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The continuous monitoring of the terrestrial Earth system by a growing number of optical satellite missions provides valuable insights into vegetation and cropland characteristics. Satellite missions typically provide different levels of data, such as level 1 top-of-atmosphere (TOA) radiance and level 2 bottom-of-atmosphere (BOA) reflectance products. Exploiting TOA radiance data directly offers the advantage of bypassing the complex atmospheric correction step, where errors can propagate and compromise the subsequent retrieval process. Therefore, the objective of our study was to develop models capable of retrieving vegetation traits directly from TOA radiance data from imaging spectroscopy satellite missions. To achieve this, we constructed hybrid models based on radiative transfer model (RTM) simulated data, thereby employing the vegetation SCOPE RTM coupled with the atmosphere LibRadtran RTM in conjunction with Gaussian process regression (GPR). The retrieval evaluation focused on vegetation canopy traits, including the leaf area index (LAI), canopy chlorophyll content (CCC), canopy water content (CWC), the fraction of absorbed photosynthetically active radiation (FAPAR), and the fraction of vegetation cover (FVC). Employing band settings from the upcoming Copernicus Hyperspectral Imaging Mission (CHIME), two types of hybrid GPR models were assessed: (1) one trained at level 1 (L1) using TOA radiance data and (2) one trained at level 2 (L2) using BOA reflectance data. Both the TOA- and BOA-based GPR models were validated against in situ data with corresponding hyperspectral data obtained from field campaigns. The TOA-based hybrid GPR models revealed a range of performance from moderate to optimal results, thus reaching R 2 = 0.92 (LAI), R 2 = 0.72 (CCC) and 0.68 (CWC), R 2 = 0.94 (FAPAR), and R 2 = 0.95 (FVC). To demonstrate the models' applicability, the TOA- and BOA-based GPR models were subsequently applied to imagery from the scientific precursor missions PRISMA and EnMAP. The resulting trait maps showed sufficient consistency between the TOA- and BOA-based models, with relative errors between 4 % and 16 % ( R 2 between 0.68 and 0.97). Altogether, these findings illuminate the path for the development and enhancement of machine learning hybrid models for the estimation of vegetation traits directly tailored at the TOA level. [ABSTRACT FROM AUTHOR]

Global mapping of essential vegetation traits (EVTs) through data acquired by Earth-observing satellites provides a spatially explicit way to analyze the current vegetation states and dynamics of our planet. Although significant efforts have been made, there is still a lack of global and consistently derived multi-temporal trait maps that are cloud-free. Here we present the processing chain for the spatiotemporally continuous production of four EVTs at a global scale: (1) fraction of absorbed photosynthetically active radiation (FAPAR), (2) leaf area index (LAI), (3) fractional vegetation cover (FVC), and (4) leaf chlorophyll content (LCC). The proposed workflow presents a scalable processing approach to the global cloud-free mapping of the EVTs. Hybrid retrieval models, named S3-TOA-GPR-1.0-WS, were implemented into Google Earth Engine (GEE) using Sentinel-3 Ocean and Land Color Instrument (OLCI) Level-1B for the mapping of the four EVTs along with associated uncertainty estimates. We used the Whittaker smoother (WS) for the temporal reconstruction of the four EVTs, which led to continuous data streams, here applied to the year 2019. Cloud-free maps were produced at 5 km spatial resolution at 10-day time intervals. The consistency and plausibility of the EVT estimates for the resulting annual profiles were evaluated by per-pixel intra-annually correlating against corresponding vegetation products of both MODIS and Copernicus Global Land Service (CGLS). The most consistent results were obtained for LAI, which showed intra-annual correlations with an average Pearson correlation coefficient (R) of 0.57 against the CGLS LAI product. Globally, the EVT products showed consistent results, specifically obtaining higher correlation than R > 0.5 with reference products between 30 and 60° latitude in the Northern Hemisphere. Additionally, intra-annual goodness-of-fit statistics were also calculated locally against reference products over four distinct vegetated land covers. As a general trend, vegetated land covers with pronounced phenological dynamics led to high correlations between the different products. However, sparsely vegetated fields as well as areas near the equator linked to smaller seasonality led to lower correlations. We conclude that the global gap-free mapping of the four EVTs was overall consistent. Thanks to GEE, the entire OLCI L1B catalogue can be processed efficiently into the EVT products on a global scale and made cloud-free with the WS temporal reconstruction method. Additionally, GEE facilitates the workflow to be operationally applicable and easily accessible to the broader community. [ABSTRACT FROM AUTHOR]

