Spatial and Temporal Analysis of Sodic Soils in Sharda Sahayak Canal Command of Amethi District using Sentinel-2A/2B MSI Data
Spatial and temporal analysis of sodic soils
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Authors
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RH Rizvi
ICAR-CSSRI Regional Research Station, Lucknow- 226002, Uttar Pradesh, India
https://orcid.org/0000-0003-1553-4382
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Sanjay Arora
ICAR-CSSRI Regional Research Station, Lucknow- 226002, Uttar Pradesh, India
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Chhedi Lal Verma
ICAR-CSSRI Regional Research Station, Lucknow- 226002, Uttar Pradesh, India
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Anil Kumar Dubey
ICAR-CSSRI Regional Research Station, Lucknow- 226002, Uttar Pradesh, India
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RK Yadav
ICAR-Central Soil Salinity Research Institute, Karnal-132001, Haryana, India
Keywords:
LULC, Sharda Canal, Sentinel-2, sodic soils, unsupervisedAbstract
In Sharda Canal Command of Uttar Pradesh, about 2,60,000 ha area is waterlogged and 2,53,300 ha area is salt affected. Therefore, this command area having problems of waterlogging and salinity needs to be properly and regularly monitored for site-specific reclamation and managment. Geospatial technologies have potential to analyse spatially and temporally a geographical area say district for desired feature. 48 soil samples at 12 locations and four depths with their GPS locations were collected from Sharda Canal Command in Amethi district. These samples were analyzed in laboratory for different soil parameters like pH2, EC2, OC, Na, K, pHe, ECe, etc. Most of the soil samples have pH more than 10 indicating that soils in the Sharda Command is highly sodic. Soil EC varied from 1.55 to 13.32 dS/m at 0-15 cm depth and ESP of the samples ranged from 81-93%. Soil pH2 and EC2 varied from 10.13 to 10.67 and dS/m, respectively at 15-30 cm depth. Organic carbon varied from 0.20 to 0.43% at 0-15 cm depth and from 0.11 to 0.40% at 15-30 cm depth. Sentinel-2A/2B MSI remote sensing images (spatial resolution 10-20m) for pre-monsoon period (May 2022 & May 2023) were downloaded and pre-processed. Land use land cover (LULC) analysis for Amethi district was done using an open source software QGIS (v. 3.24.0). Unsupervised classification method (ISODATA) was applied on district’s images and the output image was reclassified for five LULC classes mainly water/water bodies, built-ups/sandy areas, vegetation/plantation, bare/fallow land and sodic soils. Area under plantation/vegetation and bare/fallow land accounted for more than 27 percent in both the years. Estimated area under sodic soils increased from 5.50 percent in May 2022 to 6.39 percent in May 2023 in Amethi district.
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References
Aksoy S, Yildrim A, Gorji T. et al. (2021) Assessing the performance of machine learning algorithms for soil salinity mapping in Google Earth Engine Platform using Sentinel-2A and Landsat-8 OLI data. Advance in Space Research, doi: 10.1016/j.asr.2021.10.024.
Al-Gaadi KA, Tola EK, Mudugundu R and Fulleros RB (2021) Sentinel-2 images for effective mapping of soil salinity in agricultural fields. Current Science 121 (3): 384-390.
Elhag M (2016) Evaluation of different soil salinity mapping using remote sensing techniques in arid ecosystems, Saudi Arabia. Journal of Sensors, doi: 10.1155/2016/7596175.
Mandal AK and Sharma RC (2011) Delineation and characterization of waterlogged salt-affected soils in IGNP using remote sensing and GIS. Journal of the Indian Society of Remote Sensing 39(1): 39-50.
Mandal AK, Sharma RC and Singh G (2009) Assessment of salt-affected soils in India using GIS. Geocarto International 24:437-456.
Moussa I, Walter C, Michot D, Boukary IA, Nicholas H, Pichelin P and Guero Y (2020) Soil salinity assessment in irrigated paddy fields of the Niger valley using a four year time series of Sentinel-2 images. Remote Sensing 12, 3399 (doi: 3390/rs12203399).
Sahana M, Rehman S, Patel PP, Dou Jie, Hong H and Sajjad H (2021) Assessing the degree of soil salinity in the Indian Sunderban Biosphere Reserve using measured soil electric conductivity and remote sensing data driven salinity indices. Arabian Journal of Geosciences 13, 1289.
Suleymanov A, Gabbasova I, Abakumov E and Kostecki J (2021) Soil salinity assessment from satellite data in the Trans-Ural steppe zone (Southern Ural, Russia). Soil Science Annual, 72(1):132233 (doi: 10.37501/soilsa/132233).
Suleymanov A, Tuktarova I, Belan L, Suleymanov R, Gabbasova I and Arastanova L (2023) Spatial prediction of soil properties using Random forest, k-nearest neighbourhood and cubist approaches in the foothills of the Ural Mountains, Russia. Modelling Earth Systems and Environment 9: 3461-3471 (doi: 10.1007/s40808-023-01723-4).
Wang N, Xue J, Biswas A, He Young and Shi Zhou (2020) Integrating remote sensing and landscape characteristics to estimate soil salinity using machine learning methods: A case study from southern Xinjiang, China. Remote Sensing 12, 4118 (doi: 10.3990/rs12244118).
Zarei A, Hassanlou M and Mahdianpari M (2021) A comparison of machine learning models for soil salinity estimation using multispectral earth observation data. ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences (doi: 10.5194/isprs-annals-V-3-2021-257-2021).
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