PMID- 35132506
OWN - NLM
STAT- MEDLINE
DCOM- 20220209
LR  - 20220209
IS  - 1573-2959 (Electronic)
IS  - 0167-6369 (Linking)
VI  - 194
IP  - 3
DP  - 2022 Feb 7
TI  - Spatial modeling of soil organic carbon using remotely sensed indices and 
      environmental field inventory variables.
PG  - 152
LID - 10.1007/s10661-022-09842-8 [doi]
AB  - The relationship between soil organic carbon (SOC) and environmental parameters 
      was investigated in the Galazchai Watershed, Iran. Therefore, correlating the SOC 
      amounts with remote sensing (RS) indices, topographic variables, and soil texture 
      was analyzed. Some 125 soil samples gather from the upper 30 cm, and the weight 
      of each sample was about 0.5 kg. The RS indices, consisting of difference 
      vegetation index (DVI), enhanced vegetation index (EVI), optimized soil adjusted 
      vegetation index (OSAVI), normalized difference vegetation index (NDVI), and soil 
      adjusted vegetation index (SAVI), were used. Topographic variables included 
      slope, elevation, aspect, and topographical wetness index (TWI), as well as clay 
      and silt contents. The ordinary least square (OLS) and the geographically 
      weighted regression (GWR) were employed to develop the SOC relationship 
      considering different combinations of the variables. Results showed that none of 
      the combinations of variables accurately estimated SOC (R(2) < 0.32 and p 
      value > 0.001). However, EVI with GWR (R(2) = 0.291) and OSAVI, clay, slope, and 
      aspect with GWR (R(2)= 0.32) better estimated SOC. Therefore, results showed that 
      the study remotely sensed indices and environmental field inventory variables 
      could not favorably predict the SOC content. These results can be attributed to 
      the low SOC values varying from 0.917 to 3.355%, with a mean of 2.194 +/- 0.522 in 
      the study watershed. However, studies using more uniformly distributed and denser 
      sampling in the study area and other methods to investigate the relationship 
      between variables are recommended.
CI  - (c) 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.
FAU - Katebikord, Azadeh
AU  - Katebikord A
AD  - Department of Watershed Management Engineering, Faculty of Natural Resources, 
      Tarbiat Modares University, 46417-76489, Noor, Iran.
FAU - Sadeghi, Seyed Hamidreza
AU  - Sadeghi SH
AUID- ORCID: 0000-0002-5419-8062
AD  - Department of Watershed Management Engineering, Faculty of Natural Resources, 
      Tarbiat Modares University, 46417-76489, Noor, Iran. sadeghi@modares.ac.ir.
FAU - Singh, Vijay P
AU  - Singh VP
AD  - Department of Watershed Management Engineering, Faculty of Natural Resources, 
      Tarbiat Modares University, 46417-76489, Noor, Iran.
AD  - Department of Biological and Agricultural Engineering and Zachry Department of 
      Civil Engineering, Texas A & M University, College Station, TX, 77843-2117, USA.
LA  - eng
GR  - IG-39713/Tarbiat Modares University/
PT  - Journal Article
DEP - 20220207
PL  - Netherlands
TA  - Environ Monit Assess
JT  - Environmental monitoring and assessment
JID - 8508350
RN  - 0 (Soil)
RN  - 7440-44-0 (Carbon)
SB  - IM
MH  - *Carbon/analysis
MH  - Environmental Monitoring
MH  - Least-Squares Analysis
MH  - *Soil
MH  - Spatial Regression
OTO - NOTNLM
OT  - Carbon spatial distribution
OT  - Digital soil mapping
OT  - Geospatial modeling
OT  - Landsat images
OT  - Remote sensing predictors
OT  - Soil organic carbon prediction
EDAT- 2022/02/09 06:00
MHDA- 2022/02/10 06:00
CRDT- 2022/02/08 05:52
PHST- 2021/05/04 00:00 [received]
PHST- 2022/01/29 00:00 [accepted]
PHST- 2022/02/08 05:52 [entrez]
PHST- 2022/02/09 06:00 [pubmed]
PHST- 2022/02/10 06:00 [medline]
AID - 10.1007/s10661-022-09842-8 [pii]
AID - 10.1007/s10661-022-09842-8 [doi]
PST - epublish
SO  - Environ Monit Assess. 2022 Feb 7;194(3):152. doi: 10.1007/s10661-022-09842-8.