Improving root zone soil moisture estimation from surface remote sensing measurements
Satellite soil moisture provides a wider range of spatial data than in-situ observations. Satellites are sensitive to measuring soil moisture in a few centimetres of the soil layer. However, root zone soil moisture is more important, especially in vegetated areas. Therefore, it is necessary to inver...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/360289 |
| Acceso en línea: | https://hdl.handle.net/2117/360289 |
| Access Level: | acceso abierto |
| Palabra clave: | Remote sensing Satellites SMOS soil moisture root zone soil moisture estimatiom soil moisture index Teledetecció Satèl·lits Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Teledetecció |
| Sumario: | Satellite soil moisture provides a wider range of spatial data than in-situ observations. Satellites are sensitive to measuring soil moisture in a few centimetres of the soil layer. However, root zone soil moisture is more important, especially in vegetated areas. Therefore, it is necessary to invert the near-surface soil moisture collected by satellite missions to estimate root zone soil moisture. This study quantified the association between near-surface and root zone soil moisture using in-situ data from the REMEDHUS network in Spain and Soil Moisture Ocean Salinity (SMOS) mission from 2014-2018, comparing SMOS with in-situ observations based on an exponential filter approach, optimizing an optimal characteristic time length (Topt) by computing the correlation coefficients (R) of the filtered satellite time series with in-situ time series. The results show that the exponential filter's main factor T is sensitive to soil moisture and varies depending on the climate. When using this method to infer root zone soil moisture, the eight stations' average R-value reached 0.71, meeting the "Strong correlation" standard. The root zone soil moisture obtained by SMOS surface inversion is compared with in situ soil moisture. The mean R-value of soil moisture in the root zone based on SMOS is 0.58, which is approximate to the "baseline error (0.6)" -- the accuracy of the surface soil moisture estimated by SMOS. However, attention should be paid to the effects of soil moisture season and soil texture on the experimental results. |
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