Subsurface Soil Moisture Estimation by VI–LST Method

Holzman, Mauro; Rivas, Raúl Eduardo; Bayala, Martín Ignacio

doi:10.1109/lgrs.2014.2314617

Cited by 44 publications

(34 citation statements)

References 20 publications

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“…The TVDI and the VTCI have been widely used for SM estimation and drought monitoring over different regions. The results showed reasonable agreement with in situ SM observations [21], [36], [43], [63]. The difference between the TVDI and the VTCI is that the TVDI has a negative correlation with SM, whereas the VTCI positively correlates with SM.…”

supporting

confidence: 79%

Spatial Downscaling of Satellite Soil Moisture Data Using a Vegetation Temperature Condition Index

Peng

Loew

Zhang

et al. 2016

IEEE Trans. Geosci. Remote Sensing

137

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Microwave remote sensing has been largely applied to retrieve soil moisture (SM) from active and passive sensors. The obvious advantage of microwave sensors is that SM can be obtained regardless of atmospheric conditions. However, existing global SM products only provide observations at coarse spatial resolutions, which often hamper their application in regional hydrological studies. On the other hand, the vegetation temperature condition index (VTCI) has been widely used to monitor the SM status. It is based on high-spatial-resolution visible and infrared satellite observations. The aim of this study is to develop a simple and efficient downscaling approach for estimating accurate SM at higher spatial resolution. The VTCI calculated from the Moderate Resolution Imaging Spectroradiometer is used to downscale the coarse-resolution SM product that has been developed under the framework of the European Space Agency's Climate Change Initiative (CCI) projects. The original and downscaled SM estimates are further validated against the in situ SM observations collected in the Yunnan province (southwest China). It is found that the accuracy level of CCI SM is similar to the results from previously published validation studies. The downscaled SM can maintain the accuracy of CCI SM and, at the same time, present more spatial details, demonstrating the feasibility of the proposed method. Overall, the notable advantages of the proposed method are simplicity, limited data requirements and purely relying on satellite measurements, and comparable accuracy level to other complex downscaling schemes. It will facilitate local hydrological applications, particularly in data-scarce regions, where the above-listed characteristics are important and useful.

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supporting

confidence: 79%

Spatial Downscaling of Satellite Soil Moisture Data Using a Vegetation Temperature Condition Index

Peng

Loew

Zhang

et al. 2016

IEEE Trans. Geosci. Remote Sensing

137

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“…Argentine Pampas Region is a plain of over 1.5 million km 2 , with lands of high fertility and productivity (Quiroz Londoño, Romanelli, Lima, Massone, & Martínez, 2016). There, 90% of the country's grain production takes place (Magrin, Travasso, & Rodríguez, 2005), and 48% of the cattle stock is raised (Canosa, Feldkamp, Urruti, Morris, & Moscoso, 2013), because it is the most productive rain-fed and the strongest economic region of Argentina (Holzman, Rivas, & Bayala, 2014). In this region, there are gently hilly areas which occupy a small portion with respect to that occupied by plains: only 13% of the region (Mateucci, 2012).…”

Section: Introductionmentioning

confidence: 99%

Surface moisture area during rainfall–run‐off events to understand the hydrological dynamics of a basin in a plain region

Ares

Holzman

Entraigas

et al. 2018

Hydrological Processes

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The understanding of the hydrology of plain basins may be improved by the combined analysis of rainfall–run‐off records and remote sensed surface moisture data. Our work evaluates the surface moisture area (SMA) produced during rainfall–run‐off events in a plain watershed of the Argentine Pampas Region, and studies which hydrological variables are related to the generated SMA. The study area is located in the upper and middle basins of the Del Azul stream, characterized by the presence of small gently hilly areas surrounded by flat landscapes. Data from 9 rainfall–run‐off events were analysed. MODIS surface reflectance data were processed to calculate SMA subsequent to the peak discharge (post‐SMA), and previous to the rainfall events (prev‐SMA), to consider the antecedent wetness. Rainfall–run‐off data included total precipitation depth (P), maximum intensity of rainfall over 6 hr (I6max), surface run‐off registered between the beginning of the event and the day previous to the analysed MODIS scene (R), peak flow (Qp), and flood intensity (IF). In contrast with other works, post‐SMA showed a negative relationship with the R. Three groups of cases were identified: (a) Events of low I6max, high prev‐SMA, and low R were associated with slow and weakly channelized flow over plain areas, leading to saturated overland flow (SOF), with large SMA; (b) events of high I6max, low prev‐SMA, and medium to high R were rapidly transported along the gentle slopes of the basin, related to Hortonian overland flow (HOF) and low post‐SMA; and (c) events of medium to high I6max and prev‐SMA with medium R were related to heterogeneous input‐antecedent‐run‐off conditions combined: Local spatial conditions may have produced HOF or SOF, leading to an averaged response with medium SMA. The interactions between the geomorphology of the basin, the characteristics of the events, and the antecedent conditions may explain the obtained results. This analysis is relevant for the general knowledge of the hydrology of large plains, whose functioning studies are still in their early stages.

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“…Results from various studies all show the great capacity of thermal and/or optical derived vegetation indices in monitoring both surface and root zone soil moisture. Over the years, different vegetation indices have been applied in estimating soil moisture and vegetation response to its spatial and temporal variations [2,[13][14][15]. To date, there is limited work done to investigate the applicability of the Landsat-based Moisture Stress Index in estimating soil moisture at different soil depths.…”

Section: Introductionmentioning

confidence: 99%

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

Welikhe¹,

Essamuah–Quansah²,

Fall³

et al. 2017

J Remote Sensing & GIS

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Subsurface Soil Moisture Estimation by VI–LST Method

Cited by 44 publications

References 20 publications

Spatial Downscaling of Satellite Soil Moisture Data Using a Vegetation Temperature Condition Index

Spatial Downscaling of Satellite Soil Moisture Data Using a Vegetation Temperature Condition Index

Surface moisture area during rainfall–run‐off events to understand the hydrological dynamics of a basin in a plain region

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

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