Katja Berger

Parc Científic de la Universitat de València

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Jochem Verrelst

Parc Científic de la Universitat de València

Tobias Hank

Ludwig-Maximilians-Universität München

Matthias Wocher

Ludwig-Maximilians-Universität München

Wolfram Mauser

Ludwig-Maximilians-Universität München

Santiago Belda

University of Alicante

Martin Danner

Georgetown University

Pablo Reyes-Muñoz

Parc Científic de la Universitat de València

Juan Pablo Rivera

Universidad Autónoma de Nayarit

Gerbrand Koren

Utrecht University

Enrico Tomelleri

Free University of Bozen-Bolzano

Cooperative Institutions

Ludwig-Maximilians-Universität München

Parc Científic de la Universitat de València

Universitat de València

Helmholtz Centre Potsdam - GFZ German Research Centre for Geosciences

Heinrich Heine University Düsseldorf

Klinik für Frauenheilkunde

Helmholtz Zentrum München

Heidelberg University

Universität Hamburg

University Medical Center Hamburg-Eppendorf

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Hyperspectral Retrieval of Canopy Water Content Through Inversion of the Beer-Lambert Law

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium (2018)

Matthias Wocher Katja Berger Martin Danner Wolfram Mauser Tobias Hank

The retrieval of quantitative equivalent water thickness on canopy level (EWTc) is an agriculturally important task for hyperspectral remote sensing. In this study the Beer-Lambert law is applied to inversely determine water content from measured winter wheat spectra collected in 2015 and 2017. The spectral model is calibrated using a look-up-table (LUT) of 50.000 PROSPECT spectra. Validation was performed using two leaf optical properties datasets (LOPEX93 and ANGERS) and in-situ data acquired in Southern Germany. After considering destructive in-situ water content measurements separately for leaves, stems, and fruits, results indicate optically active plant water by plant component in the 930 to 1060 nm range of canopy reflectance. Results for spectrally derived EWTc were most promising for leaves and ears reaching coefficients of determination up to 0.75 and a normalized RMSE (nRMSE) of 24% between measured and estimated canopy water content.

Beer–Lambert law

10.1109/igarss.2018.8518218

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Gaussian Process Regression Hybrid Models for the Top-of-Atmosphere Retrieval of Vegetation Traits Applied to PRISMA and EnMAP Imagery

Remote Sensing (2024)

Ana B. Pascual-Venteo Jose L. Garcia Katja Berger José Estévez Jorge Vicent

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 R2 = 0.92 (LAI), R2 = 0.72 (CCC) and 0.68 (CWC), R2 = 0.94 (FAPAR), and R2 = 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% (R2 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.

10.3390/rs16071211

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Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity

Lammert Kooistra Katja Berger Benjamin Brede Lukas Valentin Graf Helge Aasen

Abstract. Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data are usually routinely acquired, vegetation can be monitored repeatedly over time; reflecting seasonal vegetation patterns and trends in vegetation productivity metrics. Such metrics include e.g., gross primary productivity, net primary productivity, biomass or yield. To summarize current knowledge, in this paper, we systematically reviewed time series (TS) literature for assessing state-of-the-art vegetation productivity monitoring approaches for different ecosystems based on optical remote sensing (RS) data. As the integration of solar-induced fluorescence (SIF) data in vegetation productivity processing chains has emerged as a promising source, we also include this relatively recent sensor modality. We define three methodological categories to derive productivity metrics from remotely sensed TS of vegetation indices or quantitative traits: (i) trend analysis and anomaly detection, (ii) land surface phenology, and (iii) integration and assimilation of TS-derived metrics into statistical and process-based dynamic vegetation models (DVM). Although the majority of used TS data streams originate from data acquired from satellite platforms, TS data from aircraft and unoccupied aerial vehicles have found their way into productivity monitoring studies. To facilitate processing, we provide a list of common toolboxes for inferring productivity metrics and information from TS data. We further discuss validation strategies of the RS-data derived productivity metrics: (1) using in situ measured data, such as yield, (2) sensor networks of distinct sensors, including spectroradiometers, flux towers, or phenological cameras, and (3) inter-comparison of different productivity products or modelled estimates. Finally, we address current challenges and propose a conceptual framework for productivity metrics derivation, including fully-integrated DVMs and radiative transfer models here labelled as "Digital Twin". This novel framework meets the requirements of multiple ecosystems and enables both an improved understanding of vegetation temporal dynamics in response to climate and environmental drivers and also enhances the accuracy of vegetation productivity monitoring.

