Physically-Based Retrieval of Canopy Equivalent Water Thickness Using Hyperspectral Data

Abstract

Quantitative equivalent water thickness on canopy level (EWT canopy ) is an important land surface variable and retrieving EWT canopy from remote sensing has been targeted by many studies. However, the effect of radiative penetration into the canopy has not been fully understood. Therefore, in this study the Beer-Lambert law is applied to inversely determine water content information in the 930 to 1060 nm range of canopy reflectance from measured winter wheat and corn spectra collected in 2015, 2017, and 2018. The spectral model was calibrated using a look-up-table (LUT) of 50,000 PROSPECT spectra. Internal model validation was performed using two leaf optical properties datasets (LOPEX93 and ANGERS). Destructive in-situ measurements of water content were collected separately for leaves, stalks, and fruits. Correlation between measured and modelled water content was most promising for leaves and ears in case of wheat, reaching coefficients of determination (R 2 ) up to 0.72 and relative RMSE (rRMSE) of 26% and in case of corn for the leaf fraction only (R 2 = 0.86, rRMSE = 23%). These findings indicate that, depending on the crop type and its structure, different parts of the canopy are observed by optical sensors. The results from the Munich-North-Isar test sites indicated that plant compartment specific EWT canopy allows us to deduce more information about the physical meaning of model results than from equivalent water thickness on leaf level (EWT) which is upscaled to canopy water content (CWC) by multiplication of the leaf area index (LAI). Therefore, it is suggested to collect EWT canopy data and corresponding reflectance for different crop types over the entire growing cycle. Nevertheless, the calibrated model proved to be transferable in time and space and thus can be applied for fast and effective retrieval of EWT canopy in the scope of future hyperspectral satellite missions.

Fitted PROSAIL Parameterization of Leaf Inclinations, Water Content and Brown Pigment Content for Winter Wheat and Maize Canopies

“…The interleaving of foliage may lead to an additional vertical stacking of biomass and exaggeration of biophysical and biochemical features in the model. Inversion of PROSPECT alone was shown to produce adequate results for estimation of EWT [99][100][101], whereas the proper retrieval from complex canopy spectra remains a difficult task [102,103]. Another solution to this problem could be crop-and phenology-specific calibration curves if further research confirms the relationship between EWT and EWT opt found for wheat in the four seasons presented.…”

A Survey of Active Learning for Quantifying Vegetation Traits from Terrestrial Earth Observation Data

“…For corn, sampling was done at the following dates: 13 [10]. Further, the studies by Danner et al [102] and Wocher et al [99] inform about sampling design, size and location of ESUs, as well as measurements of other biochemical and biophysical variables.…”

Quantifying the Robustness of Vegetation Indices through Global Sensitivity Analysis of Homogeneous and Forest Leaf-Canopy Radiative Transfer Models

“…Characteristics are available in Table 1. Despite the number of missions currently under development, the choice of the EnMAP sensor is due to the large amount of information available from a wide variety of articles [67][68][69][70][71][72][73], in addition to all the possibilities offered by its large number of bands. Although this mission has not been launched yet, it is of interest to analyze the sensitivity of EnMAP-suited VIs in preparation of future vegetation monitoring applications.…”