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1. Validation practices for satellite soil moisture retrievals: What are (the) errors?

Author

Philippe Richaume, Martin Hirschi, Yann Kerr, Wolfgang Wagner, Clément Albergel, Joaquín Muñoz-Sabater, Tracy Scanlon, Jian Peng, Rolf H. Reichle, Andreas Colliander, Alexandra G. Konings, Luca Brocca, J. P. Wigneron, Amen Al-Yaari, Carsten Montzka, Michael H. Cosh, J.C. Calvet, Clara S. Draper, Christoph Rudiger, Alexander Gruber, Wouter Dorigo, G. De Lannoy, R. van der Schalie, Wade T. Crow, William Lahoz, Kaighin A. McColl, Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven), Météo-France Direction Interrégionale Sud-Est (DIRSE), Météo-France, Milieux Environnementaux, Transferts et Interactions dans les hydrosystèmes et les Sols (METIS), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institute of Methodologies for Environmental Analysis of the National Research Council (IMAA), National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), Jet Propulsion Laboratory (JPL), NASA-California Institute of Technology (CALTECH), USDA-ARS : Agricultural Research Service, Hydrology and Remote Sensing Laboratory, USDA Agricultural Research Service [Beltsville, Maryland], USDA-ARS : Agricultural Research Service-USDA-ARS : Agricultural Research Service, Department of Geodesy and Geoinformation [Wien], Vienna University of Technology (TU Wien), Centre d'études spatiales de la biosphère (CESBIO), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Interactions Sol Plante Atmosphère (UMR ISPA), Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Météo France, École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Consiglio Nazionale delle Ricerche [Roma] (CNR), US Department of Agriculture [Beltsville] (USDA), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), and Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)

Subjects
010504 meteorology & atmospheric sciences, 0207 environmental engineering, Soil Science, Good practice, 02 engineering and technology, 01 natural sciences, Error estimation, Error characterization, Validation, ddc:550, Remote sensing, Soil moisture, Standardisation, Computers in Earth Sciences, 020701 environmental engineering, Water content, 0105 earth and related environmental sciences, Protocol (science), Geology, 13. Climate action, [SDE]Environmental Sciences, Environmental science, Satellite
Abstract

International audience; This paper presents a community effort to develop good practice guidelines for the validation of global coarse-scale satellite soil moisture products. We provide theoretical background, a review of state-of-the-art methodologies for estimating errors in soil moisture data sets, practical recommendations on data pre-processing and presentation of statistical results, and a recommended validation protocol that is supplemented with an example validation exercise focused on microwave-based surface soil moisture products. We conclude by identifying research gaps that should be addressed in the near future.

Published
2020
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2. Satellite-based soil moisture provides missing link between summertime precipitation and surface temperature biases in CMIP5 simulations over conterminous United States

Author

Al-Yaari, A., Ducharne, A., Cheruy, F., Crow, W., Wigneron, J.-P., Interactions Sol Plante Atmosphère (UMR ISPA), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro), Milieux Environnementaux, Transferts et Interactions dans les hydrosystèmes et les Sols (METIS), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Météorologie Dynamique (UMR 8539) (LMD), Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-École des Ponts ParisTech (ENPC)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Département des Géosciences - ENS Paris, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Hydrology and Remote Sensing Laboratory, US Department of Agriculture [Beltsville] (USDA), Interactions Sol Plante Atmosphère (ISPA), École pratique des hautes études (EPHE)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), École normale supérieure - Paris (ENS Paris)-École normale supérieure - Paris (ENS Paris), and USDA

Subjects
[SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology, lcsh:R, lcsh:Medicine, lcsh:Q, Hydrology, [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology, lcsh:Science, Article, Climate sciences
Abstract

International audience; Past studies have shown that climate simulations have substantial warm and dry biases during the summer in the conterminous United States (CONUS), particularly in the central Great Plains (CGP). These biases have critical implications for the interpretation of climate change projections, but the complex overlap of multiple land-atmosphere feedback processes make them difficult to explain (and therefore correct). Even though surface soil moisture (SM) is often cited as a key control variable in these processes, there are still knowledge gaps about its specific role. Here, we use recently developed remotely sensed SM products to analyse the link between spatial patterns of summertime SM, precipitation and air temperature biases over CONUS in 20 different CMIP5 simulations. We identify three main types of bias combinations: (i) a dry/warm bias over the CGP region, with a significant inter-model correlation between SM and air temperature biases (R = −0.65), (ii) a wet/cold bias in NW CONUS, and (iii) a dry/cold bias in SW CONUS. Combined with irrigation patterns, these results suggest that land-atmosphere feedbacks over the CGP are not only local but have a regional dimension, and demonstrate the added-value of large-scale SM observations for resolving the full feed-back loop between precipitation and temperature.

