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Recent Advances in Satellite Derived Global Land Product Validation

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

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 62900

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Special Issue Editors

Dr. Fernando Camacho

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Guest Editor

Earth Observation Laboratory (EOLAB), Parc Cientific University of Valencia, C/ Catedràtic Agustín Escardino, 9, 46980 Paterna, Valencia, Spain
Interests: retrieval of biophysical variables from satellite data; validation of satellite-derived global land products; fiducial reference measurements; cal/val field campaigns; climate data records of terrestrial ECVs; agriculture monitoring; climate change awareness
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Jadu Dash

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Guest Editor

Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK
Interests: land surface phenology; earth observation; biophysical variables; agriculture
Special Issues, Collections and Topics in MDPI journals

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

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Keywords

Land products
Accuracy
Uncertainty
Stability
Validation
Ground measurements
Upscaling
Intercomparison

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Published Papers (13 papers)

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Research

Jump to: Review, Other

26 pages, 9121 KiB

Open AccessArticle

Fiducial Reference Measurements for Vegetation Bio-Geophysical Variables: An End-to-End Uncertainty Evaluation Framework

by Luke A. Brown, Fernando Camacho, Vicente García-Santos, Niall Origo, Beatriz Fuster, Harry Morris, Julio Pastor-Guzman, Jorge Sánchez-Zapero, Rosalinda Morrone, James Ryder, Joanne Nightingale, Valentina Boccia and Jadunandan Dash

Remote Sens. 2021, 13(16), 3194; https://doi.org/10.3390/rs13163194 - 12 Aug 2021

Cited by 23 | Viewed by 4166

Abstract

With a wide range of satellite-derived vegetation bio-geophysical products now available to users, validation efforts are required to assess their accuracy and fitness for purpose. Substantial progress in the validation of such products has been made over the last two decades, but quantification of the uncertainties associated with in situ reference measurements is rarely performed, and the incorporation of uncertainties within upscaling procedures is cursory at best. Since current validation practices assume that reference data represent the truth, our ability to reliably demonstrate compliance with product uncertainty requirements through conformity testing is limited. The Fiducial Reference Measurements for Vegetation (FRM4VEG) project, initiated by the European Space Agency, is aiming to address this challenge by applying metrological principles to vegetation and surface reflectance product validation. Following FRM principles, and in accordance with the International Standards Organisation’s (ISO) Guide to the Expression of Uncertainty in Measurement (GUM), for the first time, we describe an end-to-end uncertainty evaluation framework for reference data of two key vegetation bio-geophysical variables: the fraction of absorbed photosynthetically active radiation (FAPAR) and canopy chlorophyll content (CCC). The process involves quantifying the uncertainties associated with individual in situ reference measurements and incorporating these uncertainties within the upscaling procedure (as well as those associated with the high-spatial-resolution imagery used for upscaling). The framework was demonstrated in two field campaigns covering agricultural crops (Las Tiesas–Barrax, Spain) and deciduous broadleaf forest (Wytham Woods, UK). Providing high-spatial-resolution reference maps with per-pixel uncertainty estimates, the framework is applicable to a range of other bio-geophysical variables including leaf area index (LAI), the fraction of vegetation cover (FCOVER), and canopy water content (CWC). The proposed procedures will facilitate conformity testing of moderate spatial resolution vegetation bio-geophysical products in future validation exercises. Full article

(This article belongs to the Special Issue Recent Advances in Satellite Derived Global Land Product Validation)

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10 pages, 4942 KiB

Open AccessCommunication

Casual Rerouting of AERONET Sun/Sky Photometers: Toward a New Network of Ground Measurements Dedicated to the Monitoring of Surface Properties?

by Dominique Carrer, Catherine Meurey, Olivier Hagolle, Guillaume Bigeard, Alexandre Paci, Jean-Marie Donier, Gilles Bergametti, Thierry Bergot, Jean-Christophe Calvet, Philippe Goloub, Stéphane Victori and Zhuosen Wang

