Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context

Transon, Julie; d’Andrimont, Raphaël; Maugnard, Alexandre; Defourny, Pierre

doi:10.3390/rs10020157

Cited by 233 publications

(123 citation statements)

References 176 publications

(282 reference statements)

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“…In summary, the retrieval results of winter wheat and corn vary because-depending on canopy structure-different plant components manifest in the 970 nm water absorption band. Several other studies also raised the fact of canopy structural influence on crop variables retrieval [68,[70][71][72][73]. Cereal crops with prominent ears will not be satisfactorily modelled if the ear water content is not included in the in-situ measurements as it can be seen in the study of Champagne, et al [53].…”

Section: Dependency Of Canopy Water Detection On Canopy Structurementioning

confidence: 99%

Physically-Based Retrieval of Canopy Equivalent Water Thickness Using Hyperspectral Data

et al. 2018

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Quantitative equivalent water thickness on canopy level (EWT canopy ) is an important land surface variable and retrieving EWT canopy from remote sensing has been targeted by many studies. However, the effect of radiative penetration into the canopy has not been fully understood. Therefore, in this study the Beer-Lambert law is applied to inversely determine water content information in the 930 to 1060 nm range of canopy reflectance from measured winter wheat and corn spectra collected in 2015, 2017, and 2018. The spectral model was calibrated using a look-up-table (LUT) of 50,000 PROSPECT spectra. Internal model validation was performed using two leaf optical properties datasets (LOPEX93 and ANGERS). Destructive in-situ measurements of water content were collected separately for leaves, stalks, and fruits. Correlation between measured and modelled water content was most promising for leaves and ears in case of wheat, reaching coefficients of determination (R 2 ) up to 0.72 and relative RMSE (rRMSE) of 26% and in case of corn for the leaf fraction only (R 2 = 0.86, rRMSE = 23%). These findings indicate that, depending on the crop type and its structure, different parts of the canopy are observed by optical sensors. The results from the Munich-North-Isar test sites indicated that plant compartment specific EWT canopy allows us to deduce more information about the physical meaning of model results than from equivalent water thickness on leaf level (EWT) which is upscaled to canopy water content (CWC) by multiplication of the leaf area index (LAI). Therefore, it is suggested to collect EWT canopy data and corresponding reflectance for different crop types over the entire growing cycle. Nevertheless, the calibrated model proved to be transferable in time and space and thus can be applied for fast and effective retrieval of EWT canopy in the scope of future hyperspectral satellite missions.

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Section: Dependency Of Canopy Water Detection On Canopy Structurementioning

confidence: 99%

Physically-Based Retrieval of Canopy Equivalent Water Thickness Using Hyperspectral Data

et al. 2018

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“…PR provides powerful tools [34] for many different applications and research areas [35] such as scientific research, private and public industries, military activities, etc., [14,[21][22][23][24]32,[37][38][39][40][41][42][43][44][45][46][47][48]. For example, PR is important for geosciences as its tools are used to analyze geographical features of environments in digital images from remote sensing, i.e., scenes [11,12,[49][50][51][52][53][54]. Additionally, PR also provides powerful tools to help machine perception for CV [3,36].…”

mentioning

confidence: 99%

An Incongruence-Based Anomaly Detection Strategy for Analyzing Water Pollution in Images from Remote Sensing

