| Title: |
Adapting Prediction Models to Bare Soil Fractional Cover for Extending Topsoil Clay Content Mapping Based on AVIRIS-NG Hyperspectral Data |
| Authors: |
George, Elizabeth, Baby; Gomez, Cécile; Kumar, Nagesh, D |
| Contributors: |
Indian Institute of Science Bangalore (IISc Bangalore); Laboratoire d'étude des Interactions Sol - Agrosystème - Hydrosystème (UMR LISAH); Institut de Recherche pour le Développement (IRD)-AgroParisTech-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Montpellier; Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro); Indo-French Cell for Water Sciences = Cellule Franco Indienne de Recherche en Science de l’Eau (IFCWS = CEFIRSE); Indian Institute of Science (IISc); The authors are indebted to ICAR-NBSS & LUP Bangalore, India, for soil sample collection and laboratory analysis. This research was supported by the project ATCHA ANR-16-CE030006 and the Programme National de Teledetection Spatiale (PNTS, http://www.insu.cnrs.fr/pnts, accessed on 1 April 2020), grant no. PNTS-2019-5. The first author wishes to acknowledge the Grantham Fellowship provided by the Divecha Centre for Climate Change, IISc, Bangalore, to conduct this research. The Kabini Critical Zone Observatory (AMBHAS, BVET, Sekhar 36; Tomer 35; www.ambhas.com; https://mtropics.obsmip.fr/, accessed on 1 September 2021), which is part of the OZCAR network (Gaillardet 2018; http://www.ozcar-ri.org/ozcar/, accessed on 1 September 2021), is also acknowledged. |
| Source: |
ISSN: 2072-4292 ; Remote Sensing ; https://hal.inrae.fr/hal-04541758 ; Remote Sensing, 2024, 16 (6), pp.1066. ⟨10.3390/rs16061066⟩. |
| Publisher Information: |
CCSD; MDPI |
| Publication Year: |
2024 |
| Subject Terms: |
composite map clay content digital soil mapping uncertainty regression model; regression model; uncertainty; digital soil mapping; clay content; composite map; [SDV]Life Sciences [q-bio] |
| Description: |
International audience ; The deployment of remote sensing platforms has facilitated the mapping of soil properties to a great extent. However, the accuracy of these soil property estimates is compromised by the presence of non-soil cover, which introduces interference with the acquired reflectance spectra over pixels. Therefore, current soil property estimation by remote sensing is limited to bare soil pixels, which are identified based on spectral indices of vegetation. Our study proposes a composite mapping approach to extend the soil properties mapping beyond bare soil pixels, associated with an uncertainty map. The proposed approach first classified the pixels based on their bare soil fractional cover by spectral unmixing. Then, a specific regression model was built and applied to each bare soil fractional cover class to estimate clay content. Finally, the clay content maps created for each bare soil fractional cover class were mosaicked to create a composite map of clay content estimations. A bootstrap procedure was used to estimate the standard deviation of clay content predictions per bare soil fractional cover dataset, which represented the uncertainty of estimations. This study used a hyperspectral image acquired by the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor over cultivated fields in South India. The proposed approach provided modest performances in prediction (Rval2 ranging from 0.53 to 0.63) depending on the bare soil fractional cover class and showed a correct spatial pattern, regardless of the bare soil fraction classes. The model’s performance was observed to increase with the adoption of higher bare soil fractional cover thresholds. The mapped area ranged from 10.4% for pixels with bare soil fractional cover >0.7 to 52.7% for pixels with bare soil fractional cover >0.3. The approach thus extended the mapped surface by 42.4%, while maintaining acceptable prediction performances. Finally, the proposed approach could be adopted to extend the mapping ... |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| Relation: |
WOS: 001193543300001 |
| DOI: |
10.3390/rs16061066 |
| Availability: |
https://hal.inrae.fr/hal-04541758; https://hal.inrae.fr/hal-04541758v1/document; https://hal.inrae.fr/hal-04541758v1/file/remotesensing-16-01066-v2.pdf; https://doi.org/10.3390/rs16061066 |
| Rights: |
http://creativecommons.org/licenses/by/ ; info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.9F97FF4B |
| Database: |
BASE |