Remote Sens. 2021, 13(16), 3072; https://doi.org/10.3390/rs13163072 - 4 Aug 2021

Cited by 1 | Viewed by 2219

Abstract

This paper presents an innovative method for observing vegetation health at a very high spatial resolution (~5 × 5 cm) and low cost by upgrading an existing Aerosol RObotic NETwork (AERONET) ground station dedicated to the observation of aerosols in the atmosphere. This study evaluates the capability of a sun/sky photometer to perform additional surface reflectance observations. The ground station of Toulouse, France, which belongs to the AERONET sun/sky photometer network, is used for this feasibility study. The experiment was conducted for a 5-year period (between 2016 and 2020). The sun/sky photometer was mounted on a metallic structure at a height of 2.5 m, and the acquisition software was adapted to add a periodical (every hour) ground-observation scenario with the sun/sky photometer observing the surface instead of being inactive. Evaluation is performed by using a classical metric characterizing the vegetation health: the normalized difference vegetation index (NDVI), using as reference the satellite NDVI derived from a Sentinel-2 (S2) sensor at 10 × 10 m resolution. Comparison for the 5-year period showed good agreement between the S2 and sun/sky photometer NDVIs (i.e., bias = 0.004, RMSD = 0.082, and R = 0.882 for a mean value of S2A NDVI around 0.6). Discrepancies could have been due to spatial-representativeness issues (of the ground measurement compared to S2), the differences between spectral bands, and the quality of the atmospheric correction applied on S2 data (accuracy of the sun/sky photometer instrument was better than 0.1%). However, the accuracy of the atmospheric correction applied on S2 data in this station appeared to be of good quality, and no dependence on the presence of aerosols was observed. This first analysis of the potential of the CIMEL CE318 sun/sky photometer to monitor the surface is encouraging. Further analyses need to be carried out to estimate the potential in different AERONET stations. The occasional rerouting of AERONET stations could lead to a complementary network of surface reflectance observations. This would require an update of the software, and eventual adaptations of the measurement platforms to the station environments. The additional cost, based on the existing AERONET network, would be quite limited. These new surface measurements would be interesting for measurements of vegetation health (monitoring of NDVI, and also of other vegetation indices such as the leaf area and chlorophyll indices), for validation and calibration exercise purposes, and possibly to refine various scientific algorithms (i.e., algorithms dedicated to cloud detection or the AERONET aerosol retrieval algorithm itself). CIMEL is ready to include the ground scenario used in this study in all new sun/sky photometers. Full article

(This article belongs to the Special Issue Recent Advances in Satellite Derived Global Land Product Validation)

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18 pages, 4271 KiB

Open AccessArticle

Global Land Cover Assessment Using Spatial Uniformity Validation Dataset

by Yoshie Ishii, Koki Iwao and Tsuguki Kinoshita

Remote Sens. 2021, 13(15), 2950; https://doi.org/10.3390/rs13152950 - 27 Jul 2021

Cited by 1 | Viewed by 2770

Abstract

The Degree Confluence Project (DCP) is a volunteer-based validation dataset that comprises useful information for global land cover map validation. However, there is a problem with using DCP points as validation data for the accuracy assessment of land cover maps. While resolutions of typical global land cover maps are several hundred meters to several kilometers, DCP points can only guarantee an area of several tens of meters that can be confirmed by ground photographs. So, the objective of this study is to create a land cover map validation dataset with added spatial uniformity information using satellite images and DCP points. For this, we devised a new method to semiautomatically guarantee the spatial uniformity of DCP validation data points at any resolution. This method can judge the validation data with guaranteed uniformity with a user’s accuracy of 0.954. Furthermore, we conducted the accuracy assessment for the existing global land cover maps by the DCP validation data with guaranteed spatial uniformity and found that the trends differed by class and region. Full article