Published
2019
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3. Comparison of SMOS and SMAP soil moisture retrieval approaches using tower-based radiometer data over a vineyard field

Author

Ernesto Lopez-Baeza, Yann Kerr, Peggy O'Neill, Ahmad Al Bitar, P. Richaume, Heather Lawrence, Roberto Fernandez Moran, Gabrielle De Lannoy, Jean-Pierre Wigneron, Richard de Jeu, Arnaud Mialon, Simone Bircher, Mike Schwank, Thomas J. Jackson, Maciej Miernecki, University of Hamburg, Interactions Sol Plante Atmosphère (UMR ISPA), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro), Universitat de València (UV), Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Vrije universiteit = Free university of Amsterdam [Amsterdam] (VU), Atmospheric and Environmental Research, Inc. (AER), Hydrology and Remote Sensing Laboratory, US Department of Agriculture [Beltsville] (USDA), Gamma Remote Sensing, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, European Centre for Medium-Range Weather Forecasts (ECMWF), VU University Amsterdam, Earth and Climate, and Amsterdam Global Change Institute

Subjects
010504 meteorology & atmospheric sciences, Mean squared error, Meteorology, [SDE.MCG]Environmental Sciences/Global Changes, 0211 other engineering and technologies, Soil Science, 02 engineering and technology, Astrophysics::Cosmology and Extragalactic Astrophysics, 01 natural sciences, Physics::Geophysics, 14. Life underwater, Computers in Earth Sciences, Time series, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, Atmospheric sounding, Valencia Anchor Station, Radiometer, Geology, Inversion (meteorology), SMAP, 15. Life on land, Brightness temperature, Soil water, Environmental science, Radiometry, Soil moisture retrieval, ELBARA, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, SMOS
Abstract

International audience; The objective of this study was to compare several approaches to soil moisture (SM) retrieval using l-band microwave radiometry. The comparison was based on a brightness temperature (TB) data set acquired since 2010 by the L-band radiometer ELBARA-II over a vineyard field at the Valencia Anchor Station (VAS) site. ELBARA-II, provided by the European Space Agency (ESA) within the scientific program of the SMOS (Soil Moisture and Ocean Salinity) mission, measures multiangular TB data at horizontal and vertical polarization for a range of incidence angles (30°–60°). Based on a three year data set (2010–2012), several SM retrieval approaches developed for spaceborne missions including AMSR-E (Advanced Microwave Scanning Radiometer for EOS), SMAP (Soil Moisture Active Passive) and SMOS were compared. The approaches include: the Single Channel Algorithm (SCA) for horizontal (SCA-H) and vertical (SCA-V) polarizations, the Dual Channel Algorithm (DCA), the Land Parameter Retrieval Model (LPRM) and two simplified approaches based on statistical regressions (referred to as ‘Mattar’ and ‘Saleh’). Time series of vegetation indices required for three of the algorithms (SCA-H, SCA-V and ‘Mattar’) were obtained from MODIS observations. The SM retrievals were evaluated against reference SM values estimated from a multiangular 2-Parameter inversion approach. As no in situ SM data was used, the evaluation made here is relative to the use of this specific reference data set. The results obtained with the current base line algorithms developed for SMAP (SCA-H and -V) are in very good agreement with the ‘reference’ SM data set derived from the multi-angular observations (R2 ≈ 0.90, RMSE varying between 0.035 and 0.056 m3/m3 for several retrieval configurations). This result showed that, provided the relationship between vegetation optical depth and a remotely-sensed vegetation index can be calibrated, the SCA algorithms can provide results very close to those obtained from multi-angular observations in this study area. The approaches based on statistical regressions provided similar results and the best accuracy was obtained with the ‘Saleh’ methods based on either bi-angular or bipolarization observations (R2 ≈ 0.93, RMSE ≈ 0.035 m3/m3). The LPRM and DCA algorithms were found to be slightly less successful in retrieving the ‘reference’ SM time series (R2 ≈ 0.75, RMSE ≈ 0.055 m3/m3). However, the two above approaches have the great advantage of not requiring any model calibrations previous to the SM retrievals.

