PMID- 35062514
OWN - NLM
STAT- MEDLINE
DCOM- 20220125
LR  - 20220128
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 22
IP  - 2
DP  - 2022 Jan 11
TI  - Precise Monitoring of Soil Salinity in China's Yellow River Delta Using UAV-Borne 
      Multispectral Imagery and a Soil Salinity Retrieval Index.
LID - 10.3390/s22020546 [doi]
LID - 546
AB  - Monitoring salinity information of salinized soil efficiently and precisely using 
      the unmanned aerial vehicle (UAV) is critical for the rational use and 
      sustainable development of arable land resources. The sensitive parameter and a 
      precise retrieval method of soil salinity, however, remain unknown. This study 
      strived to explore the sensitive parameter and construct an optimal method for 
      retrieving soil salinity. The UAV-borne multispectral image in China's Yellow 
      River Delta was acquired to extract band reflectance, compute vegetation indexes 
      and soil salinity indexes. Soil samples collected from 120 different study sites 
      were used for laboratory salt content measurements. Grey correlation analysis and 
      Pearson correlation coefficient methods were employed to screen sensitive band 
      reflectance and indexes. A new soil salinity retrieval index (SSRI) was then 
      proposed based on the screened sensitive reflectance. The Partial Least Squares 
      Regression (PLSR), Multivariable Linear Regression (MLR), Back Propagation Neural 
      Network (BPNN), Support Vector Machine (SVM), and Random Forest (RF) methods were 
      employed to construct retrieval models based on the sensitive indexes. The 
      results found that green, red, and near-infrared (NIR) bands were sensitive to 
      soil salinity, which can be used to build SSRI. The SSRI-based RF method was the 
      optimal method for accurately retrieving the soil salinity. Its modeling 
      determination coefficient (R(2)) and Root Mean Square Error (RMSE) were 0.724 and 
      1.764, respectively; and the validation R(2), RMSE, and Residual Predictive 
      Deviation (RPD) were 0.745, 1.879, and 2.211.
FAU - Yu, Xinyang
AU  - Yu X
AD  - College of Resources and Environment, Shandong Agricultural University, Tai'an 
      271018, China.
AD  - Tropical Research and Education Center/Department of Agricultural and Biological 
      Engineering, Institute of Food and Agricultural Sciences, University of Florida, 
      Homestead, FL 33031, USA.
FAU - Chang, Chunyan
AU  - Chang C
AD  - College of Resources and Environment, Shandong Agricultural University, Tai'an 
      271018, China.
FAU - Song, Jiaxuan
AU  - Song J
AD  - College of Resources and Environment, Shandong Agricultural University, Tai'an 
      271018, China.
FAU - Zhuge, Yuping
AU  - Zhuge Y
AUID- ORCID: 0000-0002-8298-196X
AD  - College of Resources and Environment, Shandong Agricultural University, Tai'an 
      271018, China.
FAU - Wang, Ailing
AU  - Wang A
AD  - College of Resources and Environment, Shandong Agricultural University, Tai'an 
      271018, China.
LA  - eng
GR  - ZR2019MD014/Natural Foundation of Shandong Province/
GR  - 2019JZZY010723/Major Science and Technology Projects in Shandong Province/
PT  - Journal Article
DEP - 20220111
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
RN  - 0 (Soil)
SB  - IM
MH  - China
MH  - Rivers
MH  - *Salinity
MH  - *Soil
MH  - Unmanned Aerial Devices
PMC - PMC8778686
OTO - NOTNLM
OT  - optimal retrieval model
OT  - random forest
OT  - remote sensing
OT  - soil salinity sensitive parameter
OT  - support vector machine
COIS- The authors declare no conflict of interest.
EDAT- 2022/01/23 06:00
MHDA- 2022/01/27 06:00
PMCR- 2022/01/11
CRDT- 2022/01/22 01:02
PHST- 2021/11/06 00:00 [received]
PHST- 2022/01/03 00:00 [revised]
PHST- 2022/01/08 00:00 [accepted]
PHST- 2022/01/22 01:02 [entrez]
PHST- 2022/01/23 06:00 [pubmed]
PHST- 2022/01/27 06:00 [medline]
PHST- 2022/01/11 00:00 [pmc-release]
AID - s22020546 [pii]
AID - sensors-22-00546 [pii]
AID - 10.3390/s22020546 [doi]
PST - epublish
SO  - Sensors (Basel). 2022 Jan 11;22(2):546. doi: 10.3390/s22020546.