Spectroscopic measurements and imaging of soil colour for field scale estimation of soil organic carbon

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12520%2F20%3A43900785" target="_blank" >RIV/60076658:12520/20:43900785 - isvavai.cz</a>

Alternative codes found

RIV/60460709:41210/20:79922

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0016706119310493" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0016706119310493</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.geoderma.2019.113972" target="_blank" >10.1016/j.geoderma.2019.113972</a>

Result language

angličtina

Original language name

Spectroscopic measurements and imaging of soil colour for field scale estimation of soil organic carbon

Original language description

Effective measurement and management of soil organic carbon (SOC) are essential for ecosystem function and food production. SOC has an important influence on soil properties and soil quality. Conventional SOC analysis is expensive and time-consuming. The development of spectral imaging sensors enables the acquisition of larger amounts of data using cheaper and faster methods. In addition, satellite remote sensing offers the potential to perform surveys more frequently and over larger areas. This research aimed to measure SOC content with colour as an indirect proxy. The measurements of soil colour were made at an agricultural site of the Czech Republic with an inexpensive digital camera and the Sentinel-2 remote sensor. Various soil colour spaces and colour indices derived from the (i) reflectance spectroscopy in the selected wavelengths of the visible (VIS) range (400-700 nm), (ii) RGB digital camera, and (iii) Sentinel-2 visible bands were used to train models for prediction of SOC. For modelling, we used the machine learning method, random forest (RF), and the models were validated with repeated 5-fold cross-validation. For prediction of SOC, the digital camera produced R-2 = 0.85 and FtMSEp = 0.11%, which had higher R-2 and similar RMSEp compared to those obtained from the spectroscopy (R-2 = 0.78 and RMSEp = 0.09%). Sentinel-2 predicted SOC with lower accuracy than other techniques; however, the results were still fair (R-2 = 0.67 and RMSEp = 0.12%) and comparable with other methods. Using a digital camera with simple colour features was efficient. It enabled cheaper and accurate predictions of SOC compared to spectroscopic measurement and Sentinel-2 data.

Czech name

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Czech description

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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

<a href="/en/project/GJ18-28126Y" target="_blank" >GJ18-28126Y: Soil contamination assessment using hyperspectral orbital data</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2020

Confidentiality

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

Name of the periodical

Geoderma

ISSN

0016-7061

e-ISSN

—

Volume of the periodical

357

Issue of the periodical within the volume

neuveden

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

10

Pages from-to

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UT code for WoS article

000496837300024

EID of the result in the Scopus database

2-s2.0-85072164813