Published
2014
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4. First application of regression analysis to retrieve soil moisture from SMAP brightness temperature observations consistent with SMOS

Author

Yann Kerr, Peggy O'Neill, Ahmad Al Bitar, J. P. Wigneron, Amen Al-Yaari, Arnaud Mialon, G. De Lannoy, Thomas J. Jackson, Philippe Richaume, Nemesio Rodriguez-Fernandez, Simon Yueh, Interactions Sol Plante Atmosphère (UMR ISPA), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro), Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), NASA Goddard Space Flight Center (GSFC), Hydrology and Remote Sensing Laboratory, US Department of Agriculture [Beltsville] (USDA), Jet Propulsion Laboratory (JPL), NASA-California Institute of Technology (CALTECH), and IEEE Geoscience and Remote Sensing Society (GRSS). USA.

Subjects
Brightness, 010504 meteorology & atmospheric sciences, Moisture, Correlation coefficient, 0211 other engineering and technologies, [SDU.STU]Sciences of the Universe [physics]/Earth Sciences, Regression analysis, 02 engineering and technology, SMAP, statistical regression, 01 natural sciences, Salinity, Brightness temperature, Environmental science, Satellite, soil moisture, Water content, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, SMOS
Abstract

International audience; In this study, we used a multilinear regression approach to retrieve surface soil moisture from NASA's Soil Moisture Active Passive (SMAP) satellite data to create a global dataset of surface soil moisture which is consistent with ESA's Soil Moisture and Ocean Salinity (SMOS) satellite retrieved surface soil moisture. This was achieved by calibrating coefficients of the regression model using SMOS soil moisture and horizontal and vertical brightness temperatures (TB), over the 2013 — 2014 period. Next, this model was applied to recent SMAP TB data from 31/03/2015–08/09/2015. The retrieved surface soil moisture from SMAP (referred here to as SMAP-reg) was compared to the operational SMAP L3 surface soil moisture retrieved using the single channel algorithm. Both exhibit comparable temporal dynamics with a good agreement of correlation (correlation coefficient R mostly > 0.8) between the SMAP-reg and the operational SMAP L3 surface soil moisture products.

Published
2016
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5. SMOS soil moisture algorithm: retrieval accuracy and intercomparison with other sensors

Author

Kerr, Yann H., Ahmad, Al Bitar, Al-Yaari, A., Bindlish, Rajat, Escorihuela, Maria-José, Jackson, Thomas, Leroux, Delphine, Munoz-Sabater, Joaquin, Richaume, Philippe, Pellarin, Thierry, Rüdiger, Christoph, Wigneron, Jean-Pierre, Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Hydrology and Remote Sensing Laboratory, US Department of Agriculture [Beltsville] (USDA), isardSAT, European Centre for Medium-Range Weather Forecasts (ECMWF), Laboratoire d'étude des transferts en hydrologie et environnement (LTHE), Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), Monash University, Interactions Sol Plante Atmosphère (UMR ISPA), and Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro)

Subjects
[SDE.MCG]Environmental Sciences/Global Changes, [SDU.STU]Sciences of the Universe [physics]/Earth Sciences, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; The Soil Moisture and Ocean Salinity (SMOS) mission was launched by European Space Agency (ESA) in November 2009 to measure soil moisture and ocean surface salinity. SMOS is a synthetic aperture L-band radiometer and provides global coverage in 3 days. The level 2 soil moisture products are distributed by ESA’s DPGS (European Space Agency’s Data Processing Ground Segment) for each half orbit since January 2010. The Centre National d’Etudes Spatiales (CNES) has developed the CATDS (Centre Aval de Traitement des Donnees SMOS) ground segment that now provides spatial and temporal synthesis products (referred to as Level 3 products) of soil moisture, ocean salinity and brightness temperatures at multiple incidence angles over the entire operational of SMOS. The AMSR –E sensor on board NASA’s AQUA satellite and its successor, JAXA’s AMSR-2, has been providing soil moisture estimates since 2002 either through the NASA DAAC at the National Snow and Ice Data Center (NSIDC) or through the Vrije Universiteit of Amsterdam. Similarly, the ERS-1 and -2 scatterometers, followed by ASCAT on board METOP have been delivering soil wetness indices through the Eumetsat H-SAF project with a very long climatological record since 1991. Also available now the new Aquarius SM retrievals which are now available and will soon be distributed through NSIDC. The goal of this presentation is three folds: 1 - provide an estimate of the SMOS retrieval accuracy using a large set of ground data 2 - intercompare the different products to assess the pros and cons of each mission and their relative merits as a function of land cover, season etc.. 3 - establish how one could build up a long term environmental data record of soil moisture from these data sets in order to study the impact of climate change on the global water cycle.

