Models of radiative transfer (RT) are important tools for remote sensing of vegetation, allowing for forward simulations of remotely sensed data as well as inverse estimation of biophysical and ...biochemical traits from vegetation optical properties. Estimation of foliar protein content is the key to monitor the nitrogen cycle in terrestrial ecosystems, in particular to assess the photosynthetic capacity of plants and to improve nitrogen management in agriculture. However, until now physically based leaf RT models have not allowed for proper spectral decomposition and estimation of leaf dry matter as nitrogen-based proteins and other carbon-based constituents (CBC) from optical properties of fresh and dry foliage. Such an achievement is the key for subsequent upscaling to canopy level and for development of new Earth observation applications.
Therefore, we developed a new version of the PROSPECT model, named PROSPECT-PRO, which separates the nitrogen-based constituents (proteins) from CBC (including cellulose, lignin, hemicellulose, starch and sugars). PROSPECT-PRO was calibrated and validated on subsets of the LOPEX dataset, accounting for both fresh and dry broadleaf and grass samples. We applied an iterative model inversion optimization algorithm and identified the optimal spectral ranges for retrieval of proteins and CBC. When combining leaf reflectance and transmittance within the selected optimal spectral domains, PROSPECT-PRO inversions revealed similarly accurate CBC estimates of fresh and dry leaf samples (respective validation R2 = 0.96 and 0.95, NRMSE = 9.6% and 13.4%), whereas a better performance was obtained for fresh than for dry leaves when estimating proteins (respective validation R2 = 0.79 and 0.57, NRMSE = 15.1% and 26.1%). The accurate estimation of leaf constituents for fresh samples is attributed to the optimal spectral feature selection procedure.
We further tested the ability of PROSPECT-PRO to estimate leaf mass per area (LMA) as the sum of proteins and CBC using independent datasets acquired for numerous plant species. Results showed that both PROSPECT-PRO and PROSPECT-D inversions were able to produce comparable LMA estimates across an independent dataset gathering 1685 leaf samples (validation R2 = 0.90 and NRMSE = 16.5% for PROSPECT-PRO, and R2 = 0.90 and NRMSE = 18.3% for PROSPECT-D). Findings also revealed that PROSPECT-PRO is capable of assessing the carbon-to‑nitrogen ratio based on the retrieved CBC-to-proteins ratio (R2 = 0.87 and NRMSE = 15.7% for fresh leaves, and R2 = 0.65 and NRMSE = 28.1% for dry leaves). The performance assessment of newly designed PROSPECT-PRO demonstrates a promising potential for its involvement in precision agriculture and ecological applications aiming at estimation of leaf carbon and nitrogen contents from observations of current and forthcoming airborne and satellite imaging spectroscopy sensors.
•Leaf dry matter is decomposed into proteins and carbon-based constituents in PROSPECT-PRO•The specific absorption coefficient of proteins revealed expected absorption features•Inversion of PROSPECT-PRO accurately estimated foliar protein content of dry and fresh leaves•Leaf nitrogen content may be quantified by estimating leaf protein content with PROSPECT-PRO•Carbon:nitrogen ratio was successfully estimated on leaves from the LOPEX dataset
Nitrogen (N) is considered as one of the most important plant macronutrients and proper management of N therefore is a pre-requisite for modern agriculture. Continuous satellite-based monitoring of ...this key plant trait would help to understand individual crop N use efficiency and thus would enable site-specific N management. Since hyperspectral imaging sensors could provide detailed measurements of spectral signatures corresponding to the optical activity of chemical constituents, they have a theoretical advantage over multi-spectral sensing for the detection of crop N. The current study aims to provide a state-of-the-art overview of crop N retrieval methods from hyperspectral data in the agricultural sector and in the context of future satellite imaging spectroscopy missions. Over 400 studies were reviewed for this purpose, identifying those estimating mass-based N (N concentration, N%) and area-based N (N content, Narea) using hyperspectral remote sensing data. Retrieval methods of the 125 studies selected in this review can be grouped into: (1) parametric regression methods, (2) linear nonparametric regression methods or chemometrics, (3) nonlinear nonparametric regression methods or machine learning regression algorithms, (4) physically-based or radiative transfer models (RTM), (5) use of alternative data sources (sun-induced fluorescence, SIF) and (6) hybrid or combined techniques. Whereas in the last decades methods for estimation of Narea and N% from hyperspectral data have been mainly based on simple parametric regression algorithms, such as narrowband vegetation indices, there is an increasing trend of using machine learning, RTM and hybrid techniques. Within plants, N is invested in proteins and chlorophylls stored in the leaf cells, with the proteins being the major nitrogen-containing biochemical constituent. However, in most studies, the relationship between N and chlorophyll content was used to estimate crop N, focusing on the visible-near infrared (VNIR) spectral domains, and thus neglecting protein-related N and reallocation of nitrogen to non-photosynthetic compartments. Therefore, we recommend exploiting the estimation of nitrogen via the proxy of proteins using hyperspectral data and in particular the short-wave infrared (SWIR) spectral domain. We further strongly encourage a standardization of nitrogen terminology, distinguishing between N% and Narea. Moreover, the exploitation of physically-based approaches is highly recommended combined with machine learning regression algorithms, which represents an interesting perspective for future research in view of new spaceborne imaging spectroscopy sensors.