(This article belongs to the Special Issue Recent Advances in Satellite Derived Global Land Product Validation)

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20 pages, 6337 KiB

Open AccessArticle

Comparison of Machine Learning Methods to Up-Scale Gross Primary Production

by Tao Yu, Qiang Zhang and Rui Sun

Remote Sens. 2021, 13(13), 2448; https://doi.org/10.3390/rs13132448 - 23 Jun 2021

Cited by 27 | Viewed by 3446

Abstract

Eddy covariance observation is an applicable way to obtain accurate and continuous carbon flux at flux tower sites, while remote sensing technology could estimate carbon exchange and carbon storage at regional and global scales effectively. However, it is still challenging to up-scale the field-observed carbon flux to a regional scale, due to the heterogeneity and the unstable air conditions at the land surface. In this paper, gross primary production (GPP) from ground eddy covariance systems were up-scaled to a regional scale by using five machine learning methods (Cubist regression tree, random forest, support vector machine, artificial neural network, and deep belief network). Then, the up-scaled GPP were validated using GPP at flux tower sites, weighted GPP in the footprint, and MODIS GPP products. At last, the sensitivity of the input data (normalized difference vegetation index, fractional vegetation cover, shortwave radiation, relative humidity and air temperature) to the precision of up-scaled GPP was analyzed, and the uncertainty of the machine learning methods was discussed. The results of this paper indicated that machine learning methods had a great potential in up-scaling GPP at flux tower sites. The validation of up-scaled GPP, using five machine learning methods, demonstrated that up-scaled GPP using random forest obtained the highest accuracy. Full article

(This article belongs to the Special Issue Recent Advances in Satellite Derived Global Land Product Validation)

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39 pages, 4288 KiB

Open AccessArticle

Parametric Models to Characterize the Phenology of the Lowveld Savanna at Skukuza, South Africa

by Hugo De Lemos, Michel M. Verstraete and Mary Scholes

Remote Sens. 2020, 12(23), 3927; https://doi.org/10.3390/rs12233927 - 30 Nov 2020

Cited by 4 | Viewed by 2931

Abstract

Mathematical models, such as the logistic curve, have been extensively used to model the temporal evolution of biological processes, though other similarly shaped functions could be (and sometimes have been) used for this purpose. Most previous studies focused on agricultural regions in the Northern Hemisphere and were based on the Normalized Difference Vegetation Index (NDVI). This paper compares the capacity of four parametric double S-shaped models (Gaussian, Hyperbolic Tangent, Logistic, and Sine) to represent the seasonal phenology of an unmanaged, protected savanna biome in South Africa’s Lowveld, using the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) generated by the Multi-angle Imaging SpectroRadiometer-High Resolution (MISR-HR) processing system on the basis of data originally collected by National Aeronautics and Space Administration (NASA)’s Multi-angle Imaging SpectroRadiometer (MISR) instrument since 24 February 2000. FAPAR time series are automatically split into successive vegetative seasons, and the models are inverted against those irregularly spaced data to provide a description of the seasonal fluctuations despite the presence of noise and missing values. The performance of these models is assessed by quantifying their ability to account for the variability of remote sensing data and to evaluate the Gross Primary Productivity (GPP) of vegetation, as well as by evaluating their numerical efficiency. Simulated results retrieved from remote sensing are compared to GPP estimates derived from field measurements acquired at Skukuza’s flux tower in the Kruger National Park, which has also been operational since 2000. Preliminary results indicate that (1) all four models considered can be adjusted to fit an FAPAR time series when the temporal distribution of the data is sufficiently dense in both the growing and the senescence phases of the vegetative season, (2) the Gaussian and especially the Sine models are more sensitive than the Hyperbolic Tangent and Logistic to the temporal distribution of FAPAR values during the vegetative season, and, in particular, to the presence of long temporal gaps in the observational data, and (3) the performance of these models to simulate the phenology of plants is generally quite sensitive to the presence of unexpectedly low FAPAR values during the peak period of activity and to the presence of long gaps in the observational data. Consequently, efforts to screen out outliers and to minimize those gaps, especially during the rainy season (vegetation’s growth phase), would go a long way to improve the capacity of the models to adequately account for the evolution of the canopy cover and to better assess the relation between FAPAR and GPP. Full article

