The soil line concept in remote sensing

Baret, Frédéric; Jacquemoud, Stéphane; Hanocq, J.-F.

doi:10.1080/02757259309532166

Cited by 173 publications

(100 citation statements)

References 10 publications

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“…Vermote and Saleous [38] used a linear relationship between the retrieved surface reflectances for a site in the Sahara Desert for converting reflectances across MODIS onboard Terra and the Advanced Very High Resolution Radiometer (AVHRR) onboard NOAA 16 in cross-calibrating the AVHRR using MODIS. In the present study, the soil line [39] refers to the linear relationship between soil reflectances of any bands in the solar-reflective range, and we used the soil line obtained from ground-measured data for translating ASTER VNIR bands.…”

Section: Band Translationmentioning

confidence: 99%

Inter-Band Radiometric Comparison and Calibration of ASTER Visible and Near-Infrared Bands

2015

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The present study evaluates inter-band radiometric consistency across the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) visible and near-infrared (VNIR) bands and develops an inter-band calibration algorithm to improve radiometric consistency. Inter-band radiometric comparison of current ASTER data shows a root mean square error (RMSE) of 3.8%-5.7% among radiance outputs of spectral bands due primarily to differences between calibration strategies of the NIR band for nadir-looking (Band 3N) and the other two bands (green and red bands, corresponding to Bands 1 and 2). An algorithm for radiometric calibration of Bands 2 and 3N with reference to Band 1 is developed based on the band translation technique and is used to obtain new radiometric calibration coefficients (RCCs) for sensor sensitivity degradation. The systematic errors between radiance outputs are decreased by applying the derived RCCs, which result in reducing the RMSE from 3.8%-5.7% to 2.2%-2.9%. The remaining errors are approximately equal to or smaller than the intrinsic uncertainties of inter-band calibration derived by sensitivity analysis. Improvement of the radiometric consistency would increase the accuracy of band algebra (e.g., vegetation indices) and its application. The algorithm can be used to evaluate inter-band radiometric consistency, as well as for the calibration of other sensors.

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Section: Band Translationmentioning

confidence: 99%

Inter-Band Radiometric Comparison and Calibration of ASTER Visible and Near-Infrared Bands

2015

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“…The value of M changes according to various soil types; in this paper, it was set to 1.16 based on previous studies [34,35,[45][46][47][48][49]. The extraction of Pixels B and C occurs via the following simplified steps: (1) exclude the water bodies and fully cloud-covered pixels in the study area; (2) make sure the remaining pixels are successfully radiometrically corrected; (3) traverse the remaining pixels, compute their distances to L0 (Figure 3b) and extract the reflectance of each pixel with the minimum and maximum distance; and (4) terminate if the ratio of R N IR relating to R red is less than the threshold value (set to 2 based on the maximum slope of soil line in NIR-red triangle space); otherwise, repeat Step (3).…”

Section: Nsmi: Normalized Soil Moisture Indexmentioning

confidence: 99%

NIR-Red Spectra-Based Disaggregation of SMAP Soil Moisture to 250 m Resolution Based on OzNet in Southeastern Australia

Chen

Zhang

2017

Remote Sensing

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Abstract:To meet the demand of regional hydrological and agricultural applications, a new method named near infrared-red (NIR-red) spectra-based disaggregation (NRSD) was proposed to perform a disaggregation of Soil Moisture Active Passive (SMAP) products from 36 km to 250 m resolution. The NRSD combined proposed normalized soil moisture index (NSMI) with SMAP data to obtain 250 m resolution soil moisture mapping. This NRSD method was validated with the data from in situ OzNet network in May and September 2015. Results showed that NRSD performed a decent downscaling (root-mean-square error (RMSE) = 0.04 m 3 /m 3 and 0.12 m 3 /m 3 in May and September, respectively). Based on the validation, it was found that the proposed NSMI was a new alternative indicator for denoting the heterogeneity of soil moisture at sub-kilometer scales. Attributed to the excellent performance of the NSMI, NRSD has a higher overall accuracy, finer spatial representation within SMAP pixels and wider applicable scope on usability tests for land cover, vegetation density and drought condition than the disaggregation based on physical and theoretical scale change (DISPATCH) has at 250 m resolution. This revealed that the NRSD method is expected to provide soil moisture mapping at 250-resolution for large-scale hydrological and agricultural studies.

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“…Baret et al (1993) and Rondeaux et al (1996) speculate that the lack of an accepted global soil line and difficulties in identifying the soil line in a satellite image with varying densities of vegetation have preserved the use of classical vegetation indices, despite their shortcomings.…”

Section: Defining Vegetation Density and The Soil Linementioning

confidence: 99%

Mapping global tropical wetlands from earth observing satellite imagery

Gumbricht¹

2012

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The soil line concept in remote sensing

Cited by 173 publications

References 10 publications

Inter-Band Radiometric Comparison and Calibration of ASTER Visible and Near-Infrared Bands

Inter-Band Radiometric Comparison and Calibration of ASTER Visible and Near-Infrared Bands

NIR-Red Spectra-Based Disaggregation of SMAP Soil Moisture to 250 m Resolution Based on OzNet in Southeastern Australia

Mapping global tropical wetlands from earth observing satellite imagery

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