Master of Science (MS), Bowling Green State University, 2024, Geology
Precise estimation of canopy chlorophyll content (CCC) and canopy nitrogen content (CNC) is essential for effective monitoring of crop growth conditions. The field of satellite imaging spectroscopy, known as hyperspectral imaging, offers a non-destructive, large-area, and real-time approach to monitoring of CCC and CNC over large areas. With the introduction of recent science-driven mission such as the Environmental Mapping and Analysis Program (EnMAP), imaging spectroscopy has emerged as a crucial component of empirical and physical modelling. This study introduces two approaches for retrieving CCC and CNC using EnMAP hyperspectral imagery acquired over Kellogg Biological Station, Michigan in the summer 2023: i) Machine learning regression algorithms (MLRAs), and ii) a hybrid model that combines a radiative transfer model with machine learning. We assessed the performances of six regression techniques including kernel ridge regression (KRR), least squares linear regression (LSLR), partial least squares regression (PLSR), Gaussian process regression (GPR), neural network (NN), and random forest (RF) for retrieving CCC and CNC. In the hybrid model workflow, each of the six techniques were integrated with PROSAIL, a widely known radiative transfer model. For both the approaches, the final CCC and CNC models were validated against field data. For CCC retrieval, KRR demonstrated the best performance in the MLRA approach (RMSE = 10.01, NRMSE = 9.81%, R2 = 0.93), while GPR exhibited the best performance in the hybrid approach (RMSE = 9.62, NRMSE = 9.43%, R2 = 0.93). Regarding CNC retrieval, KRR outperformed other models in both the MLRA (RMSE = 10.10, NRMSE = 8.13%, R2 = 0.94) and hybrid approach (RMSE = 16.98, NRMSE = 13.67%, R2 = 0.83). While the hybrid model outperformed the standalone MLRA approach for CCC retrieval, the MLRA approach surpassed the hybrid approach in CNC retrieval. In CCC retrieval, the most significant bands of EnMAP were identified in the visible to (open full item for complete abstract)
Committee: Anita Milas Ph.D. (Committee Chair); Qing Tian Ph.D. (Committee Member); Jochem Verrelst Ph.D. (Committee Member)
Subjects: Geographic Information Science; Geography; Geology; Remote Sensing