Published
2014

6. Hyperspectral remote sensing of foliar nitrogen content

Author

Mitchell A. Schull, Pauline Stenberg, Philip Lewis, Vern C. Vanderbilt, Miina Rautiainen, Matti Mõttus, Yuri Knyazikhin, Mathias Disney, Alexander Marshak, Pedro Latorre Carmona, Yan Yang, Alexei Lyapustin, Robert K. Kaufmann, Frédéric Baret, Stéphane Jacquemoud, Ranga B. Myneni, Anthony B. Davis, Department of Earth and Environment [Boston], Boston University [Boston] (BU), Hydrology and Remote Sensing Laboratory, US Department of Agriculture [Beltsville] (USDA), University of Helsinki, Tartu Observatory, European Synchrotron Radiation Facility (ESRF), NASA, Department of Geography, University College of London [London] (UCL), Institut de Physique du Globe de Paris (IPGP), Institut national des sciences de l'Univers (INSU - CNRS)-IPG PARIS-Université Paris Diderot - Paris 7 (UPD7)-Université de La Réunion (UR)-Centre National de la Recherche Scientifique (CNRS), National Aeronautics and Space Administration Earth Science Division, We thank Dr. S. V. Ollinger and L. Lepine forproviding data on canopy foliar mass-based nitrogen concentration anddistribution of the species canopy foliar dry mass fraction published in ref.11. This research was funded by the National Aeronautics and Space Admin-istration Earth Science Division, Department of Forest Sciences, and Department of Geosciences and Geography

Subjects
spurious regression, Canopy, Light, CANOPY SPECTRAL INVARIANTS, 010504 meteorology & atmospheric sciences, Climate, [SDU.ASTR.EP]Sciences of the Universe [physics]/Astrophysics [astro-ph]/Earth and Planetary Astrophysics [astro-ph.EP], 0211 other engineering and technologies, 02 engineering and technology, 01 natural sciences, Trees, Scattering, Radiation, Radiative effect, Plant ecology, GeneralLiterature_REFERENCE(e.g.,dictionaries,encyclopedias,glossaries), ComputingMilieux_MISCELLANEOUS, 4112 Forestry, Spectroscopy, Near-Infrared, Multidisciplinary, [SDU.ASTR]Sciences of the Universe [physics]/Astrophysics [astro-ph], radiative effect, TEMPERATE, Hyperspectral imaging, Spectral bands, Vegetation, Carbon cycle, Nitrogen Cycle, Nitrogen, Geography, Data Interpretation, Statistical, Spurious regression, chemistry.chemical_element, FOREST ECOSYSTEMS, carbon cycle, BOREAL FORESTS, Temperate climate, Letters, plant ecology, Nitrogen cycle, Ecosystem, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, 15. Life on land, Albedo, LEAF OPTICAL-PROPERTIES, PHOTON RECOLLISION PROBABILITY, Plant Leaves, CLIMATE FEEDBACKS, REFLECTANCE, MODEL, chemistry, [SDU]Sciences of the Universe [physics], 13. Climate action, Remote Sensing Technology, VEGETATION
Abstract

