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Artículo

Soil moisture estimation over flat lands in the Argentinian Pampas region using Sentinel-1A data and non-parametric methods

García, Gabriel AgustinIcon ; Venturini, VirginiaIcon ; Brogioni, Marco; Walker, ElisabetIcon ; Rodríguez, Leticia
Fecha de publicación: 05/2019
Editorial: Taylor & Francis
Revista: International Journal of Remote Sensing
ISSN: 0143-1161
e-ISSN: 1366-5901
Idioma: Inglés
Tipo de recurso: Artículo publicado
Clasificación temática:
Sensores Remotos

Resumen

A procedure for soil moisture (SM) estimation over flat lands in the Argentinian Pampas region, using the water balance equation that considers SM to be the result of water inflows and outflows to the soil system, is presented. In recent years, remotely sensed data with Synthetic Aperture Radar (SAR) and radiometer sensors have been used to develop different methodologies to obtain SM maps. Thus, a variety of methodologies with different levels of complexity are available nowadays. These models require soil information such as soil physical properties and mineral composition, not readily available in Argentina and many other remote areas of the world. The procedure presented in this paper takes into account water input and output processes of the soil system and represents them with different hydro-environmental variables and SAR data. The water balance equation was solved with Multiple Linear Regression (MLR), Multivariate Adaptive Regression Splines (MARS) and Artificial Neural Network (ANN) statistical models, fed with readily available data over Comisión Nacional de Actividades Espaciales (CONAE) core site located in Cordoba province, Argentina. The resulting models were obtained with precipitation (PP), air temperature (T a ) and relative humidity (RH) observations and with SAR data from the Sentinel-1A satellite mission. The accuracy of the model estimates represents 10% of the observed measured values of SM and is in line with state of the art algorithms. Results suggest that any model can be used with similar precision, since they show similar errors, although the MLR method allows analyzing and quantifying the errors introduced by the variables.
Palabras clave: Soil Moisture , Sentinel-1A images , Multiple linear regression , Artificial neural network , Multivariate adaptive regression , Flat lands
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info:eu-repo/semantics/restrictedAccess Excepto donde se diga explícitamente, este item se publica bajo la siguiente descripción: Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Unported (CC BY-NC-SA 2.5)
Identificadores
URI: http://hdl.handle.net/11336/175695
DOI: https://doi.org/10.1080/01431161.2018.1552813
URL: https://www.tandfonline.com/doi/abs/10.1080/01431161.2018.1552813
Colecciones
Articulos(CCT - SANTA FE)
Articulos de CTRO.CIENTIFICO TECNOL.CONICET - SANTA FE
Citación
García, Gabriel Agustin; Venturini, Virginia; Brogioni, Marco; Walker, Elisabet; Rodríguez, Leticia; Soil moisture estimation over flat lands in the Argentinian Pampas region using Sentinel-1A data and non-parametric methods; Taylor & Francis; International Journal of Remote Sensing; 40; 10; 5-2019; 3689-3720
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