(This article belongs to the Special Issue Recent Advances in Satellite Derived Global Land Product Validation)

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34 pages, 9162 KiB

Open AccessFeature PaperArticle

Quality Assessment of PROBA-V Surface Albedo V1 for the Continuity of the Copernicus Climate Change Service

by Jorge Sánchez-Zapero, Fernando Camacho, Enrique Martínez-Sánchez, Roselyne Lacaze, Dominique Carrer, Florian Pinault, Iskander Benhadj and Joaquín Muñoz-Sabater

Remote Sens. 2020, 12(16), 2596; https://doi.org/10.3390/rs12162596 - 12 Aug 2020

Cited by 10 | Viewed by 4459

Abstract

The Copernicus Climate Change Service (C3S) includes estimates of Essential Climate Variables (ECVs) as a series of Climate Data Records (CDRs) derived from satellite data. The C3S Surface Albedo (SA) v1.0 CDR is composed of observations from National Oceanic and Atmospheric Administration (NOAA) Very High Resolution Radiometers (AVHRR) (1981–2005), and VEGETATION sensors onboard Satellites for the Observation of the Earth (SPOT/VGT) (1998–2014) and Project for Onboard Autonomy satellite (PROBA-V) (2014–2020), and will continue with Sentinel-3 (from 2020 onwards). The goal of this study is to assess the uncertainties associated with the C3S PROBA-V SA v1.0 product, with a focus on the transition from SPOT/VGT to PROBA-V. The methodology followed the good practices recommended by the Land Product Validation sub-group (LPV) of the Working Group on Calibration and Validation (WGCV) of the Committee on Earth Observing Satellites (CEOS) for the validation of satellite-derived global albedo products. Several performance criteria were evaluated, including an intercomparison with National Aeronautics and Space Agency (NASA) MCD43A3 C6 products. C3S PROBA-V SA v1.0 and MCD43A3 C6 showed similar completeness but had higher fractions of missing data than C3S SPOT/VGT SA v1.0. C3S PROBA-V SA v1.0 showed similar precision (~1%) to MCD43A3 C6, improving the results of SPOT/VGT SA v1.0 (2–3%), but C3S PROBA-V SA v1.0 provided residual noise in the near-infrared (NIR). Good spatio-temporal continuity between C3S PROBA-V and SPOT/VGT SA v1.0 products was found with a mean bias between ±2%. The comparison with MCD43A3 C6 showed positive mean biases (5%, 8%, and 12% for visible, NIR and total shortwave, respectively). The accuracy assessment with ground measurements showed a median error of 18.4% with systematic overestimation (positive bias of 11.5%). The percentage of PROBA-V retrievals complying with the C3S target requirements was 28.6%. Full article

(This article belongs to the Special Issue Recent Advances in Satellite Derived Global Land Product Validation)

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37 pages, 16549 KiB

Open AccessFeature PaperArticle

Evaluation of Two Global Land Surface Albedo Datasets Distributed by the Copernicus Climate Change Service and the EUMETSAT LSA-SAF

by Gabriel Lellouch, Dominique Carrer, Chloé Vincent, Mickael Pardé, Sandra C. Frietas and Isabel F. Trigo

Remote Sens. 2020, 12(11), 1888; https://doi.org/10.3390/rs12111888 - 10 Jun 2020