International audience; A strong positive correlation between vegetation canopy bidirectional reflectance factor (BRF) in the near infrared (NIR) spectral region and foliar mass-based nitrogen concentration (%N) has been reported in some temperate and boreal forests. This relationship, if true, would indicate an additional role for nitrogen in the climate system via its influence on surface albedo and may offer a simple approach for monitoring foliar nitrogen using satellite data. We report, however, that the previously reported correlation is an artifact-it is a consequence of variations in canopy structure, rather than of %N. The data underlying this relationship were collected at sites with varying proportions of foliar nitrogen-poor needleleaf and nitrogen-rich broadleaf species, whose canopy structure differs considerably. When the BRF data are corrected for canopy-structure effects, the residual reflectance variations are negatively related to %N at all wavelengths in the interval 423-855 nm. This suggests that the observed positive correlation between BRF and %N conveys no information about %N. We find that to infer leaf biochemical constituents, e.g., N content, from remotely sensed data, BRF spectra in the interval 710-790 nm provide critical information for correction of structural influences. Our analysis also suggests that surface characteristics of leaves impact remote sensing of its internal constituents. This further decreases the ability to remotely sense canopy foliar nitrogen. Finally, the analysis presented here is generic to the problem of remote sensing of leaf-tissue constituents and is therefore not a specific critique of articles espousing remote sensing of foliar %N.

Published
2013
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7. Correcting rainfall using satellite-based surface soil moisture retrievals: The Soil Moisture Analysis Rainfall Tool (SMART)

Author

Crow, W.T., Van Den Berg, M.J., Huffman, G.J., Pellarin, Thierry, Hydrology and Remote Sensing Laboratory, USDA, Laboratory of Hydrology and Water Management, Ghent University [Belgium] (UGENT), Science Systems and Applications, Inc. [Lanham] (SSAI), NASA Goddard Space Flight Center (GSFC), HYBIS, Laboratoire d'étude des transferts en hydrologie et environnement (LTHE), Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), NASA Precipitation Measurement Missions Program, US Department of Agriculture [Beltsville] (USDA), Universiteit Gent = Ghent University [Belgium] (UGENT), Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Grenoble (OSUG), and Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)

Subjects
remote sensing, rainfall, [SDU.STU.GM]Sciences of the Universe [physics]/Earth Sciences/Geomorphology, soil moisture
Abstract

International audience; Recently, Crow et al. (2009) developed an algorithm for enhancing satellite-based land rainfall products via the assimilation of remotely sensed surface soil moisture retrievals into a water balance model. As a follow-up, this paper describes the benefits of modifying their approach to incorporate more complex data assimilation and land surface modeling methodologies. Specific modifications improving rainfall estimates are assembled into the Soil Moisture Analysis Rainfall Tool (SMART), and the resulting algorithm is applied outside the contiguous United States for the first time, with an emphasis on West African sites instrumented as part of the African Monsoon Multidisciplinary Analysis experiment. Results demonstrate that the SMART algorithm is superior to the Crow et al. baseline approach and is capable of broadly improving coarse-scale rainfall accumulations measurements with low risk of degradation. Comparisons with existing multisensor, satellite-based precipitation data products suggest that the introduction of soil moisture information from the Advanced Microwave Scanning Radiometer via SMART provides as much coarse-scale (3 day, 1°) rainfall accumulation information as thermal infrared satellite observations and more information than monthly rain gauge observations in poorly instrumented regions.

Published
2011
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8. Physically based estimation of bare-surface soil moisture with the passive radiometers

Author

Jean-Pierre Wigneron, Thomas J. Jackson, Jiancheng Shi, Lixin Zhang, Lingmei Jiang, Andre Chanzy, Kun-Shan Chen, State key Laboratory of Remote Sensing Science, University of California [Santa Barbara] (UCSB), University of California, Beijing Normal University (BNU), National Central University, Écologie fonctionnelle et physique de l'environnement (EPHYSE), Institut National de la Recherche Agronomique (INRA), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Hydrology and Remote Sensing Laboratory, and US Department of Agriculture [Beltsville] (USDA)

Subjects
Radiometer, 010504 meteorology & atmospheric sciences, C band, Scanning multichannel microwave radiometer, 0211 other engineering and technologies, 02 engineering and technology, Surface finish, [INFO.INFO-IA]Computer Science [cs]/Computer Aided Engineering, 01 natural sciences, Physics::Geophysics, Emissivity, Surface roughness, General Earth and Planetary Sciences, Environmental science, Special sensor microwave/imager, Electrical and Electronic Engineering, Microwave, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing
Abstract

