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Spatiotemporal Downscaling of GRACE Total Water Storage Using Land Surface Model Outputs. REMOTE SENSING 2021. [DOI: 10.3390/rs13050900] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
High spatiotemporal resolution of terrestrial total water storage plays a key role in assessing trends and availability of water resources. This study presents a two-step method for downscaling GRACE-derived total water storage anomaly (GRACE TWSA) from its original coarse spatiotemporal resolution (monthly, 3-degree spherical cap/~300 km) to a high resolution (daily, 5 km) through combining land surface model (LSM) simulated high spatiotemporal resolution terrestrial water storage anomaly (LSM TWSA). In the first step, an iterative adjustment method based on the self-calibration variance-component model (SCVCM) is used to spatially downscale the monthly GRACE TWSA to the high spatial resolution of the LSM TWSA. In the second step, the spatially downscaled monthly GRACE TWSA is further downscaled to the daily temporal resolution. By applying the method to downscale the coarse resolution GRACE TWSA from the Jet Propulsion Laboratory (JPL) mascon solution with the daily high spatial resolution (5 km) LSM TWSA from the Ecological Assimilation of Land and Climate Observations (EALCO) model, we evaluated the benefit and effectiveness of the proposed method. The results show that the proposed method is capable to downscale GRACE TWSA spatiotemporally with reduced uncertainty. The downscaled GRACE TWSA are also evaluated through in-situ groundwater monitoring well observations and the results show a certain level agreement between the estimated and observed trends.
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Evaluation of the Snow Albedo Retrieved from the Snow Kernel Improved the Ross-Roujean BRDF Model. REMOTE SENSING 2019. [DOI: 10.3390/rs11131611] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The original kernel-driven bidirectional reflectance distribution function (BRDF) models were developed based on soil-vegetation systems. To further improve the ability of the models to characterize the snow surface scattering properties, a snow kernel was derived from the asymptotic radiative transfer (ART) model and used in the kernel-driven BRDF model framework. However, there is a need to further evaluate the influence of using this snow kernel to improve the original kernel-driven models in snow albedo retrieval applications. The aim of this study is to perform such an evaluation using a variety of snow BRDF data. The RossThick-Roujean (RTR) model is used as a framework for taking in the new snow kernel (hereafter named the RTS model) since the Roujean geometric-optical (GO) kernel captures a neglectable hotspot effect and represents a more prominent dome-shaped BRDF, especially at a small solar zenith angle (SZA). We obtained the following results: (1) The RTR model has difficulties in reconstructing the snow BRDF shape, especially at large SZAs, which tends to underestimate the reflectance in the forward direction and overestimate reflectance in the backward direction for various data sources. In comparison, the RTS model performs very well in fitting snow BRDF data and shows high accuracy for all data. (2) The RTR model retrieved snow albedos at SZAs = 30°–70° are underestimated by 0.71% and 0.69% in the red and near-infrared (NIR) bands, respectively, compared with the simulation results of the bicontinuous photon tracking (bic-PT) model, which serve as “real” values. However, the albedo retrieved by the RTS model is significantly improved and generally agrees well with the simulation results of the bic-PT model, although the improved model still somewhat underestimates the albedo by 0.01% in the red band and overestimates the albedo by 0.05% in the NIR band, respectively, at SZAs = 30°–70°, which may be negligible. (3) The albedo derived by these two models shows a high correlation (R2 > 0.9) between the field-measured and Polarization and Directionality of the Earth's Reflectances (POLDER) data, especially for the black-sky albedo. However, the albedo derived using the RTR model is significantly underestimated compared with the RTS model. The RTR model underestimates the black-sky albedo (white-sky albedo) retrievals by 0.62% (1.51%) and 0.93% (2.08%) in the red and NIR bands, respectively, for the field-measured data. The shortwave black-sky and white-sky albedos derived using the RTR model for the POLDER data are underestimated by 1.43% and 1.54%, respectively, compared with the RTS model. These results indicate that the snow kernel in the kernel-driven BRDF model frame is more accurate in snow albedo retrievals and has the potential for application in the field of the regional and global energy budget.