et al. 2019

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The potential applications of computational tools, such as anomaly detection and incongruence, for analyzing data attract much attention from the scientific research community. However, there remains a need for more studies to determine how anomaly detection and incongruence applied to analyze data of static images from remote sensing will assist in detecting water pollution. In this study, an incongruence-based anomaly detection strategy for analyzing water pollution in images from remote sensing is presented. Our strategy semi-automatically detects occurrences of one type of anomaly based on the divergence between two image classifications (contextual and non-contextual). The results indicate that our strategy accurately analyzes the majority of images. Incongruence as a strategy for detecting anomalies in real-application (non-synthetic) data found in images from remote sensing is relevant for recognizing crude oil close to open water bodies or water pollution caused by the presence of brown mud in large rivers. It can also assist surveillance systems by detecting environmental disasters or performing mappings. Kittler et al. in [13] proposed a taxonomy of anomalies which expanded the concept of anomaly beyond the conventional meaning of outlier. They used sensory data quality assessment [26], contextual [9] and non-contextual [27][28][29][30] classifiers [5,[15][16][17][18], and an incongruence indicator [16][17][18] to identify each type of anomaly [13]. According to this taxonomy [13] anomalies can be, for example, of the types unknown object, measurement model drift, unknown structure, unexpected structural component, component model drift, and unexpected structure and structural components. This taxonomy [13] is well-known and widely accepted by the scientific community because it has the potential to be applied for solving problems in many different research areas [13]. Therefore, studies related to the application of the taxonomy [13] on synthetic data are common, such as in [16][17][18]. However, to the knowledge of the authors, studies have not addressed the practical application of the taxonomy [13] to solve real-world problems [2,[31][32][33], because it remains a challenge for all research areas.Anomaly detection [13] and incongruence [15][16][17][18] are two powerful computational tools from pattern recognition (PR) [3,[11][12][13][14][15][16][17][18]34,35] and computer vision (CV) [3,36]. PR is a scientific area of study dedicated to analyze patterns and regularities in data [3]. PR provides powerful tools [34] for many different applications and research areas [35] such as scientific research, private and public industries, military activities, etc., [14,[21][22][23][24]32,[37][38][39][40][41][42][43][44][45][46][47][48]. For example, PR is important for geosciences as its tools are used to analyze geographical features of environments in digital images from remote sensing, i.e., scenes [11,12,[49][50][51][52][53][54]. Additionally, PR also provides powerful tools to help machine perception...

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“…However, ice algae are taxonomically distinct from snow algae [291], and optical methods developed by the snow albedo community need to be adapted to detect ice algae or separate its effect on ice albedo from inorganic impurities or variations in ice grain size and structure [292,301]. At present, no spaceborne hyperspectral imager operates over the polar regions, although several hyperspectral missions are planned for the coming years [309]. Recently, multispectral reflectance imagery from the OLCI onboard Sentinel-3A was used to infer the spatiotemporal distribution of ice algal blooms in the southwest GrIS ablation zone, providing insight into the capability of enhanced spectral resolution for diagnosing albedo change in the polar regions [269].…”

Section: Current Challenges and Future Opportunitiesmentioning

confidence: 99%

Satellite Remote Sensing of the Greenland Ice Sheet Ablation Zone: A Review

Cooper

Smith

2019

Remote Sensing

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The Greenland Ice Sheet is now the largest land ice contributor to global sea level rise, largely driven by increased surface meltwater runoff from the ablation zone, i.e., areas of the ice sheet where annual mass losses exceed gains. This small but critically important area of the ice sheet has expanded in size by~50% since the early 1960s, and satellite remote sensing is a powerful tool for monitoring the physical processes that influence its surface mass balance. This review synthesizes key remote sensing methods and scientific findings from satellite remote sensing of the Greenland Ice Sheet ablation zone, covering progress in (1) radar altimetry, (2) laser (lidar) altimetry, (3) gravimetry, (4) multispectral optical imagery, and (5) microwave and thermal imagery. Physical characteristics and quantities examined include surface elevation change, gravimetric mass balance, reflectance, albedo, and mapping of surface melt extent and glaciological facies and zones. The review concludes that future progress will benefit most from methods that combine multi-sensor, multi-wavelength, and cross-platform datasets designed to discriminate the widely varying surface processes in the ablation zone. Specific examples include fusing laser altimetry, radar altimetry, and optical stereophotogrammetry to enhance spatial measurement density, cross-validate surface elevation change, and diagnose radar elevation bias; employing dual-frequency radar, microwave scatterometry, or combining radar and laser altimetry to map seasonal snow depth; fusing optical imagery, radar imagery, and microwave scatterometry to discriminate between snow, liquid water, refrozen meltwater, and bare ice near the equilibrium line altitude; combining optical reflectance with laser altimetry to map supraglacial lake, stream, and crevasse bathymetry; and monitoring the inland migration of snowlines, surface melt extent, and supraglacial hydrologic features.

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Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context

Cited by 233 publications

References 176 publications

Physically-Based Retrieval of Canopy Equivalent Water Thickness Using Hyperspectral Data

Physically-Based Retrieval of Canopy Equivalent Water Thickness Using Hyperspectral Data

An Incongruence-Based Anomaly Detection Strategy for Analyzing Water Pollution in Images from Remote Sensing

Satellite Remote Sensing of the Greenland Ice Sheet Ablation Zone: A Review

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