10.5194/bg-2023-88

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Synergy of Sentinel-1 and Sentinel-2 Time Series for Cloud-Free Vegetation Water Content Mapping with Multi-Output Gaussian Processes

Remote Sensing (2023)

Gabriel Caballero Alejandro Pezzola Cristina Winschel Paolo Sanchez Angonova Alejandra Casella

Optical Earth Observation is often limited by weather conditions such as cloudiness. Radar sensors have the potential to overcome these limitations, however, due to the complex radar-surface interaction, the retrieving of crop biophysical variables using this technology remains an open challenge. Aiming to simultaneously benefit from the optical domain background and the all-weather imagery provided by radar systems, we propose a data fusion approach focused on the cross-correlation between radar and optical data streams. To do so, we analyzed several multiple-output Gaussian processes (MOGP) models and their ability to fuse efficiently Sentinel-1 (S1) Radar Vegetation Index (RVI) and Sentinel-2 (S2) vegetation water content (VWC) time series over a dry agri-environment in southern Argentina. MOGP models not only exploit the auto-correlations of S1 and S2 data streams independently but also the inter-channel cross-correlations. The S1 RVI and S2 VWC time series at the selected study sites being the inputs of the MOGP models proved to be closely correlated. Regarding the set of assessed models, the Convolutional Gaussian model (CONV) delivered noteworthy accurate data fusion results over winter wheat croplands belonging to the 2020 and 2021 campaigns (NRMSEwheat2020 = 16.1%; NRMSEwheat2021 = 10.1%). Posteriorly, we removed S2 observations from the S1 & S2 dataset corresponding to the complete phenological cycles of winter wheat from September to the end of December to simulate the presence of clouds in the scenes and applied the CONV model at the pixel level to reconstruct spatiotemporally-latent VWC maps. After applying the fusion strategy, the phenology of winter wheat was successfully recovered in the absence of optical data. Strong correlations were obtained between S2 VWC and S1 & S2 MOGP VWC reconstructed maps for the assessment dates (R2¯wheat−2020 = 0.95, R2¯wheat−2021 = 0.96). Altogether, the fusion of S1 SAR and S2 optical EO data streams with MOGP offers a powerful innovative approach for cropland trait monitoring over cloudy high-latitude regions.

Sensor Fusion

10.3390/rs15071822

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Citations (15)

Canopy nitrogen content retrieval from hyperspectral satellite data through spectral band selection with Gaussian processes

Ana B. Pascual-Venteo Adrián Pérez-Suay Katja Berger Jochem Verrelst

<p>Advanced retrieval models allow us to make inferences from the signals acquired remotely by satellites to a set of variables, to better understand and describe the states and dynamics of croplands. One essential variable is canopy nitrogen content (CNC), being one of the most relevant traits for agricultural monitoring applications. In the next coming years, there will be an increasing amount of available data acquired by a new generation of hyperspectral satellites (image spectrometer missions), such as PRISMA, and upcoming EnMAP and CHIME missions. When dealing with hyperspectral satellite data, the curse of dimensionality and the effects of noise can be successfully alleviated through feature (band) selection procedures. In our proposed setting, most meaningful spectral bands for the retrieval of CNC were selected, providing a lower spectral subset of the original data but maintaining the physical meaning of each spectral band. Radiative transfer models (RTM) simulate bi-derectional reflectance as a function of diverse biochemical and biophysical input parameters. In this way, RTMs allow to build upon new methods and prepare future missions due to its capability of simulating real scenarios based on their physical consistent definition. In this work, we focus on the leaf optical properties model PROSPECT-PRO coupled with the canopy reflectance 4SAIL model to establish a training database for Gaussian process (GP) regression algorithms. The proposed methodology performs regression from input values, the reflectance, to the output values, the biophysical parameters or traits of interest. In this work, we explored a spectral band selection tool (GPR-BAT) embedded in the ARTMO toolbox (https://artmotoolbox.com/), dedicated to the transformation of optical remote sensing images into biophysical vegetation products and maps. GPR-BAT is based on a sequential backward band removal (SBBR) algorithm that iteratively removes the spectral bands which contribute less to the regression model. This procedure is repeated until only one relevant band is left over. GPR-BAT allows to: i) identify the most informative or relevant bands to estimate one specific biophysical or biochemical variable, and ii) find a smaller set of bands preserving the optimal predictions.<strong> </strong>The optimal set of 15 bands achieved a coefficient of determination (R&#178;) of 0.6 and a normalised root mean squared error (NRMSE) of 19 % to retrieve canopy nitrogen content sampled over maize and winter wheat during a field campaign in the North of Munich, Germany (MMNI site), during 2017 and 2018 growing seasons. Furthermore, a variance-based global sensitivity analysis of the PROSAIL-PRO model confirmed the optimal position of the identified band setting within the nitrogen (protein) sensitive wavelength domain. The optimal set of bands were to be found in the near infrared and in the short wave infrared, especially in the 1700-1800 nm region. Applying the established models on acquired PRISMA images revealed the adequacy of the proposed method for mapping applications. We conclude that our proposed methodology achieved promising results both in accuracy of estimates and mapping quality over different geographical regions.</p>