•Most plant nitrogen is bound in proteins and only a small part in chlorophylls.•Parametric regressions and chemometrics were the most popular methods.•Machine learning and radiative transfer modelling are increasingly used.•Leaf RTMs with spectral contributions of proteins need to be further developed.
•PROSPECT-PRO model accounting for proteins is coupled with the 1-D SAIL model.•Gaussian processes (GP) identifies optimal spectral bands for nitrogen (N) sensing.•GP and PROSAIL-PRO modelling are ...combined for N retrieval at agricultural sites.•Heteroscedastic GP provides accurate estimations of crop N from leaves plus stalks.
Hyperspectral acquisitions have proven to be the most informative Earth observation data source for the estimation of nitrogen (N) content, which is the main limiting nutrient for plant growth and thus agricultural production. In the past, empirical algorithms have been widely employed to retrieve information on this biochemical plant component from canopy reflectance. However, these approaches do not seek for a cause-effect relationship based on physical laws. Moreover, most studies solely relied on the correlation of chlorophyll content with nitrogen, and thus neglected the fact that most N is bound in proteins. Our study presents a hybrid retrieval method using a physically-based approach combined with machine learning regression to estimate crop N content. Within the workflow, the leaf optical properties model PROSPECT-PRO including the newly calibrated specific absorption coefficients (SAC) of proteins, was coupled with the canopy reflectance model 4SAIL to PROSAIL-PRO. The latter was then employed to generate a training database to be used for advanced probabilistic machine learning methods: a standard homoscedastic Gaussian process (GP) and a heteroscedastic GP regression that accounts for signal-to-noise relations. Both GP models have the property of providing confidence intervals for the estimates, which sets them apart from other machine learners. Moreover, a GP-based sequential backward band removal algorithm was employed to analyze the band-specific information content of PROSAIL-PRO simulated spectra for the estimation of aboveground N. Data from multiple hyperspectral field campaigns, carried out in the framework of the future satellite mission Environmental Mapping and Analysis Program (EnMAP), were exploited for validation. In these campaigns, corn and winter wheat spectra were acquired to simulate spectral EnMAP data. Moreover, destructive N measurements from leaves, stalks and fruits were collected separately to enable plant-organ-specific validation. The results showed that both GP models can provide accurate aboveground N simulations, with slightly better results of the heteroscedastic GP in terms of model testing and against in situ N measurements from leaves plus stalks, with root mean square error (RMSE) of 2.1 g/m². However, the inclusion of fruit N content for validation deteriorated the results, which can be explained by the inability of the radiation to penetrate the thick tissues of stalks, corn cobs and wheat ears. GP-based band analysis identified optimal spectral settings with ten bands mainly situated in the shortwave infrared (SWIR) spectral region. Use of well-known protein absorption bands from the literature showed comparative results. Finally, the heteroscedastic GP model was successfully applied on airborne hyperspectral data for N mapping. We conclude that GP algorithms, and in particular the heteroscedastic GP, should be implemented for global agricultural monitoring of aboveground N from future imaging spectroscopy data.
The accelerated erosion of biodiversity is a critical environmental challenge. Operational methods for the monitoring of biodiversity taking advantage of remotely sensed data are needed in order to ...provide information to ecologists and decision‐makers.
We present an R package designed to compute a selection of α‐ and β‐diversity indicators from optical imagery, based on spectral variation hypothesis. This package builds upon previous work on biodiversity mapping using airborne imaging spectroscopy, and has been adapted in order to process broader range of data sources, including Sentinel‐2 satellite images.
biodivMapR is able to produce α‐diversity maps including Shannon and Simpson indices, as well as β‐diversity maps derived from Bray–Curtis dissimilarity. It is able to process large images efficiently with moderate computational requirements on a personal computer. Additional functions allow computing diversity indicators directly from field plots defined as polygon shapefiles for easy comparison with ground data and validation.
The package biodivMapR should contribute to improved standards for biodiversity mapping using remotely sensed data. It should also contribute to the identification of relevant Remotely Sensed enabled Essential Biodiversity Variables.
