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Generating soil salinity, soil moisture, soil pH from satellite imagery and its analysis
Ghazali M.F.a,b,d, Wikantika K.a,b, Harto A.B.a, Kondoh A.c
a Center for Remote Sensing, Bandung Institute of Technology (CRS-ITB), Bandung, Indonesia
b ForMIND Institute, Indonesia
c Center for Environmental Remote Sensing (CeRES), Chiba University, Japan
d Department of Geodesy and Geomatics Engineering, Faculty of Engineering, University of Lampung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2019 China Agricultural UniversityIn an agricultural field, the water content and salt content are defined as soil moisture and soil salinity and have to be estimated precisely. The changing of these two factors can be assessed using remote sensing technology. This study was conducted by analysing the Landsat 8 satellite images, soil data of field surveys, laboratory analyses and statistical computations. Soil properties such as soil moisture and soil salinity were estimated using soil moisture index (SMI) and soil salinity index (SSI), respectively. The research combined and integrated the soil data from survey and laboratory with Landsat 8 satellite images to build two multiple regression equations model named the soil pH Index (SpHI). They are based on bare soil and paddy leaf models as the explanatory factors of soil moisture and soil salinity changes. All the computation processes were replicated three times using three different dates of Landsat 8 satellite images to produce the multi-temporal analysis. Soil moisture increased after 30 days, while the salt content was only trace amounts. Both proposed models detected 4.49–7.59 of soil pH, 4.66 in bare soil model and 6.62 in paddy leaf model. During the planting period, the soil pH in bare soil model decreased to 2.12–6.47 while the paddy leaf model increased to 4.49–7.59 with RMSE 1.40 and PRMSE 24% of accuracy. The spatial relationship between soil pH, soil salinity and soil moisture are linear but varied in correlation level from weak, moderate to strong. Based on the bare soil model, the relationship between soil pH and soil moisture shows a weak negative relationship with R2 8.37% and a strong positive relationship with R2 81.94% in paddy area and bare soil area respectively, as like as in paddy area based on the paddy leaf model with R2 100%. The relationship between soil temperature and soil pH shows a weak negative relationship for all models and a moderate negative relationship of soil salinity and soil pH in bare soil area based on the bare soil model with R2 34.89%.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Bare soils,LANDSAT,Soil moisture index,Soil pH,Soil salinity[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Bare soil model,Landsat 8,Paddy leaf model,Soil moisture index,Soil pH,Soil salinity index[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]I would like to thank to the Center for Remote Sensing of Bandung Institute of Technology, and The Center for Environmental Remote Sensing of Chiba University for valuable guidance and support during the research work. Also for the LPDP, thank you for the scholarship and the full funding support for the research.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1016/j.inpa.2019.08.003[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]