Assessment and Impact of Soil Moisture Index in Agricultural Drought Estimation Using Remote Sensing and GIS Techniques

Saha, Arnab; Patil, Manti; Goyal, Vikas; Rathore, Devendra Singh

doi:10.3390/ecws-3-05802

Cited by 44 publications

(20 citation statements)

References 14 publications

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“…The Landsat 8 images were selected in February, April and June of the year 2017. SMI is calculated based on the combination of the NDVI and LST calculation using (1) [27,28].…”

Section: Methodsmentioning

confidence: 99%

Soil moisture index estimation from landsat 8 images for prediction and monitoring landslide occurrences in Ulu Kelang, Selangor, Malaysia

Tajudin

Ya’acob

Ali

et al. 2021

IJECE

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Soil moisture is one of the contributing factors that accelerates soil erosion and landslide events due to the increase in pore pressure which eventually reduces the soil strength. For landslide prediction and monitoring purposes, large-scale measurement involves estimating the soil moisture. However, estimation of soil moisture usually involves point-based measurements at a particular site and time, which is difficult to capture the spatial and temporal soil moisture dynamics. This paper presents the estimation of the SMI using Landsat 8 images for prediction and monitoring of landslide events in Ulu Kelang, Selangor. The selected SMI map for dry, moist, and wet seasons are obtained from climatology rainfall analysis over 20-year periods (1998-2017). SMI is assessed based on remote sensing data which are land surface temperature (LST) and normalized difference vegetation index (NDVI) using GIS software. Overall results indicated that rainfall distribution is high during inter-monsoon (IM), followed by northeast monsoon (NEM) and southwest monsoon (SWM) season. High rainfall distribution is a direct contributor towards SMI condition. Results from simulation show that April 2017 is known to have the highest SMI estimation season and selected to be the best SMI mapping parameter to be applied for prediction and monitoring of landslide events.

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“…The Landsat 8 images were selected in February, April and June of the year 2017. SMI is calculated based on the combination of the NDVI and LST calculation using (1) [27,28].…”

Section: Methodsmentioning

confidence: 99%

Soil moisture index estimation from landsat 8 images for prediction and monitoring landslide occurrences in Ulu Kelang, Selangor, Malaysia

Tajudin

Ya’acob

Ali

et al. 2021

IJECE

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show abstract

“…Among various indices, the LST-based SMI is a widely used index to monitor soil moisture in vegetated land. The SMI is the proportion of the difference between the current soil moisture and the permanent wilting point to the field capacity as well as the residual soil moisture [48]. The value of the SMI ranges between 0 and 1, where 0 refers to very dry conditions and 1 denotes extremely wet conditions.…”

Section: Estimation Of Soil Moisture Index (Smi)mentioning

confidence: 99%

An Assessment of Drought Stress in Tea Estates Using Optical and Thermal Remote Sensing

2021

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Drought is one of the detrimental climatic factors that affects the productivity and quality of tea by limiting the growth and development of the plants. The aim of this research was to determine drought stress in tea estates using a remote sensing technique with the standardized precipitation index (SPI). Landsat 8 OLI/TIRS images were processed to measure the land surface temperature (LST) and soil moisture index (SMI). Maps for the normalized difference moisture index (NDMI), normalized difference vegetation index (NDVI), and leaf area index (LAI), as well as yield maps, were developed from Sentinel-2 satellite images. The drought frequency was calculated from the classification of droughts utilizing the SPI. The results of this study show that the drought frequency for the Sylhet station was 38.46% for near-normal, 35.90% for normal, and 25.64% for moderately dry months. In contrast, the Sreemangal station demonstrated frequencies of 28.21%, 41.02%, and 30.77% for near-normal, normal, and moderately dry months, respectively. The correlation coefficients between the SMI and NDMI were 0.84, 0.77, and 0.79 for the drought periods of 2018–2019, 2019–2020 and 2020–2021, respectively, indicating a strong relationship between soil and plant canopy moisture. The results of yield prediction with respect to drought stress in tea estates demonstrate that 61%, 60%, and 60% of estates in the study area had lower yields than the actual yield during the drought period, which accounted for 7.72%, 11.92%, and 12.52% yield losses in 2018, 2019, and 2020, respectively. This research suggests that satellite remote sensing with the SPI could be a valuable tool for land use planners, policy makers, and scientists to measure drought stress in tea estates.

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“…i. Soil Moisture Index (SMI): Soil moisture plays an important role in the regulation of the water cycle and flood inundation period (Saha et al 2018). If soil contains high moisture and even it receives further rainfall, soil cannot hold more water.…”

Section: Land Use and Land Covermentioning

confidence: 99%

A Comparative Evaluation of GIS based Flood Susceptibility Models: A Case of Kopai River Basin, Eastern India

Ghosh

Sutradhar

Das³

et al. 2021

Preprint

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Flood is a typical natural disaster which every year causes enormous damage to the natural environment, buildings and victims worldwide. The efficiency of flooding depends upon numerous criteria: flood strength, amplitude, frequency, flow time, changes in plan and river geometry, etc. In this research, three statistical models such as the frequency ratio, Shannon's entropy, and weight of evidence have been used to identify flood susceptibility regions in the Kopai River Basin. In order to run these statistical models, flooded and non-flooded pixels were employed. The outcome of these three models demonstrates that the upper reach of the basin is characterised by non-flooded zones and the bottom reach of the basin is high flooded zones. The lower part of the basin is therefore more vulnerable than that of the upper portion. 11 thematic layers are employed to get the final output of flood susceptibility zones. e.g., soil type, normalised vegetation difference index, normalised water differential index, river lift, slope, drainage density, land usage and land cover, rainfall, soil moisture indices, surface roughness. 370 sample points have been taken as test data and finally receiver operating curves have been plotted for testing the degree of accuracy. The result of the validation reveals that the accuracy of the frequency ratio is 96.5%. Shannon entropy is 91.2% accurate and the weight of evidence is 97.1% accurate. Weight of evidence is therefore the best model for flood susceptibility zones identification for this Kopai basin.

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Assessment and Impact of Soil Moisture Index in Agricultural Drought Estimation Using Remote Sensing and GIS Techniques

Cited by 44 publications

References 14 publications

Soil moisture index estimation from landsat 8 images for prediction and monitoring landslide occurrences in Ulu Kelang, Selangor, Malaysia

Soil moisture index estimation from landsat 8 images for prediction and monitoring landslide occurrences in Ulu Kelang, Selangor, Malaysia

An Assessment of Drought Stress in Tea Estates Using Optical and Thermal Remote Sensing

A Comparative Evaluation of GIS based Flood Susceptibility Models: A Case of Kopai River Basin, Eastern India

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