Prediction of Common Surface Soil Properties Based on Vis-NIR Airborne and Simulated EnMAP Imaging Spectroscopy Data: Prediction Accuracy and Influence of Spatial Resolution

Steinberg, Andreas; Chabrillat, Sabine; Stevens, Antoine; Segl, Karl; Foerster, Saskia

doi:10.3390/rs8070613

Cited by 79 publications

(55 citation statements)

References 50 publications

(49 reference statements)

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“…Gomez et al [5] attributed the lower accuracy to two factors: the noise in the Hyperion spectra and the medium spatial resolution concealing the spectral features of soil organic matter. Other studies mapping soil carbon content also obtained contrasted results and attributed the low accuracy prediction to the spatial resolution of Hyperion [3] or EnMAP [6]. Steinberg et al [6] predicted organic carbon content, but also mapped other soil properties such as clay and iron oxides' content with simulated EnMAP images.…”

Section: Soil Applicationsmentioning

confidence: 91%

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

et al. 2018

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In the last few decades, researchers have developed a plethora of hyperspectral Earth Observation (EO) remote sensing techniques, analysis and applications. While hyperspectral exploratory sensors are demonstrating their potential, Sentinel-2 multispectral satellite remote sensing is now providing free, open, global and systematic high resolution visible and infrared imagery at a short revisit time. Its recent launch suggests potential synergies between multi-and hyper-spectral data. This study, therefore, reviews 20 years of research and applications in satellite hyperspectral remote sensing through the analysis of Earth observation hyperspectral sensors' publications that cover the Sentinel-2 spectrum range: Hyperion, TianGong-1, PRISMA, HISUI, EnMAP, Shalom, HyspIRI and HypXIM. More specifically, this study (i) brings face to face past and future hyperspectral sensors' applications with Sentinel-2's and (ii) analyzes the applications' requirements in terms of spatial and temporal resolutions. Eight main application topics were analyzed including vegetation, agriculture, soil, geology, urban, land use, water resources and disaster. Medium spatial resolution, long revisit time and low signal-to-noise ratio in the short-wave infrared of some hyperspectral sensors were highlighted as major limitations for some applications compared to the Sentinel-2 system. However, these constraints mainly concerned past hyperspectral sensors, while they will probably be overcome by forthcoming instruments. Therefore, this study is putting forward the compatibility of hyperspectral sensors and Sentinel-2 systems for resolution enhancement techniques in order to increase the panel of hyperspectral uses.

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Section: Soil Applicationsmentioning

confidence: 91%

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

et al. 2018

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“…Many authors report SOC estimation from airborne data, especially exploiting the VNIR-SWIR region [10][11][12][13]16,62,63], but also the long-wave infrared region (LWIR; 8-14 µm) [15]. However, all of them build a calibration dataset for the airborne spectra on samples analysed in the laboratory (i.e., the traditional approach).…”

Section: Discussionmentioning

confidence: 99%

“…As the band width of the main airborne sensors is smaller than 40 nm, their spectral range allows for most of the soil chromophores to be exploited. There is an increasing number of papers concerning the estimation of SOC exploiting airborne hyperspectral data [10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

“…Most authors directly calibrate the airborne or satellite spectra on soil samples collected from the corresponding pixels [16,19,[21][22][23]29]. This involves collection of soil samples within bare fields, laboratory measurement of the target variable, and calibration of a multivariate model linking the quantity of the target variable with the spectra extracted from the remote-sensing instrument at the sampling points.…”

Section: Introductionmentioning

confidence: 99%

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Soil Organic Carbon Estimation in Croplands by Hyperspectral Remote APEX Data Using the LUCAS Topsoil Database

et al. 2018

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Abstract:The most commonly used approach to estimate soil variables from remote-sensing data entails time-consuming and expensive data collection including chemical and physical laboratory analysis. Large spectral libraries could be exploited to decrease the effort of soil variable estimation and obtain more widely applicable models. We investigated the feasibility of a new approach, referred to as bottom-up, to provide soil organic carbon (SOC) maps of bare cropland fields over a large area without recourse to chemical analyses, employing both the pan-European topsoil database from the Land Use/Cover Area frame statistical Survey (LUCAS) and Airborne Prism Experiment (APEX) hyperspectral airborne data. This approach was tested in two areas having different soil characteristics: the loam belt in Belgium, and the Gutland-Oesling region in Luxembourg. Partial least square regression (PLSR) models were used in each study area to estimate SOC content, using both bottom-up and traditional approaches. The PLSR model's accuracy was tested on an independent validation dataset. Both approaches provide SOC maps having a satisfactory level of accuracy (RMSE = 1.5-4.9 g·kg −1 ; ratio of performance to deviation (RPD) = 1.4-1.7) and the inter-comparison did not show differences in terms of RMSE and RPD either in the loam belt or in Luxembourg. Thus, the bottom-up approach based on APEX data provided high-resolution SOC maps over two large areas showing the within-and between-field SOC variability.

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“…However, testing all unlabeled samples exhaustively from a large dataset is inefficient. It is especially true for hyperspectral imaging applications, in which a large population of soil spectra can be collected [48][49][50]. In such a scenario, testing all available unlabeled samples is impractical.…”

Section: Discussionmentioning

confidence: 99%

Improving Spectral Estimation of Soil Organic Carbon Content through Semi-Supervised Regression

et al. 2017

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Visible and near infrared (VIS-NIR) spectroscopy has been applied to estimate soil organic carbon (SOC) content with many modeling strategies and techniques, in which a crucial and challenging problem is to obtain accurate estimations using a limited number of samples with reference values (labeled samples). To solve such a challenging problem, this study, with Honghu City (Hubei Province, China) as a study area, aimed to apply semi-supervised regression (SSR) to estimate SOC contents from VIS-NIR spectroscopy. A total of 252 soil samples were collected in four field campaigns for laboratory-based SOC content determinations and spectral measurements. Semi-supervised regression with co-training based on least squares support vector machine regression (Co-LSSVMR) was applied for spectral estimations of SOC contents, and it was further compared with LSSVMR. Results showed that Co-LSSVMR could improve the estimations of SOC contents by exploiting samples without reference values (unlabeled samples) when the number of labeled samples was not excessively small and produce better estimations than LSSVMR. Therefore, SSR could reduce the number of labeled samples required in calibration given an accuracy threshold, and it holds advantages in SOC estimations from VIS-NIR spectroscopy with a limited number of labeled samples. Considering the increasing popularity of airborne platforms and sensors, SSR might be a promising modeling technique for SOC estimations from remotely sensed hyperspectral images.

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Prediction of Common Surface Soil Properties Based on Vis-NIR Airborne and Simulated EnMAP Imaging Spectroscopy Data: Prediction Accuracy and Influence of Spatial Resolution

Cited by 79 publications

References 50 publications

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

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

Soil Organic Carbon Estimation in Croplands by Hyperspectral Remote APEX Data Using the LUCAS Topsoil Database

Improving Spectral Estimation of Soil Organic Carbon Content through Semi-Supervised Regression

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