Cited by 10 | Viewed by 4185

Abstract

The present paper is devoted to the quality assessment of two global land surface albedo products developed by Meteo France in the frame of the Copernicus Climate Change Service (C3S) and the LSA-SAF (Satellite Application Facility on Land Surface Analysis), herein called, respectively, VGT (VeGeTation) (the C3Sv1 dataset, derived from VGT sensors onboard Satellites for the Observation of the Earth, also called SPOT) and ETAL (European polar system Ten-day surface ALbedo, derived from Advanced Very High Resolution Radiometers (AVHRR) onboard METeorological OPerational (METOP) satellites). The evaluation study inter-compared these products with measurements at 33 ground stations and two independent operational products, MTAL-R/NRT (Meteosat second generation Ten-day ALbedo Reprocessed/Near Real-Time) and MODIS (MODerate resolution Imaging Spectroradiometer), over two distinct four-year periods. In accordance with the prescription from the Land Product Validation group of the joint Committee on Earth Observation Satellites (LPV/CEOS), the evaluation was addressed per land cover; furthermore, two albedo regimes were considered throughout the evaluation to distinguish between high (over 0.15) and low (below 0.15) surface albedo behaviors. First, we show that both VGT and ETAL products agree well with the measurements and the other satellite products at the ground stations. Second, when inter-compared with MODIS, the results are noteworthy for ETAL as opposed to VGT, with 11 out of 13 land cover types passing the Global Climate Observing System (GCOS) requirements for more than 80% of the sites for albedo values less than 0.15 (compared with none for VGT) and 10 out of 14 land cover types passing the GCOS requirements for more than 50% of the sites for albedo values greater than 0.15 (compared with 5 for VGT). Finally, a pixel-by-pixel analysis reveals that VGT overestimates the surface albedo as compared with MODIS by about 0.02 in absolute value for albedo values less than 0.15 and by about 22% in relative value for albedo values greater than 0.15. The root-mean-square-deviation (RMSD) in absolute value is about 0.015 for albedo values less than 0.15 and 51.5% in relative value for albedo values greater than 0.15. In contrast, the bias for ETAL when compared with MODIS remains very small. Over the four-year period, ETAL overestimates the surface albedo as compared with MODIS by 0.001 in absolute value for the regime of surface albedo less than 0.15 and by about 5.8% in relative value for albedo values greater than 0.15. The RMSD in absolute value is about 0.014 for albedo values less than 0.15 and 19.4% in relative value for albedo values greater than 0.15. Assuming that the MODIS product is a good reference, a relative bias of around 6% can be judged satisfactory for ETAL surface albedo. The lower performance of the VGT (C3Sv1) product is currently the subject of investigation. Work is ongoing to upgrade it further towards the final C3S product. Full article

(This article belongs to the Special Issue Recent Advances in Satellite Derived Global Land Product Validation)

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29 pages, 6174 KiB

Open AccessArticle

Quality Assessment of PROBA-V LAI, fAPAR and fCOVER Collection 300 m Products of Copernicus Global Land Service

by Beatriz Fuster, Jorge Sánchez-Zapero, Fernando Camacho, Vicente García-Santos, Aleixandre Verger, Roselyne Lacaze, Marie Weiss, Frederic Baret and Bruno Smets