Leaf biochemical and structural traits are vegetation characteristics related to various physiological processes. Taking advantage of the physical relationship between optical properties and leaf ...biochemistry, field-based spectroscopy has allowed for the rapid estimation of leaf biochemical constituents and repeated non-destructive measurements through time. Leaf constituent retrieval from leaf optical properties following inversion of the physically-based radiative transfer model PROSPECT is now a popular method, but some cases prompt poor retrieval success and this approach requires a strict inversion procedure. We investigated the performances of different inversion procedures for the estimation of leaf constituents, specifically chlorophyll a and b, carotenoids, water (EWT), and dry matter (LMA) from >1400 broadleaf samples, including the definition of optimal spectral subdomains, and the use of leaf reflectance or transmittance alone. We also developed a strategy to obtain prior information on the leaf structure parameter (N) in PROSPECT, when only reflectance or transmittance is measured, and examined the influence of this prior information in combination with different inversion procedures. We found that using the full domain of reflectance or transmittance only systematically leads to suboptimal estimation of chlorophyll a and b, carotenoids, EWT, and LMA, due to either the combined absorption of multiple constituents or inaccurate estimation of the N parameter. Our study confirms that the selection of optimal spectral subdomains leads to improved estimation of all leaf constituents, from 700 to 720 nm for chlorophyll a and b, 520–560 nm for carotenoids, and from 1700 to 2400 nm for EWT and LMA. Prior information on N, computed directly from the spectra, leads to systematic improved estimation of leaf constituents when only reflectance or transmittance is measured, with reductions in normalized root mean square error from 8 to 37%. We strongly recommend using optimal subdomains when inverting PROSPECT to retrieve leaf constituents, and with the availability of only reflectance or transmittance we further recommend the use of prior information on the N parameter.
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•Optimal spectral subdomains improve the retrieval success of PROSPECT constituents.•Inversion on reflectance or transmittance is enhanced by leaf structure information.•Leaf structure is well estimated by NIR spectral band ratios.•Traditional and novel PROSPECT inversion approaches can be readily enhanced.
There is a growing need for operational biodiversity mapping methods to quantify and to assess the impact of climate change, habitat alteration, and human activity on ecosystem composition and ...function. Here, we present an original method for the estimation of α- and β-diversity of tropical forests based on high-fidelity imaging spectroscopy. We acquired imagery over high-diversity Amazonian tropical forest landscapes in Peru with the Carnegie Airborne Observatory and developed an unsupervised method to estimate the Shannon index (
H
′) and variations in species composition using Bray-Curtis dissimilarity (BC) and nonmetric multidimensional scaling (NMDS). An extensive field plot network was used for the validation of remotely sensed α- and β-diversity. Airborne maps of
H
′ were highly correlated with field α-diversity estimates (
r
= 0.86), and BC was estimated with demonstrable accuracy (
r
= 0.61-0.76). Our findings are the first direct and spatially explicit remotely sensed estimates of α- and β-diversity of humid tropical forests, paving the way for new applications using airborne and space-based imaging spectroscopy.
We used synthetic reflectance spectra generated by a radiative transfer model, PROSPECT-5, to develop statistical relationships between leaf optical and chemical properties, which were applied to ...experimental data without any readjustment. Four distinct synthetic datasets were tested: two unrealistic, uniform distributions and two normal distributions based on statistical properties drawn from a comprehensive experimental database. Two methods used in remote sensing to retrieve vegetation chemical composition, spectral indices and Partial Least Squares (PLS) regression, were trained both on the synthetic and experimental datasets, and validated against observations. Results are compared to a cross-validation process and model inversion applied to the same observations. They show that synthetic datasets based on normal distributions of actual leaf chemical and structural properties can be used to optimize remotely sensed spectral indices or other retrieval methods for analysis of leaf chemical constituents. This study concludes with the definition of several polynomial relationships to retrieve leaf chlorophyll content, carotenoid content, equivalent water thickness and leaf mass per area using spectral indices, derived from synthetic data and validated on a large variety of leaf types. The straightforward method described here brings the possibility to apply or adapt statistical relationships to any type of leaf.
► Leaf reflectance datasets are simulated using radiative transfer modeling. ► Different distributions of leaf chemical and structural properties are tested. ► Statistical relationships linking chemistry to simulated reflectance are adjusted. ► These relationships are successfully applied to experimental data. ► Simulated data help optimizing spectral indices already published.
Radiative transfer models have long been used to characterize the foliar content at the leaf and canopy levels. However, they still do not apply well to close-range imaging spectroscopy, especially ...because directional effects are usually not taken into account. For this purpose, we introduce a physical approach to describe and simulate the variation in leaf reflectance observed at this scale. Two parameters are thus introduced to represent (1) specular reflection at the leaf surface and (2) local leaf orientation. The model, called COSINE (ClOse-range Spectral ImagiNg of lEaves), can be coupled with a directional–hemispherical reflectance model of leaf optical properties to relate the measured reflectance to the foliar content. In this study, we show that, when combining COSINE with the PROSPECT model, the overall PROCOSINE model allows for a robust submillimeter retrieval of foliar content based on numerical inversion and pseudo-bidirectional reflectance factor hyperspectral measurements.
