Hydrology and Earth System Sciences Estimation of soil moisture using trapezoidal relationship between remotely sensed land surface temperature and vegetation index (original) (raw)

A New Drought Index for Soil Moisture Monitoring Based on MPDI-NDVI Trapezoid Space Using MODIS Data

Remote Sensing

The temperature vegetation dryness index (TVDI) has been commonly implemented to estimate regional soil moisture in arid and semi-arid regions. However, the parameterization of the dry edge in the TVDI model is performed with a constraint to define the maximum water stress conditions. Mismatch of the spatial scale between visible and thermal bands retrieved from remotely sensed data and terrain variations also affect the effectiveness of the TVDI. Therefore, this study proposed a new drought index named the condition vegetation drought index (CVDI) to monitor the temporal and spatial variations of soil moisture status by substituting the land surface temperature (LST) with the modified perpendicular drought index (MPDI). In situ soil moisture observations at crop and pasture sites in Victoria were used to validate the effectiveness of the CVDI. The results indicate that the dry and wet edges in the parameterization scheme of the CVDI formed a better-defined trapezoid shape than that...

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

Proceeding MDPI, 2018

Soil moisture takes an important part involving climate, vegetation and drought. This paper explains how to calculate the soil moisture index and the role of soil moisture. The objective of this study is to assess the moisture content in soil and soil moisture mapping by using remote sensing data in the selected study area. We applied the remote sensing technique which relies on the use of the soil moisture index (SMI) which uses the data obtained from satellite sensors in its algorithm. The relationship between land surface temperature (LST) and the normalized difference vegetation index (NDVI) are based on experimental parameterization for the soil moisture index. Multispectral satellite data (visible, red and near-infrared (NIR) and thermal infrared sensor (TIRS) bands) were utilized for assessment of LST and to make vegetation indices map. Geographic Information System (GIS) and image processing software were utilized to determine the LST and NDVI. NDVI and LST are considered as essential data to obtain SMI calculation. The statistical regression analysis of NDVI and LST were shown in standardized regression coefficient. NDVI values are within range −1 to 1 where negative values present loss of vegetation or contaminated vegetation, whereas positive values explain healthy and dense vegetation. LST values are the surface temperature in °C. SMI is categorized into classes from no drought to extreme drought to quantitatively assess drought. The final result is obtainable with the values range from 0 to 1, where values near 1 are the regions with a low amount of vegetation and surface temperature and present a higher level of soil moisture. The values near 0 are the areas with a high amount of vegetation and surface temperature and present the low level of soil moisture. The results indicate that this method can be efficiently applied to estimate soil moisture from multi-temporal Landsat images, which is valuable for monitoring agricultural drought and flood disaster assessment.

Multi-Index Soil Moisture Estimation from Satellite Earth Observations: Comparative Evaluation of the Topographic Wetness Index (TWI), the Temperature Vegetation Dryness Index (TVDI) and the Improved TVDI (iTVDI)

Journal of the Indian Society of Remote Sensing, 2016

Soil moisture estimation from satellite earth observation has emerged effectively advantageous due to the high temporal resolution, spatial resolution, coverage, and processing convenience it affords. In this paper, we present a study carried out to estimate soil moisture level at every location within Enugu State Nigeria from satellite earth observation. Comparative analysis of multiple indices for soil moisture estimation was carried out with a view to evaluating the robustness, correlation, appropriateness and accuracy of the indices in estimating the spatial distribution of soil moisture level in Enugu State. Results were correlated and validated with In-Situ soil moisture observations from multi-sample points. To achieve this, the Topographic Wetness Index (TWI), based on digital elevation data, the Temperature Vegetation Dryness Index (TVDI) and an improved TVDI (iTVDI) incorporating air temperature and a Digital Elevation Model (DEM) were calculated from ASTER global DEM and Landsat images. Possible dependencies of the indices on land cover type, topography, and precipitation were explored. In-Situ soil moisture data were used to validate the derived indices. The results showed that there was a positive significant relationship between iTVDI versus TVDI (R = 0.53, P value \ 0.05), while in iTVDI versus TWI (R = 0.00, P value [ 0.05) and TVDI versus TWI (R =-0.01, P value [ 0.05) no significant relationship existed. There was a strong relationship between iTVDI and topography, land cover type, and precipitation than other indices (TVDI, TWI). In situ measured soil moisture values showed negative significant relationship with TVDI (R =-0.52, P value \ 0.05) and iTVDI (R =-0.63, P value \ 0.05) but not with TWI (R =-0.10, P value [ 0.05). The iTVDI outperformed the other two index; having a stronger relationship with topography, precipitation, land cover classes and soil moisture. It concludes that although iTVDI outperformed other indices (TVDI, TWI) in soil moisture estimation, the decision of which index to apply is dependent on available data, the intent of usage and spatial scale.