International audience; A physically based bare-surface soil moisture inversion technique for application with passive microwave satellite measurements, including the Advanced Microwave-Scanning Radiometer-Earth Observing System, Special Sensor Microwave/Imager, Scanning Multichannel Microwave Radiometer, and Tropical Rainfall Measuring Mission Microwave Imager, was developed in this paper. The inversion technique is based on the concept of a simple parameterized surface emission model, the Q(p) model, which was developed using advanced integral equation model simulations of microwave emission. Through evaluation of the relationship between roughness parameters Q(p) at different polarizations, it was found that they could be described by a linear function. Using this relationship and the surface enrissivities measured from two polarizations, the effect of the surface roughness is cancelled out. In other words, this approach consisted in adding different weights on the v and h polarization measurements so as to minimize the surface roughness effects. This method leads to a dual-polarization inversion technique for the estimation of the surface dielectric properties directly from the emissivity measurements. For validation, we compared the soil moisture estimates, derived from ground radiometer measurements at C- to Ka-band obtained from the Institute National de Recherches Agronorniques' field experimental data in 1993 and the Beltsville Agricultural Research Center's field experimental data at C- and X-band obtained in 1979-1982, with the field in situ soil moisture measurements. The accuracies [root-mean-square error (rmse)] are higher than 4% for the available experimental data at the incidence angles of 50 degrees and 60 degrees. The newly developed inversion technique should be very useful in monitoring global soil moisture properties using the currently available satellite instruments that commonly have incidence angles between 50 degrees and 55 degrees.

Published
2006
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9. Comparison of land surface emissivity and radiometric temperature derived from MODIS and ASTER sensors

Author

Frédéric Jacob, Franc̨ois Petitcolin, Eric Vermote, Thomas J. Schmugge, Kenta Ogawa, Andrew N. French, Laboratoire d'étude des Interactions Sol - Agrosystème - Hydrosystème (UMR LISAH), Institut de Recherche pour le Développement (IRD)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), New Mexico State University, Department of Geography, University of Maryland [College Park], University of Maryland System-University of Maryland System, United States Department of Agriculture (USDA), Hitachi Ltd, Hydrology and Remote Sensing Laboratory, US Department of Agriculture [Beltsville] (USDA), Ecole supérieure d'agriculture de Purpan (ESAP), Analytic and Computational Research, Inc. - Earth Sciences (ACRI-ST), and NASA Goddard Space Flight Center (GSFC)

Subjects
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences, TISIE, 010504 meteorology & atmospheric sciences, [SDU.STU.GP]Sciences of the Universe [physics]/Earth Sciences/Geophysics [physics.geo-ph], [SDE.MCG]Environmental Sciences/Global Changes, 0211 other engineering and technologies, [SDV.SA.AGRO]Life Sciences [q-bio]/Agricultural sciences/Agronomy, Soil Science, 02 engineering and technology, Surface emissivity and radiometric temperature, 01 natural sciences, Spatial variability, ASTER, Atmospheric corrections, Emissivity, Radiative transfer, Computers in Earth Sciences, [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, ComputingMilieux_MISCELLANEOUS, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, [SDV.EE]Life Sciences [q-bio]/Ecology, environment, Radiometer, MODTRAN, [SDE.IE]Environmental Sciences/Environmental Engineering, Atmospheric correction, Geology, MIR/TIR remote sensing, [SPI.ELEC]Engineering Sciences [physics]/Electromagnetism, MODIS, 13. Climate action, Brightness temperature, [SPI.OPTI]Engineering Sciences [physics]/Optics / Photonic, Environmental science, Radiometry, Moderate-resolution imaging spectroradiometer, [SDV.EE.BIO]Life Sciences [q-bio]/Ecology, environment/Bioclimatology, TES, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; This study compared surface emissivity and radiometric temperature retrievals derived from data collected with the MODerate resolution Imaging Spectroradiometer (MODIS) and Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) sensors, onboard the NASA's Earth Observation System (EOS)-TERRA satellite. Two study sites were selected: a semi-arid area located in northern Chihuahuan desert, USA, and a Savannah landscape located in central Africa. Atmospheric corrections were performed using the MODTRAN 4 atmospheric radiative transfer code along with atmospheric profiles generated by the National Center for Environmental Predictions (NCEP). Atmospheric radiative properties were derived from MODTRAN 4 calculations according to the sensor swaths, which yielded different strategies from one sensor to the other. The MODIS estimates were then computed using a designed Temperature-Independent Spectral Indices of Emissivity (TISIE) method. The ASTER estimates were derived using the Temperature Emissivity Separation (TES) algorithm. The MODIS and ASTER radiometric temperature retrievals were in good agreement when the atmospheric corrections were similar, with differences lower than 0.9 K. The emissivity estimates were compared for MODIS/ASTER matching bands at 8.5 and 11 μm. It was shown that the retrievals agreed well, with RMSD ranging from 0.005 to 0.015, and biases ranging from −0.01 to 0.005. At 8.5 μm, the ranges of emissivities from both sensors were very similar. At 11 μm, however, the ranges of MODIS values were broader than those of the ASTER estimates. The larger MODIS values were ascribed to the gray body problem of the TES algorithm, whereas the lower MODIS values were not consistent with field references. Finally, we assessed the combined effects of spatial variability and sensor resolution. It was shown that for the study areas we considered, these effects were not critical.

