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

Abstract

Decades after release of the first PROSPECT + SAIL (commonly called PROSAIL) versions, the model is still the most famous representative in the field of canopy reflectance modelling and has been widely used to obtain plant biochemical and structural variables, particularly in the agricultural context. The performance of the retrieval is usually assessed by quantifying the distance between the estimated and the in situ measured variables. While this has worked for hundreds of studies that obtained canopy density as a one-sided Leaf Area Index (LAI) or pigment content, little is known about the role of the canopy geometrical properties specified as the Average Leaf Inclination Angle (ALIA). In this study, we exploit an extensive field dataset, including narrow-band field spectra, leaf variables and canopy properties recorded in seven individual campaigns for winter wheat (4x) and silage maize (3x). PROSAIL outputs generally did not represent field spectra well, when in situ variables served as input for the model. A manual fitting of ALIA and leaf water (EWT) revealed significant deviations for both variables (RMSE = 14.5°, 0.020 cm) and an additional fitting of the brown leaf pigments (Cbrown) was necessary to obtain matching spectra at the near infrared (NIR) shoulder. Wheat spectra tend to be underestimated by the model until the emergence of inflorescence when PROSAIL begins to overestimate crop reflectance. This seasonal pattern could be attributed to an attenuated development of ALIAopt compared to in situ measured ALIA. Segmentation of nadir images of wheat was further used to separate spectral contributors into dark background, ears and leaves + stalks. It could be shown that the share of visible fruit ears from nadir view correlates positively with the deviations between field spectral measurement and PROSAIL spectral outputs (R² = 0.78 for aggregation by phenological stages), indicating that retrieval errors increase for ripening stages. An appropriate model parameterization is recommended to assure accurate retrievals of biophysical and biochemical products of interest. The interpretation of inverted ALIA as physical leaf inclinations is considered unfeasible and we argue in favour of treating it as a free calibration parameter.

VIS-NIR, Red-Edge and NIR-Shoulder Based Normalized Vegetation Indices Response to Co-Varying Leaf and Canopy Structural Traits in Heterogeneous Grasslands

“…In this study, the PROSAIL RTM [52] was used to assess the influence of leaf and canopy PTs on the SVIs calculated based on VIS, RE and NIR-shoulder spectral regions. PROSAIL couples two separate models: (a) the PROSPECT leaf optical model; and (b) the SAIL canopy reflectance model [28]. PROSAIL can simulate the canopy bidirectional reflectance in the spectral range between 400 and 2500 nm as a function of up to 16 input parameters.…”

“…In addition, RTMs are mainly focused on chlorophyll and not on other pigments such as brown pigment content (polyphenols; Cbrown) which play an important role in shaping the spectral response of grasslands at varying phenological stages. This trait is rather poorly analyzed in the literature and the RTM parameter itself lacks of a proper physically meaning, and it is thus not measurable with field observations [28].…”

OBIA4RTM – towards an operational open-source solution for coupling object-based image analysis with radiative transfer modelling

“…Since the parametrization of ProSAIL is crucial for the quality of the LAI derivation, in-situ values for the parametrization were used, which were obtained by Danner et al (2019) at the MNI testsite. A LUT for maize and a LUT for the two winter cereals (winter wheat and triticale) with 240,000 and 562,500 reference spectra, respectively, was created for each image acquisition date according to Table 2.…”

“…In this study the parameters were obtained from in-situ measurements taken at the MNI test area. However, Danner et al (2019) found that the use of in-situ parameters in ProSAIL alone does not provide optimal results and manual adjustments are necessary to achieve higher retrieval accuracy. Since such a manual adjustments were omitted for reasons of time, there is further potential for error.…”

“…Due to their unrestricted availability, global coverage and reasonable temporal resolution, the group of medium-resolution optical datasets (in particular Landsat-8 and Sentinel-2, but more experimental instruments such as PROBA-V) are considered a valuable source of information in vegetation studies. The most relevant parameters on physical and chemical vegetation properties, as derived from optical imagery include: (i) leaf area index (LAI) (Colwell, 1974;Garrigues et al, 2008;Zheng & Moskal, 2009); (ii) leaf chlorophyll and water content (Daughtry et al, 2000;Gitelson & Merzlyak, 1997;Tucker, 1980); (iii) leaf incidence angle (Danner et al, 2019); (iv) fraction of brown leaves (Verhoef & Bach, 2003). Assessing these parameters in a reliable and repeatable manner from space allows for near real-time monitoring of plant dynamics.…”