Evaluating land surface moisture conditions from the remotely sensed temperature/vegetation index measurements

Remote Sensing of Environment, 2002

Land soil moisture conditions play a critical role in evaluating terrestrial environmental conditions related to ecological, hydrological, and atmospheric processes. Extensive efforts to exploit the potential of remotely sensed observations to help quantify this complex variable are still underway. Among the various methods, several investigators have explored a combination of surface temperatures and spectral vegetation index (SVI) measurements, the TVX method, as a means to account for the variable influence of vegetation cover in soil moisture assessment. Although considerable empirical evidence has been presented exploring the potential of TVX methods to assess regional moisture conditions, less attention has been given to assessing the underlying biophysics of the observed TVX patterns. In this study, the Simplified Simple Biosphere (SSiB) model is exploited to examine the factors that lead to the observed TVX relation. For a range of typical, midlatitude, growing season conditions, the SSiB model produces the expected TVX relationship, surface temperature decreases with increasing SVI values. The most critical factors that cause the TVX relation to vary include near-surface soil moisture (2 cm), incident radiation (IR), and, to a lesser degree, wind speed. Whereas many empirical studies have suggested that the slope of the TVX relation may provide an important diagnostic of soil moisture conditions, in this analysis, the impact of plant stomatal function is shown to confuse this interpretation of the TVX slope. However, other aspects of the TVX metrics, specifically bare soil temperature and canopy temperature, do provide diagnostic near-surface soil moisture information. Growing season variations in TVX metrics were examined for the conditions recorded at the Hydrological and Atmospheric Pilot Experiment-Modelisation du Bilan Hydrique (HAPEX-Mobilhy) study site. The results from this analysis indicate that soil and canopy temperatures vary as a function of soil moisture conditions and, to a lesser degree, as a result of varying solar insolation and wind speed. The results also show that the TVX metrics are able to provide daily soil moisture variation up to 2 cm of soil depth and seasonal trend up to 10 cm. Using the satellite-derived surface temperatures and a SSiB-derived retrieval equation, the retrieved soil moistures at the HAPEX-Mobilhy site generally closely approximate the conditions recorded on the ground.

Estimation of Soil Moisture Percentage Using LANDSAT-based Moisture Stress Index

Journal of Remote Sensing & GIS

The global agronomy community needs quick and frequent information on soil moisture variability and spatial trends in order to maximize crop production to meet growing food demands in a changing climate. However, in situ soil moisture measurement is expensive and labor intensive. Remote sensing based biophysical and predictive regression modeling approach have the potential for efficiently estimating soil moisture content over large areas. The study investigates the use of Moisture Stress Index (MSI) to estimate soil moisture variability in Alabama. In situ data were obtained from Soil Climate Analysis Network (SCAN) sites in Alabama and MSI developed from LANDSAT 8 OLI and LANDSAT 5 TM data. Pearson product moment correlation analysis showed that MSI strongly correlates with 16-day average growing season soil moisture measurements, with negative correlations of-0.519,-0.482 and-0.895 at 5, 10, and 20 cm soil depths respectively. The correlations of MSI and growing season moisture were low at sites where soil moisture was extremely low (<-0.3 at all depths). Simple linear regression model constructed for soil moisture at 20 cm depth (R²=0.79, p<0.05) correlated well with MSI values and was successfully used to estimate soil moisture percentage within a standard error of ± 3. Resulting MSI products were used to successfully produce the spatial distribution of soil moisture percentage at 20 cm depth. The study concludes that MSI is a good indicator of soil moisture conditions, and could be efficiently utilized in areas where in situ soil moisture data are unavailable.

Estimation of soil moisture content by remote sensing methods: A review

Journal of Pharmacognosy and Phytochemistry, 2018

Understanding the spatial and temporal variations of soil moisture is crucial for the land surface processes and their management. The soil moisture content in the surface layers of the soil is an important parameter for many applications in hydrology, horticulture, geotechnical, agriculture and meteorology. Hence accurate estimation of spatial and temporal variation in soil moisture content is important. Recently, remote sensing techniques have been used to estimate soil moisture. Estimation of soil moisture by remote sensing techniques provides only surface layer information and is unable to observe the entire soil column. On the other hand field measurement provide valuable information regarding both surface and subsurface soil moisture, but are insufficient to characterize the spatial and temporal variability of soil moisture at larger scale. Therefore, remote sensing methods have an edge over field methods in terms of spatial and temporal scale. This paper presents a comprehens...

Downscaling of Surface Soil Moisture Retrieval by Combining MODIS/Landsat and In Situ Measurements

Remote Sensing, 2018

Soil moisture, especially surface soil moisture (SSM), plays an important role in the development of various natural hazards that result from extreme weather events such as drought, flooding, and landslides. There have been many remote sensing methods for soil moisture retrieval based on microwave or optical thermal infrared (TIR) measurements. TIR remote sensing has been popular for SSM retrieval due to its fine spatial and temporal resolutions. However, because of limitations in the penetration of optical TIR radiation and cloud cover, TIR methods can only be used under clear sky conditions. Microwave SSM retrieval is based on solid physical principles, and has advantages in cases of cloud cover, but it has low spatial resolution. For applications at the local scale, SSM data at high spatial and temporal resolutions are important, especially for agricultural management and decision support systems. Current remote sensing measurements usually have either a high spatial resolution or a high temporal resolution, but not both. This study aims to retrieve SSM at both high spatial and temporal resolutions through the fusion of Moderate Resolution Imaging Spectroradiometer (MODIS) and Land Remote Sensing Satellite (Landsat) data. Based on the universal triangle trapezoid, this study investigated the relationship between land surface temperature (LST) and the normalized difference vegetation index (NDVI) under different soil moisture conditions to construct an improved nonlinear model for SSM retrieval with LST and NDVI. A case study was conducted in Iowa, in the United States (USA) (Lat: 42.2 •~4 2.7 • , Lon: −93.6 •~− 93.2 •), from 1 May 2016 to 31 August 2016. Daily SSM in an agricultural area during the crop-growing season was downscaled to 120-m spatial resolution by fusing Landsat 8 with MODIS, with an R 2 of 0.5766, and RMSE from 0.0302 to 0.1124 m 3 /m 3 .