Published
2004
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10. Mapping surface fluxes using airborne visible, near infrared, thermal infrared remote sensing data and a spatialized surface energy balance model

Author

Albert Olioso, Zhongbo Su, Bernard Seguin, Xing Fa Gu, Frédéric Jacob, US Department of Agriculture [Beltsville] (USDA), Unité Climat, Sol et Environnement (CSE), Institut National de la Recherche Agronomique (INRA), Wageningen Environmental Research (Alterra), Faculty of Geo-Information Science and Earth Observation, Department of Water Resources, and UT-I-ITC-WCC

Subjects
Convection, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, SEBAL, difference vegetation index, Alterra - Centrum Geo-informatie, télédétection, near infrared, 0207 environmental engineering, Energy balance, spatialization, 02 engineering and technology, Sensible heat, WRS, heat-flux, sensed data, Wind speed, remote sensing, bilan d'énergie de surface, ADLIB-ART-2197, 020701 environmental engineering, thematic mapper, Physics::Atmospheric and Oceanic Physics, satellite data, Remote sensing, infrarouge thermique, soil-water content, proche infrarouge, surface energy balance, hemispherical reflectance, semiarid rangeland, 04 agricultural and veterinary sciences, Albedo, Centre Geo-information, regional-scale, spatialisation, visible, Heat flux, 13. Climate action, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, thermal infrared, Spatial variability, leaf-area index, Agronomy and Crop Science
Abstract

International audience; A spatialized surface energy balance model was validated over the database acquired in the framework of the ReSeDA program. The benefit of the SEBAL model we considered was to compute wind speed and air temperature using the information contained in the spatial variability of convective fluxes. The multitemporal database allowed performing a validation over cycles of several crops. Problems induced by mixed pixels were reduced using high spatial resolution remote sensing data. We verified the validity of the model basic assumption, i.e. the simultaneous presence of partial areas with very high and very low evaporation rates, and the resulting relation between surface temperature and albedo. Besides, the model provided estimates of wind speed and air temperature close to the field references. The validation of soil heat flux showed the inadequacy of the empirical relationship used through a significant underestimation of the references. The validation of sensible heat flux provided similar results as compared to previous studies that dealt with model validations over databases including numerous situations.; Nous avons validé un modèle de bilan d'énergie de surface en utilisant la base de données ReSeDA. L'intérêt du modèle SEBAL était d'estimer la vitesse du vent et la température de l'air à partir de l'information contenue dans la variabilité spatiale des flux convectifs. La base de données multitemporelle permettait d'effectuer une validation en considérant les cycles de plusieurs cultures. Les problèmes dûs aux pixels mixtes étaient réduits par l'utilisation de données télédétectées à haute résolution spatiale. Nous avons pu vérifier la validité de l'hypothèse de base du modèle, i.e. la présence simultanée de zones très évaporantes ou peu évaporantes, ainsi que la relation entre albedo et température qui en résulte. De plus, le modèle a fourni des estimations de la vitesse du vent et de la température de l'air proche des mesures de référence. La validation du flux de chaleur dans le sol a montré que la relation empirique générait une sous-estimation importante. La validation du flux de chaleur sensible a fourni des résultats comparables à ceux rencontrés dans la littérature et concernant la validation de modèles sur des bases de données incluant de nombreuses situations.

Published
2002
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