Welcome to International Network for Natural Sciences | INNSpub

Paper Details

Research Paper | November 10, 2022

VIEWS 55
| Download 30

Soil Moisture Estimation Using Multitemporal Remote Sensing Data in Tabunio Watershed

Nurlina, Syarifuddin Kadir, Ahmad Kurnain, Wahyuni Ilham, Akhmad Rizalli Saidy, Ichsan Ridwan

Key Words:


Int. J. Biosci.21(5), 148-158, November 2022

DOI: http://dx.doi.org/10.12692/ijb/21.5.148-158

Certification:

IJB 2022 [Generate Certificate]

Abstract

Soil moisture, vegetation cover, and land surface temperature influence water energy balance, eco-hydrological processes, and water resources management. This research focused on the spatial and temporal variability of soil moisture, vegetation cover, land surface temperature, and Temperature Vegetation Dryness Index (TVDI) in Tabunio watershed. In this research, soil moisture and vegetation cover data were recorded and statistically evaluated from 2005 until 2020 using remote sensing data. Soil moisture, vegetation cover, and land surface temperature were significantly different over the research period. Increased vegetation cover had an inverse influence on land surface temperature and TVDI while directly affecting soil moisture. TVDI averages were 0 – 0.83. Decreased soil dryness in the period 2022 can increase water availability by controlling water resources. Proper watershed management can improve soil moisture and water availability. Vegetation cover protection and biological activities can be used to restore the watersheds. The approach combination of LST and NDVI to calculate TVDI is able to estimate the soil moisture as its estimation is extremely important for the evaluation of the overall ecological setting as well as the monitoring of the moisture content of wetland environments.

VIEWS 55

Copyright © 2022
By Authors and International Network for
Natural Sciences (INNSPUB)
http://innspub.net
This article is published under the terms of the Creative
Commons Attribution Liscense 4.0

Soil Moisture Estimation Using Multitemporal Remote Sensing Data in Tabunio Watershed

Amani M, Parsian S, MirMazloumi SM, Aieneh O. 2016. Two new soil moisture indices based on the NIR-red triangle space of Landsat-8 data. Thoracic Surgery Clinics 50, 176–186. https://doi.org/10.1016/j.jag.2016.03.018

Amazirh A, Merlin O, Er-Raki S, Gao Q, Rivalland V, Malbeteau Y, Khabba S, Escorihuela MJ. 2018. Retrieving surface soil moisture at high spatio-temporal resolution from a synergy between Sentinel-1 radar and Landsat thermal data: A study case over bare soil. Remote Sensing of Environment 211(November 2017), 321–337. https://doi.org/10.1016/j.rse.2018.04.013

Amiri R, Weng Q, Alimohammadi A, Alavipanah SK. 2009. Spatial-temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use/cover in the Tabriz urban area, Iran. Remote Sensing of Environment 113(12), 2606–2617. https://doi.org/10.1016/j.rse.2009.07.021

Ansari S, Deshmukh RR. 2017. Estimation of Soil Moisture Content: A Review. International Journal of Theoretical and Applied Mechanics 12(3), 571–577. http://www.ripublication.com

Bartels GK, Castro NM. dos R, Pedrollo O, Collares GL. 2021. Soil moisture estimation in two layers for a small watershed with neural network models: Assessment of the main factors that affect the results. Catena 207 (June), 105631. https://doi.org/10.1016/j.catena.2021.105631

Baruti J. 2004. Study of soil moisture in relation to soil erosion in the proposed Tancítaro Geopark, Central Mexico 104. http://citeseerx.ist.psu.edu/viewdoc/download?doi= 10.1.1.58.7440&rep=rep1&type=pdf

Bhan S, Behera UK. 2014. Conservation agriculture in India – Problems, prospects and policy issues 1 Introduction 2 Conservation agriculture definition and goals. International Soil and Water Conservation Research 2(4), 1–12. https://doi.org/10.1016/S2095-6339(15)30053-8

Borrelli P, Schütt B. 2014. Assessment of soil erosion sensitivity and post-timber-harvesting erosion response in a mountain environment of Central Italy. Geomorphology 204, 412–424. https://doi.org/10.1016/j.geomorph.2013.08.022

Campos de Oliveira MH, Sari V, dos Reis Castro NM, Pedrollo OC. 2017. Estimation of soil water content in watershed using artificial neural networks. Hydrological Sciences Journal 62(13), 2120–2138. https://doi.org/10.1080/02626667.2017.1364844

