Modeling grassland parameters with hyperspectral satellite data: Comparison of sensors, acquisition times and spectral transformations
Abstract
Recent advancements in hyperspectral satellite technology, including sensors like EnMAP, are promising for monitoring grassland ecosystems at the landscape scale. These developments include detailed vegetation analysis capabilities, crucial for understanding plant traits and species composition. However, technical restrictions of recent hyperspectral satellite missions can hinder comprehensive coverage of research areas resulting in a temporal mismatch of field measurements and satellite data. Here we utilize hyperspectral data from the DESIS, PRISMA, and EnMAP mission, along with field measurements from 74 grassland plots of the German Biodiversity Exploratories in Schorfheide-Chorin and Hainich, collected in May 2020 and 2023. We focus on the impact of different satellite sensors and their acquisition timing grouped within five phenological seasons (early April to August) on the accuracy of biomass and species composition models using Partial Least Squares Regression (PLSR) and Procrustes randomization tests. Additionally, we are evaluating the effectiveness of two spectral transformations in improving model accuracy and reliability. Our findings reveal significant differences in the relationship of hyperspectral satellite data with grassland biomass and species composition. Even though comparison of DESIS biomass models indicated that sensor data from the beginning of April achieved best results for biomass (R2 = 0.48), sensor data covering the SWIR from late April and June showed slightly better modeling results (EnMAP: R2 = 0.51, PRISMA: R2 = 0.53). Species composition was significantly related to spectral composition, with sensor data from late April showing the strongest relationships. The performance of sensors, including VNIR and VNIR-SWIR, was almost equal (e.g., DESIS: R2 = 0.54, PRISMA: R2 = 0.59). Overall, the results highlight the benefit of SWIR bands for biomass modeling, while their importance was minor in relation to species composition. A trend of improved model performance was observed with mean normalization for EnMAP and PRISMA data, while continuum removal led to a decrease in performance. Our study underscores the critical role of temporal and spectral data selection in improving grassland models, suggesting potential pathways for refining remote sensing approaches in ecological monitoring and management.