In preparation for new-generation imaging spectrometer missions and the accompanying unprecedented inflow of hyperspectral data, optimized models are needed to generate vegetation traits routinely. Hybrid models, combining radiative transfer models with machine learning algorithms, are preferred, however, dealing with spectral collinearity imposes an additional challenge. In this study, we analyzed two spectral dimensionality reduction methods: principal component analysis (PCA) and band ranking (BR), embedded in a hybrid workflow for the retrieval of specific leaf area (SLA), leaf area index (LAI), canopy water content (CWC), canopy chlorophyll content (CCC), the fraction of absorbed photosynthetic active radiation (FAPAR), and fractional vegetation cover (FVC). The SCOPE model was used to simulate training data sets, which were optimized with active learning. Gaussian process regression (GPR) algorithms were trained over the simulations to obtain trait-specific models. The inclusion of PCA and BR with 20 features led to the so-called GPR-20PCA and GPR-20BR models. The 20PCA models encompassed over 99.95% cumulative variance of the full spectral data, while the GPR-20BR models were based on the 20 most sensitive bands. Validation against in situ data obtained moderate to optimal results with normalized root mean squared error (NRMSE) from 13.9% (CWC) to 22.3% (CCC) for GPR-20PCA models, and NRMSE from 19.6% (CWC) to 29.1% (SLA) for GPR-20BR models. Overall, the GPR-20PCA slightly outperformed the GPR-20BR models for all six variables. To demonstrate mapping capabilities, both models were tested on a PRecursore IperSpettrale della Missione Applicativa (PRISMA) scene, spectrally resampled to Copernicus Hyperspectral Imaging Mission for the Environment (CHIME), over an agricultural test site (Jolanda di Savoia, Italy). The two strategies obtained plausible spatial patterns, and consistency between the two models was highest for FVC and LAI ( R 2 = 0.91 , R 2 = 0.86 ) and lowest for SLA mapping ( R 2 = 0.53 ). From these findings, we recommend implementing GPR-20PCA models as the most efficient strategy for the retrieval of multiple crop traits from hyperspectral data streams. Hence, this workflow will support and facilitate the preparations of traits retrieval models from the next-generation operational CHIME. [ABSTRACT FROM AUTHOR]

Thanks to the emergence of cloud-computing platforms and the ability of machine learning methods to solve prediction problems efficiently, this work presents a workflow to automate spatiotemporal mapping of essential vegetation traits from Sentinel-3 (S3) imagery. The traits included leaf chlorophyll content (LCC), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fractional vegetation cover (FVC), being fundamental for assessing photosynthetic activity on Earth. The workflow involved Gaussian process regression (GPR) algorithms trained on top-of-atmosphere (TOA) radiance simulations generated by the coupled canopy radiative transfer model (RTM) SCOPE and the atmospheric RTM 6SV. The retrieval models, named to S3-TOA-GPR-1.0, were directly implemented in Google Earth Engine (GEE) to enable the quantification of the traits from TOA data as acquired from the S3 Ocean and Land Colour Instrument (OLCI) sensor. Following good to high theoretical validation results with normalized root mean square error (NRMSE) ranging from 5% (FAPAR) to 19% (LAI), a three fold evaluation approach over diverse sites and land cover types was pursued: (1) temporal comparison against LAI and FAPAR products obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) for the time window 2016–2020, (2) spatial difference mapping with Copernicus Global Land Service (CGLS) estimates, and (3) direct validation using interpolated in situ data from the VALERI network. For all three approaches, promising results were achieved. Selected sites demonstrated coherent seasonal patterns compared to LAI and FAPAR MODIS products, with differences between spatially averaged temporal patterns of only 6.59%. In respect of the spatial mapping comparison, estimates provided by the S3-TOA-GPR-1.0 models indicated highest consistency with FVC and FAPAR CGLS products. Moreover, the direct validation of our S3-TOA-GPR-1.0 models against VALERI estimates indicated good retrieval performance for LAI, FAPAR and FVC. We conclude that our retrieval workflow of spatiotemporal S3 TOA data processing into GEE opens the path towards global monitoring of fundamental vegetation traits, accessible to the whole research community. [ABSTRACT FROM AUTHOR]