Doan P, Luky RIA. 2020. Performance Analysis of Soil Moisture Monitoring Based on Internet of Things With Lora Communications. Journal of Southwest Jiaotong University 55(5), 55–64. https://doi.org/10.35741/issn.0258-2724.55.5.31

Dong Q, Han J, Luo L, Wang H, Lei N, Liu Z, He J. 2019. Distribution characteristics of soil moisture of small watershed in gully catchment of the Loess Plateau of China. IOP Conference Series: Earth and Environmental Science 384(1). https://doi.org/10.1088/1755-1315/384/1/012207

Fu P, Weng Q. 2016. A time series analysis of urbanization induced land use and land cover change and its impact on land surface temperature with Landsat imagery. Remote Sensing of Environment 175, 205–214. https://doi.org/10.1016/j.rse.2015.12.040

Gao Q, Zribi M, Escorihuela MJ, Baghdadi N. 2017. Synergetic use of sentinel-1 and sentinel-2 data for soil moisture mapping at 100 m resolution. Sensors (Switzerland) 17(9). https://doi.org/10.3390/s17091966

Guha S, Govil H. 2021. An assessment on the relationship between land surface temperature and normalized difference vegetation index. Environment, Development and Sustainability 23(2), 1944–1963. https://doi.org/10.1007/s10668-020-00657-6

Guo J, Wang K, Wang T, Bai N, Zhang H, Cao Y, Liu H. 2022. Spatiotemporal Variation of Vegetation NDVI and Its Climatic Driving Forces in Global Land Surface. Polish Journal of Environmental Studies 31(4), 3541–3549. https://doi.org/10.15244/pjoes/147194

Hao F, Zhang X, Ouyang W, Skidmore AK, Toxopeus AG. 2012. Vegetation NDVI Linked to Temperature and Precipitation in the Upper Catchments of Yellow River. Environmental Modeling and Assessment 17(4), 389–398. https://doi.org/10.1007/s10666-011-9297-8

Horrocks C, Dungait J, Cardenas L, Heal K. 2014. Does extensification lead to enhanced provision of ecosystem services from soils in UK agriculture. Land Use Policy 38, 123–128. https://doi.org/10.1016/j.landusepol.2013.10.023

Hosseini M, Saradjian MR. 2011. Multi-index-based soil moisture estimation using MODIS images. International Journal of Remote Sensing 32(21), 6799–6809. https://doi.org/10.1080/01431161.2010.523027

Jackson TJ, Chen D, Cosh M, Li F, Anderson M, Walthall C, Doriaswamy P, HuntE. R. 2004. Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans. Remote Sensing of Environment 92(4), 475–482. https://doi.org/10.1016/j.rse.2003.10.021

Kadir S, Badaruddin, Nurlina, Farma E. 2017. Power Recovery Support Tabunio Watershed Based on Analysis of Erosion Based on Geographic Information System in the Province of South Kalimantan. Mediterranean Journal of Social Sciences 8(4–1), 73–81. https://doi.org/10.2478/mjss-2018-0075

Kadir S, Ilham W, Ridwan I, Kurnanin A, Hartati W, Nurofiq HF, Nurlina. 2022. Assessment of erosion hazard levels on various land cover types in Panjaratan sub watershed. International Journal of Biosciences 21(3), 85–94. http://dx.doi.org/10.12692/ijb/21.3.85-94

Kazemzadeh M, Salajegheh A, Malekian A, Liaghat A, Hashemi H. 2021. Soil moisture change analysis under watershed management practice using in situ and remote sensing data in a paired watershed. Environmental Monitoring and Assessment 193(5). https://doi.org/10.1007/s10661-021-09078-y

Mobasheri MR. 2016. Soil moisture content assessment based on Landsat 8 red, near- infrared, and thermal channels 10(2). https://doi.org/10.1117/1.JRS.10.026011

Mohamed ES, Ali A, El-Shirbeny M, Abutaleb K, Shaddad SM. 2020. Mapping soil moisture and their correlation with crop pattern using remotely sensed data in arid region. Egyptian Journal of Remote Sensing and Space Science 23(3), 347–353. https://doi.org/10.1016/j.ejrs.2019.04.003

Moran MS, Peters-Lidard CD, Watts JM, Mc Elroy S. 2004. Estimating soil moisture at the watershed scale with satellite-based radar and land surface models. Canadian Journal of Remote Sensing 30(5), 805–826. https://doi.org/10.5589/m04-043