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Pasetto D, Arenas‐Castro S, Bustamante J, Casagrandi R, Chrysoulakis N, Cord AF, Dittrich A, Domingo‐Marimon C, El Serafy G, Karnieli A, Kordelas GA, Manakos I, Mari L, Monteiro A, Palazzi E, Poursanidis D, Rinaldo A, Terzago S, Ziemba A, Ziv G. Integration of satellite remote sensing data in ecosystem modelling at local scales: Practices and trends. Methods Ecol Evol 2018. [DOI: 10.1111/2041-210x.13018] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Damiano Pasetto
- Laboratory of Ecohydrology École Polytechnique Fédérale de Lausanne Lausanne Switzerland
| | - Salvador Arenas‐Castro
- CIBIO/InBIO Research Center in Biodiversity and Genetic Resources University of Porto Vairão Portugal
| | | | - Renato Casagrandi
- Dipartimento di Elettronica, Informazione e Bioingegneria Politecnico di Milano Milan Italy
| | - Nektarios Chrysoulakis
- Institute of Applied and Computational Mathematics Foundation for Research and Technology Hellas Heraklion Greece
| | - Anna F. Cord
- Department of Computational Landscape Ecology UFZ – Helmholtz Centre for Environmental Research Leipzig Germany
| | - Andreas Dittrich
- Department of Computational Landscape Ecology UFZ – Helmholtz Centre for Environmental Research Leipzig Germany
| | | | - Ghada El Serafy
- Deltares Delft The Netherlands
- Department of Applied Mathematics Delft University of Technology Delft The Netherlands
| | - Arnon Karnieli
- Jacob Blaustein Institutes for Desert Research Ben‐Gurion University of the Negev Beersheba Israel
| | - Georgios A. Kordelas
- Information Technologies Institute Centre for Research and Technology Hellas Thermi Greece
| | - Ioannis Manakos
- Information Technologies Institute Centre for Research and Technology Hellas Thermi Greece
| | - Lorenzo Mari
- Dipartimento di Elettronica, Informazione e Bioingegneria Politecnico di Milano Milan Italy
| | - Antonio Monteiro
- CIBIO/InBIO Research Center in Biodiversity and Genetic Resources University of Porto Vairão Portugal
| | - Elisa Palazzi
- Institute of Atmospheric Sciences and Climate National Research Council Turin Italy
| | - Dimitris Poursanidis
- Institute of Applied and Computational Mathematics Foundation for Research and Technology Hellas Heraklion Greece
| | - Andrea Rinaldo
- Laboratory of Ecohydrology École Polytechnique Fédérale de Lausanne Lausanne Switzerland
- Department of Civil Environmental and Architectural Engineering University of Padova Padova Italy
| | - Silvia Terzago
- Institute of Atmospheric Sciences and Climate National Research Council Turin Italy
| | - Alex Ziemba
- Deltares Delft The Netherlands
- Department of Applied Mathematics Delft University of Technology Delft The Netherlands
| | - Guy Ziv
- School of Geography Faculty of Environment University of Leeds Leeds UK
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Verrelst J, Malenovský Z, Van der Tol C, Camps-Valls G, Gastellu-Etchegorry JP, Lewis P, North P, Moreno J. Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods. SURVEYS IN GEOPHYSICS 2018; 40:589-629. [PMID: 36081834 PMCID: PMC7613341 DOI: 10.1007/s10712-018-9478-y] [Citation(s) in RCA: 106] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2017] [Accepted: 05/16/2018] [Indexed: 05/04/2023]
Abstract
An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2) nonparametric regression, including linear and nonlinear machine learning regression algorithms; (3) physically based, including inversion of radiative transfer models (RTMs) using numerical optimization and look-up table approaches; and (4) hybrid regression methods, which combine RTM simulations with machine learning regression methods. For each of these categories, an overview of widely applied methods with application to mapping vegetation properties is given. In view of processing imaging spectroscopy data, a critical aspect involves the challenge of dealing with spectral multicollinearity. The ability to provide robust estimates, retrieval uncertainties and acceptable retrieval processing speed are other important aspects in view of operational processing. Recommendations towards new-generation spectroscopy-based processing chains for operational production of biophysical variables are given.
