Abstract
Satellite hyperspectral Earth observation missions have strong potential to support sustainable agriculture by providing accurate spatial and temporal information of important vegetation biophysical and biochemical variables. To meet this goal, possible error sources in the modelling approaches should be minimized. Thus, first of all, the capability of a model to reproduce the measured spectral signals has to be tested before applying any retrieval algorithm. For an exemplary demonstration, the coupled PROSPECT-D and SAIL radiative transfer models (PROSAIL) were employed to emulate the setup of future hyperspectral sensors in the visible and near-infrared (VNIR) spectral regions with a 6.5 nm spectral sampling distance. Model uncertainties were determined to subsequently exclude those wavelengths with the highest mean absolute error (MAE) between model simulation and spectral measurement. The largest mismatch could be found in the green visible and red edge regions, which can be explained by complex interactions of several biochemical and structural variables in these spectral domains. For leaf area index (LAI, m2·m−2) retrieval, results indicated only a small improvement when using optimized spectral samplings. However, a significant increase in accuracy for leaf chlorophyll content (LCC, µg·cm−2) estimations could be obtained, with the relative root mean square error (RMSE) decreasing from 26% (full VNIR range) to 15% (optimized VNIR) for maize and from 77% to 29% for soybean, respectively. We therefore recommend applying a specific model-error threshold (MAE of ~0.01) to stabilize the retrieval of crop biochemical variables.
Highlights
Worldwide, there is an increasing interest in and need to optimize agricultural management systems to enhance yields while minimizing environmental impacts [1]
Since a spectral sampling distance of 6.5 nm represents a good compromise between several upcoming hyperspectral sensors, ranging from 2.55 nm (DESIS) over 6.5 nm (EnMAP) to 12 nm (PRISMA), Environmental Mapping and Analysis Program (EnMAP) band-specific spectral response functions with 6.5 nm spectral sampling distance were applied to the field spectrometer data in the visible and near-infrared (VNIR) domain (Nsamples = 73 bands in the spectral range 423–863 nm)
Note that our study provides results from the VNIR region only, which includes the absorption spectra of leaf pigments (e.g., LCC) and the NIR shoulder that is very sensitive to canopy structure effects (i.e., LAI)
Summary
There is an increasing interest in and need to optimize agricultural management systems to enhance yields while minimizing environmental impacts [1]. Considering the high spectral information content that will be provided by the new sensors, physically based retrieval techniques combined with flexible and computationally efficient machine-learning regression algorithms (MLRAs) can strongly support the effective exploitation of the full spectral signals. This can be realized, for instance, by employing radiative transfer models (RTM) such as the well-known and widely used canopy bidirectional reflectance model 4SAIL [10] and the PROSPECT-D leaf optical properties model [11]. LUTs serve as training databases for MLRAs within hybrid retrieval schemes [8]
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