach to field-scale Near-Real- Time (NRT) crop progress monitoring, incorporating a NRT crop phenology prediction algorithm and a near-real-time crop mapping method.

Publisher

South Dakota State University

Recommended Citation

Shen, Yu, "Near-Real-Time Monitoring of Crop Progress at Field Scales by Fusing Observations from Both Polar-Orbiting and Geostationary Satellites" (2024). Electronic Theses and Dissertations. 956.
https://openprairie.sdstate.edu/etd2/956