Conclusions
In this paper, we selected sensitive bands, the optimum
spectral index (
SAVI
) and
ECa
as the parameters of
PLSR
models
to monitor soil salinity. Our major conclusions include the
following:
1. Based on the soil-adjusted parameter (
L
= 100), the
SAVI
index shows the best correlation with measured soil
conductivity (
ECa
).
ECa
is also significantly related to the
bands [(Red Edge) Band6, (Near-IR1) Band7 and (Near-IR2)
Band8].
ECa
and visible bands (Blue band, Red band, and
Green band) revealed the lowest correlation.
2. With different combinations of variables (
SAVI
Index, (Red
Edge) B6, (Near-IR1) B7 and (Near-IR2) B8), the accuracy
of
PLSR
models has been significantly enhanced, as is
shown by an increase in the determination coefficient of
PLSR
model from 0.25 to 0.67. Predictive model calibra-
tions indicated that the conceptual model accuracy was
more acceptable with
R
2
= 0.67 and a root mean square
error (
RMSE
) of 1.19 dS·m
-1
.
Although this study shows promising results for assessing
soil salinization in study area with optimum index and sensi-
tive bands that were extracted from Worldview-2 high-spatial
resolution images, additional research is needed. For in-
stance, narrow-band indices derived from hyper-spectral im-
ages should be investigated because they may yield a greater
degree of accuracy. Thus, this study can be extended in the
future by developing predictive models for soil salinization
based on remote sensing indicators and
PLSR
techniques.
Acknowledgments
This work was jointly supported by multiple grants from the
National Natural Science Foundation of China (No. 41561089;
No. U1138303; NO. 41361016), College of Resources and
Environment Science and the key lab of Oasis Ecology at Xin-
jiang University. The authors thank classmates who assisted
long and strenuous hours to collect field data and authors
also thank the editor and the anonymous reviewers for their
review and constructive comments.
References
Allbed, A., L. Kumar, and P. Sinha, 2014. Mapping and modelling
spatial variation in soil salinity in the Al Hassa oasis based on
remote sensing indicators and regression techniques,
Remote
Sensing
, 6(1):1137–1157.
Abliz, A., T. Tiyip, A. Ghulam, H.
Ümüt
, J.L. Ding, and M. Sawut,
2016. Effects of shallow groundwater table and salinity on
soil salt dynamics in the Keriya oasis, northwestern China,
Environmental Earth Sciences
, 75(3):260.
Aldabaa, A. A. A., D.C. Weindorf, S. Chakraborty, A. Sharma, and B.
Li, 2015. Combination of proximal and remote sensing methods
for rapid soil salinity quantification,
Geoderma
, (239-240):34–46.
Allbed, A., L. Kumar, and P. Sinha, 2014. Mapping and modelling
spatial variation in soil salinity in the Al Hassa oasis based on
remote sensing indicators and regression techniques,
Remote
Sensing
, 6(1):1137–1157.
Allbed, A., L. Kumar, and Y.Y. Aldakheel, 2014. Assessing soil
salinity using soil salinity and vegetation indices derived from
Ikonos high-spatial resolution imageries: Applications in a date
palm dominated region,
Geoderma
, 230–231(7):1–8.
Aly, Z., F.J. Bonn, and R. Magagi, 2007. Analysis of the backscattering
coefficient of salt-affected soils using modeling and Radarsat-1
SAR data,
IEEE Transactions on Geoscience and Remote
Sensing
, 45(2):332–341.
Amezketa, E. 2006. An integrated methodology for assessing soil
salinization, a pre-condition for land desertification,
Journal of
Arid Environments
, 67(4):594–606.
Benedetto, D.D., A. Castrignanò, M. Rinaldi, S. Ruggieri, F. Santoro,
and B. Figorito, 2013. An approach for delineating homogeneous
zones by using multi-sensor data,
Geoderma
, 199:117–127.
Bilgili, A.V. 2013. Spatial assessment of soil salinity in the Harran
Plain using multiple kriging techniques,
Environmental
Monitoring & Assessment
, 185(1):777–795.
