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Special Issue "Recent Advances in Satellite Derived Global Land Product Validation"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Biogeosciences Remote Sensing".

Deadline for manuscript submissions: 30 June 2019

Special Issue Editors

Guest Editor
Dr. Fernando Camacho

EOLAB, C/Catedrátic Agustín Escardino, 9, 46980 Paterna, Valencia, Spain
Website | E-Mail
Interests: retrieval of biophysical variables from satellite data; validation of satellite-derived land products; cal/val field campaigns; climate data records of vegetation variables; agriculture monitoring; climate change awareness
Guest Editor
Prof. Jadu Dash

Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK
Website | E-Mail
Phone: +44(0)23 8059 2203
Interests: land surface phenology; Earth observation; biophysical variables; agriculture

Special Issue Information

Dear Colleagues,

The retrieval of global land properties from space has entered into an operational phase with a multiplicity of Earth Observation services and space agencies delivering bio-geophysical variables over land at global scale and from a wide range of spaceborne sensors at different spatial and temporal resolutions. In particular, climate data records (CDR) of terrestrial Essential Climate Variables (ECVs) are being produced in support of Global Climate Observing System (GCOS) exploiting past and current satellite observations. The quality of these global land products and CDR of ECVs must be assessed by independent means to inform users on the uncertainties attached to these satellite derived land products. Global validation of land products is however a challenging task due to the variety of conditions encountered at global scale and by the spatial and temporal mismatch between ground references and the satellite products, which requires well-established methodologies for the collection of fiducial ground measurements, and for performing the validation over global conditions. Due to nature of these applications information on uncertainty and traceability are crucial.
This Special Issue aims at collecting recent developments, methodologies, and best practices for global land product validation and ground data collection, as well as the latest results on validation of global land products.

• Satellite-derived land products validation methodologies and best practices
• Methods to estimate accuracy, uncertainty and traceability of bio-geophysical variables
• Recent results on validation of global satellite derived land products and CDR of ECVs
• Protocols and best practice for in-situ data collection of bio-geophysical variables
• New tools and techniques for in-situ data collection of bio-geophysical variables
• Fiducial reference measurements in support of satellite-derived land products validation
• Upscaling methodologies, from point measurements to satellite resolution
• Networks of sites and supersites for global satellite-derived land products validation
• Benchmarking exercises between existing global satellite-derived land products

Dr. Fernando Camacho
Dr. Jadu Dash
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Land products
  • Accuracy
  • Uncertainty
  • Stability
  • Validation
  • Ground measurements
  • Upscaling
  • Intercomparison

Published Papers (2 papers)

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Research

Open AccessArticle Operational Soil Moisture from ASCAT in Support of Water Resources Management
Remote Sens. 2019, 11(5), 579; https://doi.org/10.3390/rs11050579
Received: 9 February 2019 / Revised: 26 February 2019 / Accepted: 5 March 2019 / Published: 9 March 2019
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Abstract
This study provides the results of an extensive investigation of the Advanced Scaterometter (ASCAT) surface soil moisture global operational product accuracy across three continents (United States of America (USA), Europe, and Australia). ASCAT predictions of surface soil moisture were compared against near concurrent [...] Read more.
This study provides the results of an extensive investigation of the Advanced Scaterometter (ASCAT) surface soil moisture global operational product accuracy across three continents (United States of America (USA), Europe, and Australia). ASCAT predictions of surface soil moisture were compared against near concurrent in situ measurements from the FLUXNET observational network. A total of nine experimental sites were used to assess the accuracy of ASCAT Surface Soil Moisture (ASCAT SSM) predictions for two complete years of observations (2010, 2011). Results showed a generally reasonable agreement between the ASCAT product and the in situ soil moisture measurements in the 0–5 cm soil moisture layer. The Root Mean Square Error (RMSE) was below 0.135 m3 m−3 at all of the sites. With a few exceptions, Pearson’s correlation coefficient was above 45%. Grassland, shrublands, and woody savanna land cover types exhibited satisfactory agreement in all the sites analyzed (RMSE ranging from 0.05 to 0.13 m3 m−3). Seasonal performance was tested, but no definite conclusion can be made with statistical significance at this time, as the seasonal results varied from continent to continent and from year to year. However, the satellite and in situ measurements for Needleleaf forests were practically uncorrelated (R = −0.11 and −0.04). ASCAT predictions overestimated the observed values at all of the sites in Australia. A positive bias of approximately 0.05 m3 m−3 was found with respect to the observed values that were in the range 0–0.3 m3 m−3. Better agreement was observed for the grassland sites in most cases (RMSE ranging from 0.09 to 0.10 m3 m−3 and R from 0.46 to 0.90). Our results provide supportive evidence regarding the potential value of the ASCAT global operational product for meso-scale studies and the relevant practical applications. A key contribution of this study is a comprehensive evaluation of ASCAT product soil moisture estimates at different sites around the globe. These sites represent a variety of climatic, environmental, biome, and topographical conditions. Full article
(This article belongs to the Special Issue Recent Advances in Satellite Derived Global Land Product Validation)
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Open AccessArticle Spatial Consistency Assessments for Global Land-Cover Datasets: A Comparison among GLC2000, CCI LC, MCD12, GLOBCOVER and GLCNMO
Remote Sens. 2018, 10(11), 1846; https://doi.org/10.3390/rs10111846
Received: 27 September 2018 / Revised: 11 November 2018 / Accepted: 16 November 2018 / Published: 21 November 2018
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Abstract
Numerous global-scale land-cover datasets have greatly contributed to the study of global environmental change and the sustainable management of natural resources. However, land-cover datasets inevitably experience information loss because of the nature of the uncertainty in the interpretation of remote-sensing images. Therefore, analyzing [...] Read more.
Numerous global-scale land-cover datasets have greatly contributed to the study of global environmental change and the sustainable management of natural resources. However, land-cover datasets inevitably experience information loss because of the nature of the uncertainty in the interpretation of remote-sensing images. Therefore, analyzing the spatial consistency of multi-source land-cover datasets on the global scale is important to maintain the consistency of time and consider the effects of land-cover changes on spatial consistency. In this study, we assess the spatial consistency of five land-cover datasets, namely, GLC2000, CCI LC, MCD12, GLOBCOVER and GLCNMO, at the global and continental scales through climate and elevation partitions. The influencing factors of surface conditions and data producers on the spatial inconsistency are discussed. The results show that the global overall consistency of the five datasets ranges from 49.2% to 67.63%. The spatial consistency of Europe is high, and the multi-year value is 66.57%. In addition, the overall consistency in the EF climatic zone is very high, around 95%. The surface conditions and data producers affect the spatial consistency of land-cover datasets to different degrees. CCI LC and GLCNMO (2013) have the highest overall consistencies on the global scale, reaching 67.63%. Generally, the consistency of these five global land-cover datasets is relatively low, increasing the difficulty of satisfying the needs of high-precision land-surface-process simulations. Full article
(This article belongs to the Special Issue Recent Advances in Satellite Derived Global Land Product Validation)
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