Remote Sens. 2020, 12(6), 1017; https://doi.org/10.3390/rs12061017 - 22 Mar 2020

Cited by 105 | Viewed by 7908

Abstract

The Copernicus Global Land Service (CGLS) provides global time series of leaf area index (LAI), fraction of absorbed photosynthetically active radiation (fAPAR) and fraction of vegetation cover (fCOVER) data at a resolution of 300 m and a frequency of 10 days. We performed a quality assessment and validation of Version 1 Collection 300 m products that were consistent with the guidelines of the Land Product Validation (LPV) subgroup of the Committee on Earth Observation System (CEOS) Working Group on Calibration and Validation (WGCV). The spatiotemporal patterns of Collection 300 m V1 LAI, fAPAR and fCOVER products are consistent with CGLS Collection 1 km V1, Collection 1 km V2 and Moderate Resolution Imagery Spectroradiometer Collection 6 (MODIS C6) products. The Collection 300 m V1 products have good precision and smooth temporal profiles, and the interannual variations are consistent with similar satellite products. The accuracy assessment using ground measurements mainly over crops shows an overall root mean square deviation of 1.01 (44.3%) for LAI, 0.12 (22.2%) for fAPAR and 0.21 (42.6%) for fCOVER, with positive mean biases of 0.36 (15.5%), 0.05 (10.3%) and 0.16 (32.2%), respectively. The products meet the CGLS user accuracy requirements in 69.1%, 62.5% and 29.7% of the cases for LAI, fAPAR and fCOVER, respectively. The CGLS will continue the production of Collection 300 m V1 LAI, fAPAR and fCOVER beyond the end of the PROBA-V mission by using Sentinel-3 OLCI as input data. Full article

(This article belongs to the Special Issue Recent Advances in Satellite Derived Global Land Product Validation)

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25 pages, 4164 KiB

Open AccessArticle

Operational Soil Moisture from ASCAT in Support of Water Resources Management

by Khidir Abdalla Kwal Deng, Salim Lamine, Andrew Pavlides, George P. Petropoulos, Prashant K. Srivastava, Yansong Bao, Dionissios Hristopulos and Vasileios Anagnostopoulos

Remote Sens. 2019, 11(5), 579; https://doi.org/10.3390/rs11050579 - 9 Mar 2019

Cited by 21 | Viewed by 4526

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 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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18 pages, 3549 KiB

Open AccessArticle

Spatial Consistency Assessments for Global Land-Cover Datasets: A Comparison among GLC2000, CCI LC, MCD12, GLOBCOVER and GLCNMO

by Ting Hua, Wenwu Zhao, Yanxu Liu, Shuai Wang and Siqi Yang

Remote Sens. 2018, 10(11), 1846; https://doi.org/10.3390/rs10111846 - 21 Nov 2018

Cited by 78 | Viewed by 5710

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 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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Review

Jump to: Research, Other

43 pages, 7098 KiB

Open AccessReview

European Space Agency (ESA) Calibration/Validation Strategy for Optical Land-Imaging Satellites and Pathway towards Interoperability

by Fabrizio Niro, Philippe Goryl, Steffen Dransfeld, Valentina Boccia, Ferran Gascon, Jennifer Adams, Britta Themann, Silvia Scifoni and Georgia Doxani

Remote Sens. 2021, 13(15), 3003; https://doi.org/10.3390/rs13153003 - 30 Jul 2021

Cited by 28 | Viewed by 7634

Abstract

Land remote sensing capabilities in the optical domain have dramatically increased in the past decade, owing to the unprecedented growth of space-borne systems providing a wealth of measurements at enhanced spatial, temporal and spectral resolutions. Yet, critical questions remain as how to unlock the potential of such massive amounts of data, which are complementary in principle but inherently diverse in terms of products specifications, algorithm definition and validation approaches. Likewise, there is a recent increase in spatiotemporal coverage of in situ reference data, although inconsistencies in the used measurement practices and in the associated quality information still hinder their integrated use for satellite products validation. In order to address the above-mentioned challenges, the European Space Agency (ESA), in collaboration with other Space Agencies and international partners, is elaborating a strategy for establishing guidelines and common protocols for the calibration and validation (Cal/Val) of optical land imaging sensors. Within this paper, this strategy will be illustrated and put into the context of current validation systems for land remote sensing. A reinforced focus on metrology is the basic principle underlying such a strategy, since metrology provides the terminology, the framework and the best practices, allowing to tie measurements acquired from a variety of sensors to internationally agreed upon standards. From this general concept, a set of requirements are derived on how the measurements should be acquired, analysed and quality reported to users using unified procedures. This includes the need for traceability, a fully characterised uncertainty budget and adherence to community-agreed measurement protocols. These requirements have led to the development of the Fiducial Reference Measurements (FRM) concept, which is promoted by the ESA as the recommended standard within the satellite validation community. The overarching goal is to enhance user confidence in satellite-based data and characterise inter-sensor inconsistencies, starting from at-sensor radiances and paving the way to achieving the interoperability of current and future land-imaging systems. Full article