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Affiliation(s)
- Jochem Verrelst
- Image Processing Laboratory (IPL), Parc Científic, Universitat de València, Paterna, València 46980, Spain
| | - Zbyněk Malenovský
- Surveying and Spatial Sciences Group, School of Technology, Environments and Design, University of Tasmania, Private Bag 76, Hobart, TAS 7001, Australia
- Remote Sensing Department, Global Change Research Institute CAS, Bělidla 986/4a, 60300 Brno, Czech Republic
- USRA/GESTAR, Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, 8800 Greenbelt Rd, Greenbelt, MD 20771, USA
| | - Christiaan Van der Tol
- Department of Water Resources, Faculty ITC, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands
| | - Gustau Camps-Valls
- Image Processing Laboratory (IPL), Parc Científic, Universitat de València, Paterna, València 46980, Spain
| | | | - Philip Lewis
- Department of Geography, University College London, Pearson Building, Gower Street, WC1E 6BT London, UK
- National Centre for Earth Observation, Department of Physics and Astronomy, The University of Leicester, Michael Atiyah Building, LE1 7RH Leicester, UK
| | - Peter North
- Department of Geography, Swansea University, Swansea SA2 8PP, UK
| | - Jose Moreno
- Image Processing Laboratory (IPL), Parc Científic, Universitat de València, Paterna, València 46980, Spain
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Remote Sensing of Vegetation: Potentials, Limitations, Developments and Applications. CANOPY PHOTOSYNTHESIS: FROM BASICS TO APPLICATIONS 2016. [DOI: 10.1007/978-94-017-7291-4_11] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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Good SP, Rodriguez-Iturbe I, Caylor KK. Analytical expressions of variability in ecosystem structure and function obtained from three-dimensional stochastic vegetation modelling. Proc Math Phys Eng Sci 2013. [DOI: 10.1098/rspa.2013.0003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Whole ecosystem exchange of water, carbon and energy is predominately determined by complex leaf-level processes occurring at individual plants. Interaction between individuals results in a distribution of environmental conditions that drive a variety of nonlinear response functions such as transpiration and photosynthesis. The nonlinearity of biophysical processes requires higher-order statistical descriptions of micro-environment distributions in order to accurately determine the landscape-scale mean functional response. We present a mathematical framework for describing vegetation structure based on the density, dispersion, size distribution and allometry of individuals within a landscape. Using three-dimensional stochastic vegetation modelling, we develop analytic expressions of the second-order statistics of vegetation canopies, namely the mean and variance of leaf area density and leaf area index with height. These expressions also allow for the approximation of the distribution of beam penetration and sunfleck statistics through the canopy as a function of height. Finally, we demonstrate how landscape-scale fluxes are strongly affected by the variability in canopy micro-environments, and how stochastic vegetation modelling improves flux estimates relative to traditional homogeneous canopy models.
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Affiliation(s)
- Stephen P. Good
- Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544, USA
| | - I. Rodriguez-Iturbe
- Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544, USA
| | - K. K. Caylor
- Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544, USA
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Govind A, Guyon D, Roujean JL, Yauschew-Raguenes N, Kumari J, Pisek J, Wigneron JP. Effects of canopy architectural parameterizations on the modeling of radiative transfer mechanism. Ecol Modell 2013. [DOI: 10.1016/j.ecolmodel.2012.11.014] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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