With an upcoming unprecedented stream of imaging spectroscopy data, there is a rising need for tools and software applications exploiting the spectral possibilities to extract relevant information on an operational basis. In this study, we investigate the potential of a scientific processor designed to quantify biophysical and biochemical crop traits from spectroscopic imagery of the upcoming Environmental Mapping and Analysis Program (EnMAP) satellite. Said processor relies on a hybrid retrieval workflow executing pre-trained machine learning regression models fast and efficiently based on training data from a lookup table of synthetic vegetation spectra and their associated parameterization of the well-known radiative transfer model (RTM) PROSAIL. The established models provide spatial information about leaf area index (LAI), average leaf inclination angle (ALIA), leaf chlorophyll content (C ab) and leaf mass per area (C m). In contrast to using site-specific training data, the approach facilitates a universal application without the need to integrate a priori information into the processor. Four machine learning algorithms, namely artificial neural networks (ANN), random forest regression (RFR), support vector machine regression (SVR), and Gaussian process regression (GPR), were found to estimate biophysical and biochemical variables of unseen targets with high performance (relative error scores < 10%). ANNs excelled in terms of accuracy, model size and execution time when the 242 spectral bands were transformed into 15 principal components, the signals of which were scaled by a z-transformation. Validation using in situ data from the SPARC03 Barrax campaign dataset revealed an overall good estimation of measured functional traits, for instance for LAI with root mean squared error (RMSE) of 0.81 m2 m−2, and for C ab RMSE of 6.2 µg cm−2 with the ANN model. Moreover, both crop traits could be successfully mapped using a pseudo-EnMAP scene revealing plausible within-field patterns. Conformity with LAI output of the SNAP biophysical processor was found especially for grassland and maize in the vegetative stages. Based on these findings, ANN models are considered the best choice for implementation of a hybrid retrieval workflow within the context of operational agricultural crop traits monitoring from future satellite imaging spectroscopy. [ABSTRACT FROM AUTHOR]

Remote sensing (RS) of biophysical variables plays a vital role in providing the information necessary for understanding spatio-temporal dynamics in ecosystems. The hybrid approach to retrieve biophysical variables from RS by combining Machine Learning (ML) algorithms with surrogate data generated by Radiative Transfer Models (RTM). The susceptibility of the ill-posed solutions to noise currently constrains further application of hybrid approaches. Here, we explored how noise affects the performance of ML algorithms for biophysical trait retrieval. We focused on synthetic Sentinel-2 (S2) data generated using the PROSAIL RTM and four commonly applied ML algorithms: Gaussian Processes (GPR), Random Forests (RFR), and Artificial Neural Networks (ANN) and Multi-task Neural Networks (MTN). After identifying which biophysical variables can be retrieved from S2 using a Global Sensitivity Analysis, we evaluated the performance loss of each algorithm using the Mean Absolute Percentage Error (MAPE) with increasing noise levels. We found that, for S2 data, Carotenoid concentrations are uniquely dependent on band 2, Chlorophyll is almost exclusively dependent on the visible ranges, and Leaf Area Index, water, and dry matter contents are mostly dependent on infrared bands. Without added noise, GPR was the best algorithm (<0.05%), followed by the MTN (<3%) and ANN (<5%), with the RFR performing very poorly (<50%). The addition of noise critically affected the performance of all algorithms (>20%) even at low levels of added noise (≈5%). Overall, both neural networks performed significantly better than GPR and RFR when noise was added with the MTN being slightly better when compared to the ANN. Our results imply that the performance of the commonly used algorithms in hybrid-RTM inversion are pervasively sensitive to noise. The implication is that more advanced models or approaches are necessary to minimize the impact of noise to improve near real-time and accurate RS monitoring of biophysical trait retrieval. [ABSTRACT FROM AUTHOR]

Conventional methods of plant nutrient estimation for nutrient management need a huge number of leaf or tissue samples and extensive chemical analysis, which is time-consuming and expensive. Remote sensing is a viable tool to estimate the plant's nutritional status to determine the appropriate amounts of fertilizer inputs. The aim of the study was to use remote sensing to characterize the foliar nutrient status of mango through the development of spectral indices, multivariate analysis, chemometrics, and machine learning modeling of the spectral data. A spectral database within the 350–1050 nm wavelength range of the leaf samples and leaf nutrients were analyzed for the development of spectral indices and multivariate model development. The normalized difference and ratio spectral indices and multivariate models–partial least square regression (PLSR), principal component regression, and support vector regression (SVR) were ineffective in predicting any of the leaf nutrients. An approach of using PLSR-combined machine learning models was found to be the best to predict most of the nutrients. Based on the independent validation performance and summed ranks, the best performing models were cubist (R2 ≥ 0.91, the ratio of performance to deviation (RPD) ≥ 3.3, and the ratio of performance to interquartile distance (RPIQ) ≥ 3.71) for nitrogen, phosphorus, potassium, and zinc, SVR (R2 ≥ 0.88, RPD ≥ 2.73, RPIQ ≥ 3.31) for calcium, iron, copper, boron, and elastic net (R2 ≥ 0.95, RPD ≥ 4.47, RPIQ ≥ 6.11) for magnesium and sulfur. The results of the study revealed the potential of using hyperspectral remote sensing data for non-destructive estimation of mango leaf macro- and micro-nutrients. The developed approach is suggested to be employed within operational retrieval workflows for precision management of mango orchard nutrients. [ABSTRACT FROM AUTHOR]

