PMID- 36435496
OWN - NLM
STAT- MEDLINE
DCOM- 20230110
LR  - 20230207
IS  - 1096-0953 (Electronic)
IS  - 0013-9351 (Linking)
VI  - 217
DP  - 2023 Jan 15
TI  - Estimating soil salinity using Gaofen-2 imagery: A novel application of combined 
      spectral and textural features.
PG  - 114870
LID - S0013-9351(22)02197-1 [pii]
LID - 10.1016/j.envres.2022.114870 [doi]
AB  - Gaofen-2 (GF-2) imagery data has been playing an important role in environmental 
      monitoring. However, the scarcity of spectral bands makes GF-2 difficult to use 
      in soil salinity estimation. In this paper, we combined spectral and textual 
      features for soil salinity estimation from GF-2 imagery. The spectral features 
      comprised five classes of predictors: spectral value, vegetation index, salinity 
      index, brightness index, and intensity index. Four gray-level co-occurrence 
      matrix (GLCM) indices were used as the textural features. The least absolute 
      shrinkage and selection operator (LASSO) was applied to select features. Four 
      methods, namely, Random forest (RF), support vector machine (SVM), back 
      propagation neural network (BPNN), and partial least squares regression (PLSR) 
      were applied and compared. To this end, 211 soil samples were collected in the 
      Yellow River Delta through field investigation. The results showed that GF-2 
      imagery could successfully estimate soil salinity by integrating spectral and 
      texture features, and among the four methods, the RF had the highest accuracy 
      with the determination coefficient for cross-validation (R(2)(CV)), a root mean 
      square error for cross-validation (RMSE(CV)), and the ratio of the standard 
      deviation to the root mean square error of prediction (RPD) of 0.82, 
      2.36 g kg(-1), and 2.28, respectively. Especially, the impact of different scale 
      features on the soil salinity estimation accuracy was evaluated. The optimal 
      window size for features was 9 x 9 pixels, and increasing or decreasing the 
      window size will decrease the estimation accuracy. The study provides a novel 
      application to soil salinity estimation from remote sensing imagery.
CI  - Copyright (c) 2022. Published by Elsevier Inc.
FAU - Yang, Han
AU  - Yang H
AD  - College of Geography and Environment, Shandong Normal University, Ji'nan, 250014, 
      China.
FAU - Wang, Zhaohai
AU  - Wang Z
AD  - College of Geography and Environment, Shandong Normal University, Ji'nan, 250014, 
      China. Electronic address: wzhsdnu@163.com.
FAU - Cao, Jianfei
AU  - Cao J
AD  - College of Geography and Environment, Shandong Normal University, Ji'nan, 250014, 
      China; Shandong Dongying Institute of Geographic Sciences, Dongying, 257000, 
      China. Electronic address: cjfsdnu@163.com.
FAU - Wu, Quanyuan
AU  - Wu Q
AD  - College of Geography and Environment, Shandong Normal University, Ji'nan, 250014, 
      China.
FAU - Zhang, Baolei
AU  - Zhang B
AD  - College of Geography and Environment, Shandong Normal University, Ji'nan, 250014, 
      China.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20221123
PL  - Netherlands
TA  - Environ Res
JT  - Environmental research
JID - 0147621
RN  - 0 (Soil)
SB  - IM
MH  - *Soil
MH  - *Salinity
MH  - Least-Squares Analysis
MH  - Environmental Monitoring/methods
MH  - Support Vector Machine
OTO - NOTNLM
OT  - Gaofen-2 imagery
OT  - Gray-level co-occurrence matrix (GLCM)
OT  - Soil salinity
OT  - Spectral features
OT  - Textural features
COIS- Declaration of competing interest The authors declare the following financial 
      interests/personal relationships which may be considered as potential competing 
      interests: Wu Quanyuan reports financial support was provided by Shandong 
      Provincial Department of Education. Cao Jianfei reports financial support was 
      provided by Zhongke Shandong Dongying Institute of Geographic Sciences.
EDAT- 2022/11/27 06:00
MHDA- 2023/01/11 06:00
CRDT- 2022/11/26 19:33
PHST- 2021/11/16 00:00 [received]
PHST- 2022/11/11 00:00 [revised]
PHST- 2022/11/19 00:00 [accepted]
PHST- 2022/11/27 06:00 [pubmed]
PHST- 2023/01/11 06:00 [medline]
PHST- 2022/11/26 19:33 [entrez]
AID - S0013-9351(22)02197-1 [pii]
AID - 10.1016/j.envres.2022.114870 [doi]
PST - ppublish
SO  - Environ Res. 2023 Jan 15;217:114870. doi: 10.1016/j.envres.2022.114870. Epub 2022 
      Nov 23.