Spectral bands

10.5194/egusphere-egu22-12511

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Biophysical information in asymmetric and symmetric Diurnal bidirectional canopy reflectance

IEEE Transactions on Geoscience and Remote Sensing (1991)

V. C. Vanderbilt Susan L. Ustin Katja Berger William F. Caldwell J. A. Clark

The authors present a theory for partitioning the information content in diurnal bidirectional reflectance measurements in order to detect differences potentially related to biophysical variables. The theory, which divides the canopy reflectance into asymmetric and symmetric functions of solar azimuth angle, attributes asymmetric variation to diurnal changes in the canopy biphysical properties. The symmetric function is attributed to the effects of sunlight interacting with a hypothetical average canopy which would display the average diurnal properties of the actual canopy. The authors analyzed radiometer data collected diurnally in the Thematic Mapper wavelength bands from two walnut canopies that received differing irrigation treatments. The reflectance of the canopies varied with sun and view angles and across seven bands in the visible, near-infrared, and middle infrared wavelength regions. Although one of the canopies was permanently water stressed and the other was stressed in mid-afternoon each day, no water stress signature was unambiguously evident in the reflectance data.

10.1109/tgrs.1991.1019471

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Citations (7)

Developing a Sandbox Environment for Prosail, Suitable for Education and Research

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium (2018)

Martin Danner Matthias Wocher Katja Berger Wolfram Mauser Tobias Hank

We introduce the Interactive Visualization of Vegetation Reflectance Models (IVVRM) tool as a sandbox environment for the PROSAIL family of radiative transfer models. Every interaction with the Graphical User Interface (GUI) invokes a new model run of the updated parameter set and the results are instantly plotted on the screen. The quasi-simultaneous response allows easy hands-on practice with PROSAIL for education and training as well as straightforward inversions of biophysical variables from spectra by manual curve fitting. It is shown that expert knowledge can improve the quality of parameter retrieval and reveal sources of uncertainties in the field data and the models. IVVRM is free of charge and available as an application through the EnMAP-Box 3.0.

Sandbox (software development)

Graphical user interface

10.1109/igarss.2018.8519378

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Seasonal Mapping of Irrigated Winter Wheat Traits in Argentina with a Hybrid Retrieval Workflow Using Sentinel-2 Imagery

Remote Sensing (2022)

Gabriel Caballero Alejandro Pezzola Cristina Winschel Alejandra Casella Paolo Sanchez Angonova

Earth observation offers an unprecedented opportunity to monitor intensively cultivated areas providing key support to assess fertilizer needs and crop water uptake. Routinely, vegetation traits mapping can help farmers to monitor plant development along the crop's phenological cycle, which is particularly relevant for irrigated agricultural areas. The high spatial and temporal resolution of the Sentinel-2 (S2) multispectral instrument leverages the possibility to estimate leaf area index (LAI), canopy chlorophyll content (CCC), and vegetation water content (VWC) from space. Therefore, our study presents a hybrid retrieval workflow combining a physically-based strategy with a machine learning regression algorithm, i.e., Gaussian processes regression, and an active learning technique to estimate LAI, CCC and VWC of irrigated winter wheat. The established hybrid models of the three traits were validated against in-situ data of a wheat campaign in the Bonaerense valley, South of the Buenos Aires Province, Argentina, in the year 2020. We obtained good to highly accurate validation results with LAI: R