The new-generation satellite imaging spectrometers provide an unprecedented data stream to be processed into quantifiable vegetation traits. Hybrid models have gained widespread acceptance in recent years due to their versatility in converting spectral data into traits. In hybrid models, the retrieval is obtained through a machine learning regression algorithm (MLRA) trained on a wide range of simulated data. For instance, they are currently under development for trait retrieval in preparation for the upcoming Copernicus Hyperspectral Imaging Mission for the Environment (CHIME), among others targeting routine estimation of canopy nitrogen content (CNC). However, like any retrieval algorithm, the process is not error-free, and most MLRAs inherently lack an uncertainty estimation related to the retrieved traits, which implies a risk of misinterpretation when applying the model to real-world data. Therefore, this study aimed to assess epistemic uncertainty estimation strategies (Bayesian method, drop-out, quantile regression, and bootstrapping) alongside the estimation of CNC using competitive MLRAs. Each of the regression models was evaluated using three data sets: (1) simulated scenes with varying noise using the SCOPE 2.1 radiative transfer model, (2) hyperspectral images from the PRISMA sensor, and (3) field-measured data. Analysis of generated uncertainty intervals led to the following findings: First, Gaussian processes regression (GPR) offers meaningful uncertainties, primarily attributable to spectral data degradation, which provide supplementary insights into the quality of trait mapping. Second, bootstrapping uncertainties can be used as quality indicators of the reliability of the estimates retrieved by hybrid models. Yet, its variability depends on the used MLRA, which impedes trusting its variance as a confidence interval. Third, quantile regression forest (QRF), despite not being top-performing algorithm, exhibit outstanding robustness estimations and uncertainty when the spectral data is degraded, either by Gaussian noise or by striping, often occurring in satellite imagery. Fourth, bootstrapped kernel ridge regression (KRR) demonstrated comparable performance to the benchmark algorithm GPR; the retrievals and uncertainties of these two MLRAs were highly correlated. Fifth, bootstrapped partial least squares regression (PLSR) estimations and uncertainties exhibit poor robustness to noise degradation, with normalized root mean square error (NRMSE) increasing from 19% to 112%. Additionally, a GUI tool was integrated into the ARTMO software package for assessing epistemic uncertainties from the embedded regression algorithms, providing a trait mapping quality indicator for mapping applications, and improving decision-making. [Display omitted] • Quantified uncertainty provides insight in pixel retrieval from imaging spectroscopy. • Great variability in epistemic uncertainty depends on the ML algorithm. • Bootstrapped KRR and Bayesian GPR achieved top estimates and uncertainty. • PLSR is weak to noise degradation and is unreliable for spatiotemporal transference. • Bootstrapping, Bayesian and dropout uncertainties serve as quality mapping indicators. [ABSTRACT FROM AUTHOR]

• We reviewed optical remote sensing literature on non-photosynthetic vegetation (NPV). • NPV can be detected and quantified thanks to narrow absorption features in the SWIR. • Spectral unmixing analysis and vegetation indices dominate NPV application studies. • NPV identified as a key quantifiable trait by upcoming imaging spectrometer missions. • Operational monitoring of NPV biomass (in g/m2) becomes possible with hybrid models. Non-photosynthetic vegetation (NPV) is considered a key quantifiable variable in the context of new spaceborne imaging spectroscopy missions. Knowledge of NPV is essential for all terrestrial ecosystems, and its mapping is beneficial for agriculture and forestry. In agriculture, crop residues (CR) play an important role in tillage, erosion control and soil health management. In forest management, NPV mapping supports understanding the dynamics of important ecological processes such as fire, erosion, and land use changes. Whereas the fractional cover of NPV has been extensively studied across managed and natural ecosystems, so far little attention has been paid to the quantification of biomass of senescent material. In this comprehensive survey, we summarize past attempts to quantify landscape-scale NPV or CR cover (in %) and biomass (in g/m2) from optical Earth observation data, with a particular focus on hyperspectral data exploitation. Given three decades of spectroscopy studies on NPV or CR cover detection, we identify the following methodological trends: (1) a shift from unmixing approaches towards regression-based models; (2) a shift from two-band indices towards multi-band equations; and (3) a shift from linear regression towards data-driven machine learning models. (4) In addition, gradual progress in radiative transfer modelling (RTM) in describing the interaction of radiation with non-photosynthetic plant material has been achieved. These trends have enabled progress from merely identifying the presence of NPV and CR to the explicit quantification of NPV biomass over croplands and grasslands. We highlight the potential of recent and upcoming next-generation spaceborne missions and their unprecedented hyperspectral and enhanced multispectral data streams and propose to implement efficient workflows for the operational delivery of global NPV products along with associated uncertainties. In summary, this survey emphasizes the significance of imaging spectroscopy as an efficient way to quantify NPV and support the management of croplands, grasslands, forests, and natural vegetation. [ABSTRACT FROM AUTHOR]