Surface soil moisture quantification and validation based on hyperspectral data and field measurements

Haubrock, Soeren-Nils

doi:10.1117/1.3059191

Cited by 47 publications

(28 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Microwave remote sensing (wavelength coverage 1 mm-1 000 mm) platforms are sensitive to water discrimination and are capable of almost all weather viewing, which is a distinct advantage (Alain and Robert, 2005;Song et al, 2007;Anguela et al, 2010). For water flux related ecosystem service modeling, the most important difference between optical and microwave remote sensing is the penetration depth and consequently the depth of the soil layer for which the water content can be quantified; the penetration depth for optical remote sensing is significantly less than for microwave sensing (Haubrock et al, 2008).…”

Section: Water Flux Related Ecosystem Servicesmentioning

confidence: 99%

“…Land surface containing water or moisture appears dark in contrast to surrounding vegetation (Swain and Davis, 1978). Soil moisture quantification models based on optical wavelength data take the surface radiant temperature as a proxy (Haubrock et al, 2008). Microwave remote sensing (wavelength coverage 1 mm-1 000 mm) platforms are sensitive to water discrimination and are capable of almost all weather viewing, which is a distinct advantage (Alain and Robert, 2005;Song et al, 2007;Anguela et al, 2010).…”

Section: Water Flux Related Ecosystem Servicesmentioning

confidence: 99%

See 1 more Smart Citation

Remote sensing of ecosystem services: An opportunity for spatially explicit assessment

Feng

Yang

et al. 2010

Chin. Geogr. Sci.

View full text Add to dashboard Cite

Ecosystem service is an emerging concept that grows to be a hot research area in ecology. Spatially explicit ecosystem service values are important for ecosystem service management. However, it is difficult to quantify ecosystem services. Remote sensing provides images covering Earth surface, which by nature are spatially explicit. Thus, remote sensing can be useful for quantitative assessment of ecosystem services. This paper reviews spatially explicit ecosystem service studies conducted in ecology and remote sensing in order to find out how remote sensing can be used for ecosystem service assessment. Several important areas considered include land cover, biodiversity, and carbon, water and soil related ecosystem services. We found that remote sensing can be used for ecosystem service assessment in three different ways: direct monitoring, indirect monitoring, and combined use with ecosystem models. Some plant and water related ecosystem services can be directly monitored by remote sensing. Most commonly, remote sensing can provide surrogate information on plant and soil characteristics in an ecosystem. For ecosystem process related ecosystem services, remote sensing can help measure spatially explicit parameters. We conclude that acquiring good in-situ measurements and selecting appropriate remote sensor data in terms of resolution are critical for accurate assessment of ecosystem services.

show abstract

Section: Water Flux Related Ecosystem Servicesmentioning

confidence: 99%

Section: Water Flux Related Ecosystem Servicesmentioning

confidence: 99%

Remote sensing of ecosystem services: An opportunity for spatially explicit assessment

Feng

Yang

et al. 2010

Chin. Geogr. Sci.

View full text Add to dashboard Cite

show abstract

“…Much research has been done on quantifying and removing these effects from the soil spectra [1,19,20,[29][30][31][32][33][34][35][36][37][38][39][40]. However, most research is focused on the effect on specific soil properties (e.g., [34,36]); focus only on specific spectroscopy techniques (e.g., [35]); or are laboratory based (e.g., [33]).…”

Section: Introductionmentioning

confidence: 99%

Creating Multi-Temporal Composites of Airborne Imaging Spectroscopy Data in Support of Digital Soil Mapping