10.3390/rs14184531

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Citations (11)

Comment on bg-2023-88

Lammert Kooistra Katja Berger Benjamin Brede Lukas Valentin Graf Helge Aasen

Abstract. Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data are usually routinely acquired, vegetation can be monitored repeatedly over time; reflecting seasonal vegetation patterns and trends in vegetation productivity metrics. Such metrics include e.g., gross primary productivity, net primary productivity, biomass or yield. To summarize current knowledge, in this paper, we systematically reviewed time series (TS) literature for assessing state-of-the-art vegetation productivity monitoring approaches for different ecosystems based on optical remote sensing (RS) data. As the integration of solar-induced fluorescence (SIF) data in vegetation productivity processing chains has emerged as a promising source, we also include this relatively recent sensor modality. We define three methodological categories to derive productivity metrics from remotely sensed TS of vegetation indices or quantitative traits: (i) trend analysis and anomaly detection, (ii) land surface phenology, and (iii) integration and assimilation of TS-derived metrics into statistical and process-based dynamic vegetation models (DVM). Although the majority of used TS data streams originate from data acquired from satellite platforms, TS data from aircraft and unoccupied aerial vehicles have found their way into productivity monitoring studies. To facilitate processing, we provide a list of common toolboxes for inferring productivity metrics and information from TS data. We further discuss validation strategies of the RS-data derived productivity metrics: (1) using in situ measured data, such as yield, (2) sensor networks of distinct sensors, including spectroradiometers, flux towers, or phenological cameras, and (3) inter-comparison of different productivity products or modelled estimates. Finally, we address current challenges and propose a conceptual framework for productivity metrics derivation, including fully-integrated DVMs and radiative transfer models here labelled as "Digital Twin". This novel framework meets the requirements of multiple ecosystems and enables both an improved understanding of vegetation temporal dynamics in response to climate and environmental drivers and also enhances the accuracy of vegetation productivity monitoring.

10.5194/bg-2023-88-rc1

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Modellbasierte Selektion hyperspektraler EnMAP Kanäle zur optimalen Invertierung von Strahlungstransfermodellen für landwirtschaftliche Kulturen

PFG – Journal of Photogrammetry Remote Sensing and Geoinformation Science (2018)

Katja Berger Clement Atzberger Martin Danner Matthias Wocher Wolfram Mauser

Model-based Selection of hyperspectral EnMAP Channels for optimal Inversion of Radiation Transfer Models in Agriculture. Satellite-based hyperspectral Earth observation data combined with physically based radiative transfer models have the strong potential to support sustainable agriculture by providing accurate spatial and temporal information of important vegetation biophysical and biochemical variables such as leaf chlorophyll content. To meet this goal, possible error sources in the modelling should be minimized. Thus, the capability of a model to reproduce the measured spectral signals has to be tested before applying any retrieval algorithm. For an exemplary demonstration, the PROSAIL model was employed to emulate the setup of the future EnMAP hyperspectral sensor in the visible and near-infrared (VNIR) spectral region with a 6.5 nm spectral sampling distance. Model uncertainties were determined to subsequently exclude those wavelengths with highest mean absolute error (MAE) between model simulation and spectral measurement. For this purpose data from two campaigns were exploited (1) from Nebraska–Lincoln (maize and soybean) and (2) from Munich–North-Isar (maize and winter wheat). A significant increase of accuracy for leaf chlorophyll content (LCC, µg cm−2) estimations could be obtained, with relative RMSE decreasing from 26% (full VNIR range) to 15% (optimized VNIR) for maize and from 77% to 29% for soybean, respectively. We therefore recommend applying a specific model-error threshold (MAE ~ 0.01) to stabilize the retrieval of crop biochemical variables.

VNIR

Precision Agriculture

10.1007/s41064-019-00063-2

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Citations (1)