Soil organic carbon and texture retrieving and mapping using proximal, airborne and Sentinel-2 spectral imaging

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12520%2F18%3A43897574" target="_blank" >RIV/60076658:12520/18:43897574 - isvavai.cz</a>

Alternative codes found

RIV/00027049:_____/18:N0000066

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0034425718304267" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0034425718304267</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.rse.2018.09.015" target="_blank" >10.1016/j.rse.2018.09.015</a>

Result language

angličtina

Original language name

Soil organic carbon and texture retrieving and mapping using proximal, airborne and Sentinel-2 spectral imaging

Original language description

Soil Organic Carbon (SOC) is a useful representative of soil fertility and an essential parameter in controlling the dynamics of various agrochemicals in soil. Soil texture is also used to calculate soil&apos;s ability to retain water for plant growth. SOC and soil texture are thus important parameters of agricultural soils and need to be regularly monitored. Optical satellite remote sensing offers the potential for frequent surveys over large areas. In addition, the recently-operated Sentinel-2 missions provide free imagery. This study compared the capabilities of Sentinel 2 for monitoring and mapping of SOC and soil texture (clay, silt and sand content) with those obtained from airborne hyperspectral (CASI/SASI sensors) and lab ASD FieldSpec spectroradiometer measurements at four agricultural sites in the Czech Republic. Combination of 10 extracted bands of the Sentinel-2 and 18 spectral indices, as independent variables, were used to train prediction models and then produce spatial distribution maps of the selected attributes. Results showed that the prediction accuracy based on lab spectroscopy, airborne and Sentinel-2 in the majority of the sites was adequate for SOC and fair for clay; however, Sentinel-2 imagery could not be used to detect and map variations in silt and sand. The SOC and clay maps derived from the airborne and spaceborne datasets showed similar trend, with both performing better where SOC levels were relatively high, though at the highest levels Sentinel-2 was able to create the SOC map more precisely than the airborne sensors. Taken across all SOC levels measured in the reference data, Sentinel-2 results were marginally lower than lab spectroscopy and airborne imagery, but this reduction in precision may be offset by the extensive geographical coverage and more frequent revisit characteristic of satellite observation. The increased temporal revisit and area are expected to be positive enhancements to the acquisition of high-quality information on variations in SOC and clay content of bare soils.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20705 - Remote sensing

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2018

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Remote Sensing of Environment

ISSN

0034-4257

e-ISSN

—

Volume of the periodical

218

Issue of the periodical within the volume

12/2018

Country of publishing house

US - UNITED STATES

Number of pages

15

Pages from-to

89-103

UT code for WoS article

000449449800007

EID of the result in the Scopus database

2-s2.0-85053836935