(This article belongs to the Special Issue Recent Advances in Satellite Derived Global Land Product Validation)

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34 pages, 8255 KiB

Open AccessEditor’s ChoiceReview

Validation of Earth Observation Time-Series: A Review for Large-Area and Temporally Dense Land Surface Products

by Stefan Mayr, Claudia Kuenzer, Ursula Gessner, Igor Klein and Martin Rutzinger

Remote Sens. 2019, 11(22), 2616; https://doi.org/10.3390/rs11222616 - 8 Nov 2019

Cited by 27 | Viewed by 7189

Abstract

Large-area remote sensing time-series offer unique features for the extensive investigation of our environment. Since various error sources in the acquisition chain of datasets exist, only properly validated results can be of value for research and downstream decision processes. This review presents an overview of validation approaches concerning temporally dense time-series of land surface geo-information products that cover the continental to global scale. Categorization according to utilized validation data revealed that product intercomparisons and comparison to reference data are the conventional validation methods. The reviewed studies are mainly based on optical sensors and orientated towards global coverage, with vegetation-related variables as the focus. Trends indicate an increase in remote sensing-based studies that feature long-term datasets of land surface variables. The hereby corresponding validation efforts show only minor methodological diversification in the past two decades. To sustain comprehensive and standardized validation efforts, the provision of spatiotemporally dense validation data in order to estimate actual differences between measurement and the true state has to be maintained. The promotion of novel approaches can, on the other hand, prove beneficial for various downstream applications, although typically only theoretical uncertainties are provided. Full article

(This article belongs to the Special Issue Recent Advances in Satellite Derived Global Land Product Validation)

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Mapping Fragmented Impervious Surface Areas Overlooked by Global Land-Cover Products in the Liping County, Guizhou Province, China

by Jing Zhao and Narumasa Tsutsumida

Remote Sens. 2020, 12(9), 1527; https://doi.org/10.3390/rs12091527 - 11 May 2020

Cited by 5 | Viewed by 3485

Abstract

Imperviousness is an important indicator for monitoring urbanization and environmental changes, and is evaluated widely in urban areas, but not in rural areas. An accurate impervious surface area (ISA) map in rural areas is essential to achieve environmental conservation and sustainable rural development. Global land-cover products such as MODIS MCD12Q1, ESA CCI-LC, and Global Urban Land are common resources for environmental practitioners to collect land-cover information including ISAs. However, global products tend to focus on large ISA agglomerations and may not identify fragmented ISA extents in less populated regions. Land-use planners and practitioners have to map ISAs if it is difficult to obtain such spatially explicit information from local governments. A common and consistent approach for rural ISA mapping is yet to be established. A case study of the Liping County, a typical rural region in southwest China, was undertaken with the objectives of assessing the global land-cover products in the context of rural ISA mapping and proposing a simple and feasible approach for the mapping. This approach was developed using Landsat 8 imagery and by applying a random forests classifier. An appropriate number of training samples were distributed to towns or villages across all townships in the study area for classification. The results demonstrate that the global land-cover products identified major ISA agglomerations, specifically at the county seat; however, other fragmented ISAs over the study area were overlooked. In contrast, the map created using the developed approach inferred ISAs across all townships with an overall accuracy of 91%. A large amount of training samples together with geographic information of towns or villages is the key suggestion to identify and map ISAs in rural areas. Full article

(This article belongs to the Special Issue Recent Advances in Satellite Derived Global Land Product Validation)

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