Muro J, Strauch A, Heinemann S, Steinbach S, Thonfeld F, Waske B, Diekkrüger B. 2018. Land surface temperature trends as indicator of land use changes in wetlands. International Journal of Applied Earth Observation and Geoinformation 70, 62–71. https://doi.org/10.1016/J.JAG.2018.02.002

Nurlina, Kadir S, Kurnain A, Ilham W, Ridwan I. 2022. Analysis of soil erosion and its relationships with land use/cover in Tabunio watershed. IOP Conference Series: Earth and Environmental Science 976(1). https://doi.org/10.1088/1755-1315/976/1/012027

Pablos M, Martínez-Fernández J, Piles M, Sánchez N, Vall-llossera M, Camps A. 2016. Multi-temporal evaluation of Soil Moisture and land surface temperature dynamics using in situ and satellite observations. Remote Sensing 8(7). https://doi.org/10.3390/rs8070587

Paddies WF. 2016. Estimating Soil Moisture with Landsat Data and Its Application in Extracting the Spatial Distribution of Winter Flooded Paddies. https://doi.org/10.3390/rs8010038

Peng W, Wang J Zhang J, Zhang Y. 2020. Soil moisture estimation in the transition zone from the Chengdu Plain region to the Longmen Mountains by field measurements and LANDSAT 8 OLI/TIRS-derived indices. Arabian Journal of Geosciences 13, 168. https://doi.org/10.1007/s12517-020-5152-z

Potic I, Bugarski M, Varenica JM. 2019. Soil Moisture Determination Using Remote Sensing Data for the. 2017 World Bank Conference on Land and Poverty, May. https://doi.org/10.13140/RG.2.2.30426.59845

Ridwan I, Nurlina, Putri WE. 2022. Estimation of Peatland Fire Carbon Emissions Using Remote Sensing and GIS Physics Study Program , Faculty of Mathematics and Natural Sciences Lambung Mangkurat. International Journal of Biosciences 20(6), 246–253. http://dx.doi.org/10.12692/ijb/20.6.246-253

Sardiana IK, Susila D, Supadma AA, Saifulloh M. 2017. Soil Fertility Evaluation and Land Management of Dryland Farming at Tegallalang Sub-District, Gianyar Regency, Bali, Indonesia. IOP Conference Series: Earth and Environmental Science 98(1). https://doi.org/10.1088/1755-1315/98/1/012043

Shao H, Sun X, Wang H, Zhang X, Xiang Z, Tan R, Chen X, Xian W, Qi J. 2016. A method to the impact assessment of the returning grazing land to grassland project on regional eco-environmental vulnerability. Environmental Impact Assessment Review 56(2016), 155–167. https://doi.org/10.1016/j.eiar.2015.10.006

Sidi Almouctar MA, Wu Y, Kumar A, Zhao F, Mambu KJ, Sadek M. 2021. Spatiotemporal analysis of vegetation cover changes around surface water based on NDVI: a case study in Korama basin, Southern Zinder, Niger. Applied Water Science 11(1), 1–14. https://doi.org/10.1007/s13201-020-01332-x

Solangi GS, Siyal AA, Siyal P. 2019. Spatiotemporal Dynamics of Land Surface Temperature and Its Impact on the Vegetation. Civil Engineering Journal 5(8), 1753–1763. https://doi.org/10.28991/cej-2019-03091368

Vicente-Serrano SM, Pons-Fernández X, Cuadrat-Prats JM. 2004. Mapping soil moisture in the central Ebro river valley (northeast Spain) with Landsat and NOAA satellite imagery: A comparison with meteorological data. International Journal of Remote Sensing 25(20), 4325–4350. https://doi.org/10.1080/01431160410001712990

Wang J, Ding J, Yu D, Teng D, He B, Chen X, Ge X, Zhang Z, Wang Y, Yang X, Shi T, Su F. 2020. Machine learning-based detection of soil salinity in an arid desert region, Northwest China: A comparison between Landsat-8 OLI and Sentinel-2 MSI. Science of the Total Environment 707, 136092. https://doi.org/10.1016/j.scitotenv.2019.136092

Younis SMZ, Iqbal J. 2015. Estimation of soil moisture using multispectral and FTIR techniques. Egyptian Journal of Remote Sensing and Space Science 18(2), 151–161. https://doi.org/10.1016/j.ejrs.2015.10.001

Zeng Y, Feng Z, Xiang N. 2004. Assessment of soil moisture using Landsat ETM + temperature/vegetation index in semiarid environment 04(c), 4306–4309.

SUBMIT MANUSCRIPT

Style Switcher

Select Layout
Chose Color
Chose Pattren
Chose Background