The relevance of the added parameters is first shown through a sensitivity analysis performed in the visible and near-infrared (VNIR) and shortwave infrared (SWIR) ranges. PROCOSINE is then validated based on VNIR and SWIR hyperspectral images of various leaf species exhibiting different surface properties. Introducing these two parameters within the inversion allows us to obtain accurate maps of PROSPECT parameters, e.g., the chlorophyll content in the VNIR range, and the equivalent water thickness and leaf mass per area in the SWIR range. Through the estimation of light incident angle, the PROCOSINE inversion also provides information on leaf orientation, which is a critical parameter in vegetation remote sensing.
•We propose a model for ClOse-range Spectral ImagiNg of lEaves (COSINE).•COSINE models the variability due to bidirectional effects and leaf orientation.•COSINE must be combined with a leaf directional–hemispherical model such as PROSPECT.•The overall PROCOSINE model is validated based on VNIR and SWIR close-range images.•Model inversion allows a submillimeter retrieval of leaf biochemistry and orientation.
Question
Which optical traits, retrieved from biophysical models applied to Sentinel‐2 images, enable an estimation of tree species diversity based on the Spectral Variation Hypothesis?
Location
...Coniferous mountain forest in the eastern Italian Alps.
Methods
We analyzed the PROSPECT‐5 and Invertible Forest Reflectance Model (INFORM) biophysical parameters as retrieved from canopy reflectance data of different forest plots (using Sentinel‐2 images for the years 2017, 2018 and 2019) as optical trait indicators (OTIs). We successively tested the Spectral Variation Hypothesis (SVH) for each retrieved OTI using the Rao's Q as heterogeneity index, validating them against Shannon's H values calculated as a tree species diversity index derived from in‐situ collected data.
Results
We demonstrated differences among OTIs in terms of how well their variations can be linked to species diversity. In particular the variations of brown pigments (Cbrown), carotenoids (Car) and chlorophyll content (Cab) can be considered the most relevant OTIs for the application of the SVH when using the Rao's Q as a proxy for tree species diversity in our study site.
Conclusions
This research underlined that the OTIs contribute differently in the SVH to estimate tree species diversity, highlighting significant positive correlations between tree species diversity and the spatial heterogeneity of the estimated pigment content (Cab, Car, Cbrown).
The heterogeneity of various OTIs (optical trait indicators; e.g., Cab chlorophyll content, and Car carotenoid content), derived from biophysical models (PROSPECT‐5 and INFORM) on different Sentinel‐2 images and calculated through the Rao's Q index, is related to the tree species diversity of a coniferous mountain forest in the eastern Italian Alps.
The Sentinel-2 mission of the European Space Agency (ESA) Copernicus program provides multispectral remote sensing data at decametric spatial resolution and high temporal resolution. The objective of ...this work is to evaluate the ability of Sentinel-2 time-series data to enable classification of an inherent biophysical property, in terms of accuracy and uncertainty estimation. The tested inherent biophysical property was the soil texture. Soil texture classification was performed on each individual Sentinel-2 image with a linear support vector machine. Two sources of uncertainty were studied: uncertainties due to the Sentinel-2 acquisition date and uncertainties due to the soil sample selection in the training dataset. The first uncertainty analysis was achieved by analyzing the diversity of classification results obtained from the time series of soil texture classifications, considering that the temporal resolution is akin to a repetition of spectral measurements. The second uncertainty analysis was achieved from each individual Sentinel-2 image, based on a bootstrapping procedure corresponding to 100 independent classifications obtained with different training data. The Simpson index was used to compute this diversity in the classification results. This work was carried out in an Indian cultivated region (84 km2, part of Berambadi catchment, in the Karnataka state). It used a time-series of six Sentinel-2 images acquired from February to April 2017 and 130 soil surface samples, collected over the study area and characterized in terms of texture. The classification analysis showed the following: (i) each single-date image analysis resulted in moderate performances for soil texture classification, and (ii) high confusion was obtained between neighboring textural classes, and low confusion was obtained between remote textural classes. The uncertainty analysis showed that (i) the classification of remote textural classes (clay and sandy loam) was more certain than classifications of intermediate classes (sandy clay and sandy clay loam), (ii) a final soil textural map can be produced depending on the allowed uncertainty, and iii) a higher level of allowed uncertainty leads to increased bare soil coverage. These results illustrate the potential of Sentinel-2 for providing input for modeling environmental processes and crop management.
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