Evaluation of soil moisture estimation techniques based on Sentinel-1 observations over wheat fields
Fecha
2023Autor
Versión
Acceso abierto / Sarbide irekia
Tipo
Artículo / Artikulua
Versión
Versión publicada / Argitaratu den bertsioa
Identificador del proyecto
Impacto
|
10.1016/j.agwat.2023.108422
Resumen
Soil moisture (SM) is a key variable in agriculture and its monitoring is essential. SM determines the amount of
water available to plants, having a direct impact on the development of crops, on the forecasting of crop yields
and on the surveillance of food security. Microwave remote sensing offers a great potential for estimating SM
because it is sensitive to the dielectric characteristics of ...
[++]
Soil moisture (SM) is a key variable in agriculture and its monitoring is essential. SM determines the amount of
water available to plants, having a direct impact on the development of crops, on the forecasting of crop yields
and on the surveillance of food security. Microwave remote sensing offers a great potential for estimating SM
because it is sensitive to the dielectric characteristics of observed surface that depend on surface soil moisture.
The objective of this study is the evaluation of three change detection methodologies for SM estimation over
wheat at the agricultural field scale based on Sentinel-1 time series: Short Term Change Detection (STCD), TU
Wien Change Detection (TUWCD) and Multitemporal Bayesian Change Detection (MTBCD). Different methodological alternatives were proposed for the implementation of these techniques at the agricultural field scale. Soil
moisture measurements from eight experimental wheat fields were used for validating the methodologies. All
available Sentinel-1 acquisitions were processed and the eventual benefit of correcting for vegetation effects in
backscatter time series was evaluated. The results were rather variable, with some experimental fields achieving
successful performance metrics (ubRMSE ~ 0.05 m3
/m3
) and some others rather poor ones (ubRMSE > 0.12 m3
/
m3
). Evaluating median performance metrics, it was observed that both TUWCD and MTBCD methods obtained
better results when run with vegetation corrected backscatter time series (ubRMSE ~0.07 m3
/m3
) whereas STCD
produced similar results with and without vegetation correction (ubRMSE ~0.08 m3
/m3
). The soil moisture
content had an influence on the accuracy of the different methodologies, with higher errors observed for drier
conditions and rain-fed fields, in comparison to wetter conditions and irrigated fields. Taking into account the
spatial scale of this case study, results were considered promising for the future application of these techniques in
irrigation management. [--]
Materias
Soil wetness,
Agriculture,
SAR,
Change detection,
Field scale
Editor
Elsevier
Publicado en
Agricultural Water Management, 287 (2023) 108422
Departamento
Universidad Pública de Navarra. Departamento de Agronomía, Biotecnología y Alimentación /
Nafarroako Unibertsitate Publikoa. Agronomia, Bioteknologia eta Elikadura Saila /
Universidad Pública de Navarra. Departamento de Ingeniería /
Nafarroako Unibertsitate Publikoa. Ingeniaritza Saila /
Universidad Pública de Navarra/Nafarroako Unibertsitate Publikoa. Institute on Innovation and Sustainable Development in Food Chain - ISFOOD
Versión del editor
Entidades Financiadoras
This work was supported by the Spanish Ministry of Science and
Innovation and the European Regional Development Fund (MICINN/
FEDER-UE) through projects [CGL2016–75217-R and
PID2019–107386RB-I00 / AEI / 10.13039/501100011033] and
doctoral grant [BES-2017–080560]. Open access funding provided by the Public University of Navarre.