2016

View full text Add to dashboard Cite

An increasing demand for full spatio-temporal coverage of soil information drives the growing use of soil spectroscopy. Soil spectroscopy application performed under laboratory conditions or in-field studies in semi-arid areas have shown promising results. However, when acquiring data in temperate zones, limitations by vegetation-free coverage, variation in soil moisture and management are driving coherent spatio-temporal data collection. This study explores the use of multi-temporal imaging spectroscopy data to increase the total mapping area of bare soils in a heterogeneous agricultural landscape. Spectrally and spatially high-resolution data from the Airborne Prism Experiment (APEX) were collected in September 2013, April 2014 and April 2015. Bare soils in all acquisitions were identified. To eliminate short-term differences in soil moisture and soil surface roughness, the empirical line method was used to calibrate the reflectance values of the singular images (2013 and 2015) towards the singular image with most bare soil pixels (2014). Difference indicators show that the calibration was successful (decrease in root mean square difference and angle difference, increase in R 2 and gain and offset close to one and zero). Finally, the multi-temporal composite image contained more than double the amount of bare soil pixels as compared to a singular acquisition. Summary statistics show that reflectance values of the multi-temporal composite approximate the single image data of 2014 (mean and standard deviation of 2014: 24.2 ± 8.9 vs. 24.0 ± 9.5 for the multi-temporal composite of 2013, 2014 and 2015). This indicates that global differences in soil moisture and land management have been corrected for. As a result, an improved spatial representation of soil parameters can be retrieved from the composite data. Spatial distribution of the correction factors and analysis of the spatial variability of all images, however, indicate that non-linear, short-term differences like variation in soil moisture and land management largely influence the result of the multi-temporal composite. Quantification and attribution of those factors will be required in the future to allow correcting for them.

show abstract

“…Several papers describe soil surface moisture estimation using airborne data [42][43][44][45]. Haubrock et al [46] show a clear connection between their image based data retrieved by using the Normalised Soil Moisture Index (NSMI) and in-situ measurements of top layer soil moisture. These results encourage the community in considering the future hyperspectral space missions to retrieve the SMC from space remote sensing.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Soil Moisture Retrieval from Hyperspectral VNIR-SWIR Data Using Clay Content Information: From Laboratory to Field Experiments

et al. 2015

View full text Add to dashboard Cite

Abstract:The aim of this work is to study the constraints and performance of SMC retrieval methodologies in the VNIR (Visible-Near InfraRed) and SWIR (ShortWave InfraRed) regions (from 0.4 to 2.5 µm) when passing from controlled laboratory conditions to field conditions. Five different approaches of signal processing found in literature were considered. Four local criteria are spectral indices (WISOIL, NSMI, NINSOL and NINSON). These indices are the ratios between the spectral reflectances acquired at two specific wavelengths to characterize moisture content in soil. The last criterion is based in the convex hull concept and it is a global method, which is based on the analysis of the full spectral signature of the soil. The database was composed of 464 and 9 spectra, respectively, measured over bare soils in laboratory and in-situ. For each measurement, SMC and texture were well-known and the database was divided in two parts dedicated to calibration and validation steps. The calibration part was used to define the empirical relation between SMC and SMC retrieval approaches, with coefficients of determination (R 2 ) between 0.72 and 0.92. A clay content (CC) dependence was detected for the NINSOL and NINSON indices. Consequently, two new criteria were proposed taking into account the CC contribution (NINSOLCC and NINSONCC). The well-marked regression between SMC and global/local indices, and the interest of using the CC, were confirmed during the validation step using laboratory data (R² in all cases) and using in-situ data, where WISOIL, NINSOLCC and NINSONCC criteria stand out among the NSMI and CH.

show abstract

Surface soil moisture quantification and validation based on hyperspectral data and field measurements

Cited by 47 publications

References 27 publications

Remote sensing of ecosystem services: An opportunity for spatially explicit assessment

Remote sensing of ecosystem services: An opportunity for spatially explicit assessment

Creating Multi-Temporal Composites of Airborne Imaging Spectroscopy Data in Support of Digital Soil Mapping

Improvement of Soil Moisture Retrieval from Hyperspectral VNIR-SWIR Data Using Clay Content Information: From Laboratory to Field Experiments

Contact Info

Product

Resources

About