Billings, S., J. Keranen, G. Schultz, and C. Bassani, C. 2012. Large
loop EMI sensor for detection of deeply buried munitions in
magnetic soils,
Proceedings of SPIE
,-The
International Society for Optical Engineering, 835707-835707-12.
Bouaziz, M., J. Matschullat, and R. Gloaguen, 2011. Improved remote
sensing detection of soil salinity from a semi-arid climate in
northeast Brazil,
Comptes Rendus Geosciences
, 343(11):795–803.
Brevik, E.C., and T. Fenton. 2003. Use of the Geonics EM-38 to
delineate soils in a loess over. till landscape, Southwestern Iowa,
Soil Survey Horizons
, 44(1):16-23.
Brevik, E.C., J. Heilig, J. Kempenich, J. Doolittle, and M. Ulmer, 2012.
Evaluation of electromagnetic induction to characterize and map
sodium-affected soils in the northern great plains of the United
States,
Proceedings of the European Geosciences Union General
Assembly
, Vol.14, pp.9.
Cao, L., J. Ding, and H. Yu, 2016. Relationship between multi-scale
landscape pattern and salinity in Weigan and Kuqa rivers
delta oasis,
Transactions of the Chinese Society of Agricultural
Engineering
.
Corwin, D.L., S.M. Lesch, J.D. Oster, and S.R. Kaffka, 2008. Short-term
sustainability of drainage water reuse: Spatio-temporal impacts
on soil chemical properties,
Journal of Environmental Quality
,
37(5 Supplment):S8–S24.
Daniels, D.J., D.J. Gunton, H.F. Scott, and J. Tao, 1989. Electrical
conductivity of soils and rocks,
Modern Radar
, 06:17-33–61.
Dehaan, R., and G.R. Taylor. 2003. Image-derived spectral
endmembers as indicators of salinization,
International Journal
of Remote Sensing
, 24(4):775–794.
Ding, J., and D. Yu, 2014. Monitoring and evaluating spatial
variability of soil salinity in dry and wet seasons in the Werigan–
Kuqa Oasis, China, using remote sensing and electromagnetic
induction instruments,
Geoderma
, 235-236(4):316–322.
Doolittle, J.A., and E.C. Brevik, 2014. The use of electromagnetic
induction techniques in soils studies,
Geoderma
, 223:33–45.
Douaoui, A.E.K., H. Nicolas, and C. Walter, 2006. Detecting salinity
hazards within a semiarid context by means of combining soil
and remote-sensing data,
Geoderma
, 134(1):217–230.
Ekercin, S., and C. Ormeci, 2008. estimating soil salinity using
satellite remote sensing data and real-time field sampling,
Environmental Engineering Science
, 25(7):981–988.
Eldeiry, A.A., and L.A. Garcia, 2008. Detecting soil salinity in alfalfa
fields using spatial modeling and remote sensing,
Soil Science
Society of America Journal
, 72(1):201–211.
FAO, 2010.
Land Resource Potential and Constraints Statistic sat
Country and Regional Level
, Food and Agriculture Organization,
URL:
(last date accessed: 04
December 2017).
Fidahussein, M, J. Friedlin, and S. Grannis S. 2004. Comparison
of land-cover classification methods in the Brazilian Amazon
Basin[J],
Photogrammetric Engineering & Remote Sensing
,
70(6):723–732.
Fan, X., B. Pedroli, G. Liu, Q. Liu, H. Liu, and L. Shu, 2012. Soil
salinity development in the Yellow River delta in relation to
groundwater dynamics,
Land Degradation & Development
,
23(2):175–189.
Farifteh, J., F.V.D. Meer, C. Atzberger, and E.J.M. Carranza, 2007.
Quantitative analysis of salt-affected soil reflectance spectra: A
comparison of two adaptive methods (PLSR and ANN),
Remote
Sensing of Environment
, 110(1):59–78.
Farifteh, J., J., F.V.D Van, and E.J.M. Carranza, 2007. Similarity
measures for spectral discrimination of salt-affected soils,
International Journal of Remote Sensing
, 28(23):5273–5293.
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January 2018
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