PMID- 36894100
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230430
LR  - 20230430
IS  - 1879-1026 (Electronic)
IS  - 0048-9697 (Linking)
VI  - 876
DP  - 2023 Jun 10
TI  - Estimation of surface soil moisture by combining a structural equation model and 
      an artificial neural network (SEM-ANN).
PG  - 162558
LID - S0048-9697(23)01174-9 [pii]
LID - 10.1016/j.scitotenv.2023.162558 [doi]
AB  - Soil moisture is an important variable of the environment that directly affects 
      hydrological, ecological, and climatic processes. However, owing to the influence 
      of soil type, soil structure, topography, vegetation, and human activities, the 
      distribution of soil water content is spatially heterogeneous. It is difficult to 
      accurately monitor the distribution of soil moisture over large areas. To 
      investigate the direct or indirect influence of various factors on soil moisture 
      and obtain accurate soil moisture inversion results, we used structural equation 
      models (SEMs) to determine the structural relationships between these factors and 
      the degree of their influence on soil moisture. These models were subsequently 
      transformed into the topology of artificial neural networks (ANN). Finally, a 
      structural equation model coupled with an artificial neural network was 
      constructed (SEM-ANN) for soil moisture inversion. The results showed the 
      following: (1) The most important predictor of the spatial variability of soil 
      moisture in the April was the temperature-vegetation dryness index, while land 
      surface temperature was the most important predictor in the August; (2) After the 
      ANN model was improved, the inversion accuracy of surface soil moisture by 
      SEM-ANN model was improved, and the R(2) of verification set was increased by 
      0.01 and 0.02 in April and August, respectively, and the relative analysis error 
      was reduced by 0.5 % and 1.13 %. (3) There were no significant differences in 
      soil moisture distribution trends between the April and August.
CI  - Copyright (c) 2023 Elsevier B.V. All rights reserved.
FAU - Wang, Sinan
AU  - Wang S
AD  - Yinshanbeilu National Field Research Station of Desert Steppe Eco-hydrological 
      System, China Institute of Water Resources and Hydropower Research, Beijing 
      100038, China; College of Water Conservancy and Civil Engineering, Inner Mongolia 
      Agricultural University, Hohhot 010018, Inner Mongolia, China; Institute of Water 
      Resources for Pastoral Area Ministry of Water Resources, Hohhot 010020, China.
FAU - Li, Ruiping
AU  - Li R
AD  - College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural 
      University, Hohhot 010018, Inner Mongolia, China. Electronic address: 
      wangsn@iwhr.com.
FAU - Wu, Yingjie
AU  - Wu Y
AD  - Yinshanbeilu National Field Research Station of Desert Steppe Eco-hydrological 
      System, China Institute of Water Resources and Hydropower Research, Beijing 
      100038, China; Institute of Water Resources for Pastoral Area Ministry of Water 
      Resources, Hohhot 010020, China.
FAU - Wang, Wenjun
AU  - Wang W
AD  - Yinshanbeilu National Field Research Station of Desert Steppe Eco-hydrological 
      System, China Institute of Water Resources and Hydropower Research, Beijing 
      100038, China; Institute of Water Resources for Pastoral Area Ministry of Water 
      Resources, Hohhot 010020, China.
LA  - eng
PT  - Journal Article
DEP - 20230307
PL  - Netherlands
TA  - Sci Total Environ
JT  - The Science of the total environment
JID - 0330500
SB  - IM
OTO - NOTNLM
OT  - Environmental variables
OT  - Neural network
OT  - Remote sensing inversion
OT  - Soil moisture
OT  - Structural equation modeling
COIS- Declaration of competing interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have 
      influenced the work reported in this study.
EDAT- 2023/03/10 06:00
MHDA- 2023/03/10 06:01
CRDT- 2023/03/09 19:35
PHST- 2022/05/13 00:00 [received]
PHST- 2023/02/25 00:00 [revised]
PHST- 2023/02/26 00:00 [accepted]
PHST- 2023/03/10 06:01 [medline]
PHST- 2023/03/10 06:00 [pubmed]
PHST- 2023/03/09 19:35 [entrez]
AID - S0048-9697(23)01174-9 [pii]
AID - 10.1016/j.scitotenv.2023.162558 [doi]
PST - ppublish
SO  - Sci Total Environ. 2023 Jun 10;876:162558. doi: 10.1016/j.scitotenv.2023.162558. 
      Epub 2023 Mar 7.