Verifying the predictive performance for soil organic carbon when employing field Vis-NIR spectroscopy and satellite imagery obtained using two different sampling methods

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00027073%3A_____%2F22%3AN0000113" target="_blank" >RIV/00027073:_____/22:N0000113 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/60460709:41210/22:89974

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/abs/pii/S0168169922001132" target="_blank" >https://www.sciencedirect.com/science/article/abs/pii/S0168169922001132</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.compag.2022.106796" target="_blank" >10.1016/j.compag.2022.106796</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Verifying the predictive performance for soil organic carbon when employing field Vis-NIR spectroscopy and satellite imagery obtained using two different sampling methods

Popis výsledku v původním jazyce

In soil research, the most employed sampling design techniques can be categorized as random sampling (stratified or simple random (SR)) or systematic techniques (transects or grid). Many other sampling approaches have also been developed by researchers based on these sampling principles. The purpose of this study is to compare the differences in SOC prediction when using field spectra (FS) and Sentinel-2 (S2) data collected separately through SR and grid design (GD) on the same agricultural field. Additionally, the impact of spectral indices on S2 data in a merged data approach under the two-sampling strategies will also be tested. The data for each sampling method were obtained based on a previous study in which 130 soil samples were collected from a full grid design (with 40 m spacing) covering the entire area. Although the full GD method was used for this current study, the distance between the samples was increased (80 m apart). The schemes were therefore structured for the collection of 65 samples in the field for each sampling technique. However, 63 samples were collected with the GD because two of the sampling points fell on rocky areas and were eliminated accordingly. For SR sampling, the study field was not stratified, and no requirements were used for minimum sample spacing. Sixty-five samples and spectral data were collected at various locations. To achieve the mentioned objective, this study builds a fivefold cross-validation model based on support vector machines (SVMs). Different pretreatment combinations were also implemented. The results showed that the GD was better than the SR approach using the merged dataset (R-CV(2) = 0.45, RMSECV = 0.26, RPD = 1.41, bias =-0.0073); however, SOC prediction under SR sampling using FS yielded the highest accuracy and lowest error margin (R-CV(2) = 0.60, RMSECV = 0.21, RPD = 1.66, and bias = 0.0045). Despite the above-mentioned disparity between the single and merged data, this study shows that using different sampling design methods on the same field separately is a very promising approach for SOC estimation, particularly in fields with low SOC. Based on these results, the robustness of this approach should be investigated next in future studies using larger sample sizes as well as other modeling techniques.

Název v anglickém jazyce

Verifying the predictive performance for soil organic carbon when employing field Vis-NIR spectroscopy and satellite imagery obtained using two different sampling methods

Popis výsledku anglicky

In soil research, the most employed sampling design techniques can be categorized as random sampling (stratified or simple random (SR)) or systematic techniques (transects or grid). Many other sampling approaches have also been developed by researchers based on these sampling principles. The purpose of this study is to compare the differences in SOC prediction when using field spectra (FS) and Sentinel-2 (S2) data collected separately through SR and grid design (GD) on the same agricultural field. Additionally, the impact of spectral indices on S2 data in a merged data approach under the two-sampling strategies will also be tested. The data for each sampling method were obtained based on a previous study in which 130 soil samples were collected from a full grid design (with 40 m spacing) covering the entire area. Although the full GD method was used for this current study, the distance between the samples was increased (80 m apart). The schemes were therefore structured for the collection of 65 samples in the field for each sampling technique. However, 63 samples were collected with the GD because two of the sampling points fell on rocky areas and were eliminated accordingly. For SR sampling, the study field was not stratified, and no requirements were used for minimum sample spacing. Sixty-five samples and spectral data were collected at various locations. To achieve the mentioned objective, this study builds a fivefold cross-validation model based on support vector machines (SVMs). Different pretreatment combinations were also implemented. The results showed that the GD was better than the SR approach using the merged dataset (R-CV(2) = 0.45, RMSECV = 0.26, RPD = 1.41, bias =-0.0073); however, SOC prediction under SR sampling using FS yielded the highest accuracy and lowest error margin (R-CV(2) = 0.60, RMSECV = 0.21, RPD = 1.66, and bias = 0.0045). Despite the above-mentioned disparity between the single and merged data, this study shows that using different sampling design methods on the same field separately is a very promising approach for SOC estimation, particularly in fields with low SOC. Based on these results, the robustness of this approach should be investigated next in future studies using larger sample sizes as well as other modeling techniques.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

40104 - Soil science

Projekt

<a href="/cs/project/SS02030018" target="_blank" >SS02030018: Centrum pro krajinu a biodiverzitu</a><br>

Návaznosti

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

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

Computers and Electronics in Agriculture

ISSN

0168-1699

e-ISSN

1872-7107

Svazek periodika

194

Číslo periodika v rámci svazku

March 2022

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

10

Strana od-do

106796

Kód UT WoS článku

000784219300003

EID výsledku v databázi Scopus

2-s2.0-85124897572