Model-Based Optimization of Spectral Sampling for the Retrieval of Crop Variables with the PROSAIL Model

Berger, Katja; Atzberger, Clement; Danner, Martin; Wocher, Matthias; Mauser, Wolfram; Hank, Tobias

doi:10.3390/rs10122063

Cited by 37 publications

(26 citation statements)

References 47 publications

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“…Besides one red edge and one NIR band, in most cases NN tended to select several additional NIR bands. This is consistent with observations of many studies; namely, in addition to a red edge band the most often selected bands for LAI estimation by NN were located in the NIR region (Richter et al, 2012; Verger et al, 2011; Verrelst et al, 2013, 2016; Berger et al, 2018). The inclusion of additional NIR bands may better indicate multiple and complex canopy scattering related to crop structure (monoculture and row structure).…”

Section: Discussionsupporting

confidence: 90%

Gross Primary Production Estimation in Crops Using Solely Remotely Sensed Data

Kira

Nguy-Robertson

et al. 2019

Agronomy Journal

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Gross primary production (GPP) is a measure for crop productivity, indicating yield and expressing C exchange of agro-ecosystems. A multitude of satellite sensors at varying spatial and spectral resolution brings a possibility to use remotely sensed data for regional and global GPP estimation. More work is still needed to develop algorithms for GPP estimation applicable to multiple, if not all, vegetation types, phenological phases, and environmental conditions. This study employed neural networks (NN), multiple linear regressions (MLR), and vegetation indices (VI) to develop algorithms for GPP estimation based solely on remotely sensed data in two crops, maize (Zea mays L.) and soybean [Glycine max (L.) Merr.], with contrasting canopy architectures, leaf structures, and photosynthetic pathways. The focus of the study was to devise algorithms not requiring re-parameterization for different crop species. Data used in the models included in situ hyperspectral reflectance and satellite surface reflectance products. For the tested NN, MLR, and VI algorithms, the bands selected to obtain minimal errors in maize and soybean combined were mainly located in red edge and near infrared (NIR) spectral regions. For both in situ reflectance and satellite surface reflectance, a NN estimated GPP with normalized root mean square errors (NRMSE) below 14 and 18.7%, respectively, and VI using bands in red edge and NIR with NRMSE 15.6 and 20.4%, respectively. The results showed that the models based on the red edge and NIR bands may facilitate accurate assessments of crop GPP at multiple scales, from close range to satellite platforms. core Ideas• The models using remotely sensed data allow accurate estimation of gross primary production in two crops. • The optimal bands for gross primary production estimation in two crops were in near infrared and red edge regions. • Vegetation indices with red edge and near infrared reflectance were generic for maize and soybean. G ross primary production is the amount of biomass C that plants produce in a given period of time. As such, it is the sum of the net primary production (NPP), that is, the net amount of useful biomass C produced by plants, and plant respiration (Re). We have quantified the relationship of cumulative growing season GPP (g C m -2 season -1 ) and grain yield (g C m -2 season -1 ) at the Nebraska AmeriFlux sites (US-Ne1, US-Ne2, and US-Ne3). The coefficient of determination (R 2 ) for this relationship over the first 14 yr of the project is 0.95. This includes both maize and soybean grown under irrigated and rainfed conditions and encompasses a wide variation in environmental conditions. Grain yield is arguably the single most important consideration for agricultural producers and, due to its close correlation with GPP, measurements of GPP are of great interest for evaluating crop yield (e.g., Reeves et al., 2005). Traditionally, field studies use tower eddy covariance systems (e.g., Baldocchi, 2003) to obtain GPP in croplands. However, such approaches bring results over a l...

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Section: Discussionsupporting

confidence: 90%

Gross Primary Production Estimation in Crops Using Solely Remotely Sensed Data

Kira

Nguy-Robertson

et al. 2019

Agronomy Journal

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“…When methods are trained on such synthetic data but applied to real data, we often observe a systematic bias. Even though PROSAIL is the most prominent canopy reflectance model for the inversion of vegetation spectral data [47], only few studies have focused on its capability to reproduce field spectral observations [51,59,60]. These observations are instead commonly used as validation data for retrieval methods assuming that errors of the inversion indicate a weak model performance.…”

Section: Descriptionmentioning

confidence: 99%

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

et al. 2019

Self Cite

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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.

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“…Because chlorophyll does not directly affect NIR reflectance, the potentially stronger sensitivity of broad red band canopy ρ to CCC is supposed to be the reason for the observed stronger sensitivity of broad-band EVI to CCC. Different GSA results for EVI from broad-and narrow-band sensors indicate that band settings (central wavelength and full width at half maximum, FWHM) may change the sensitivity of a VI and should be considered for applications with multi-sensor analysis and future spaceborne sensor design [29].…”

Section: Resultsmentioning

confidence: 99%

A Global Sensitivity Analysis of Commonly Used Satellite-Derived Vegetation Indices for Homogeneous Canopies Based on Model Simulation and Random Forest Learning

Wang

Dong

et al. 2019

Remote Sensing

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Remote sensing (RS) provides operational monitoring of terrestrial vegetation. For optical RS, vegetation information is generally derived from surface reflectance (ρ). More generally, vegetation indices (VIs) are built on the basis of ρ as proxies for vegetation traits. At canopy level, ρ can be affected by a variety of factors, including leaf constituents, canopy structure, background reflectivity, and sun-sensor geometry. Consequently, VIs are mixtures of different information. In this study, a global sensitivity analysis (GSA) is made for several commonly used satellite-derived VIs in order to better understand the application of these VIs at large scales. The sensitivities of VIs to different parameters are analyzed on the basis of PROSPECT-SAIL (PROSAIL) radiative transfer model simulations, which apply for homogeneous canopies, and random forest (RF) learning. Specifically, combined factors such as canopy chlorophyll content (CCC) and canopy water content (CWC) are introduced in the RF-based GSA. We find that for most VIs, the leaf area index is the most influential factor, while the broad-band sensor-derived enhanced VI (EVI) exhibits a strong sensitivity to CCC, and the universal normalized VI (UNVI) is sensitive to CWC. The potential and uncertainty for the application of all the considered VIs are analyzed according to the GSA results. The results can help to improve the use of VIs in different contexts, and the RF-based GSA method can be further applied in more sophisticated situations.

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Model-Based Optimization of Spectral Sampling for the Retrieval of Crop Variables with the PROSAIL Model

Cited by 37 publications

References 47 publications

Gross Primary Production Estimation in Crops Using Solely Remotely Sensed Data

Gross Primary Production Estimation in Crops Using Solely Remotely Sensed Data

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

A Global Sensitivity Analysis of Commonly Used Satellite-Derived Vegetation Indices for Homogeneous Canopies Based on Model Simulation and Random Forest Learning

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