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Discussion papers | Copyright
https://doi.org/10.5194/hess-2018-94
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 05 Apr 2018

Research article | 05 Apr 2018

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This discussion paper is a preprint. A revision of the manuscript is under review for the journal Hydrology and Earth System Sciences (HESS).

The value of satellite remote sensing soil moisture data and the DISPATCH algorithm in irrigation fields

Mireia Fontanet1,2,3, Daniel Fernández-Garcia2,3, and Francesc Ferrer1 Mireia Fontanet et al.
  • 1LabFerrer, Cervera, 25200, Spain
  • 2Department of Civil and Environmental Engineering, Universitat Politècnica de Catalunya (UPC), Barcelona, 08034, Spain
  • 3Associated Unit: Hydrogeology Group (UPC-CSIC)

Abstract. Soil moisture measurements are needed in a large number of applications such as climate change, watershed water balance and irrigation management. One of the main characteristics of this property is that soil moisture is highly variable with both space and time, hindering the estimation of a representative value. Deciding how to measure soil moisture before undertaking any type of study is therefore an important issue that needs to be addressed correctly. Nowadays, different kinds of methodologies exist for measuring soil moisture; Remote Sensing, soil moisture sensors or gravimetric measurements. This work is focused on how to measure soil moisture for irrigation scheduling, where soil moisture sensors are the main methodology for monitoring soil moisture. One of its disadvantages, however, is that soil moisture sensors measure a small volume of soil, and do not take into account the existing variability in the field. In contrast, Remote Sensing techniques are able to estimate soil moisture with a low spatial resolution, and thus it is not possible to apply these estimations to agricultural applications. In order to solve this problem, different kinds of algorithms have been developed for downscaling these estimations from low to high resolution. The DISPATCH algorithm downscales soil moisture estimations from 40km to 1km resolution using SMOS satellite soil moisture, NDVI and LST from MODIS sensor estimations. In this work, DISPATCH estimations are compared with soil moisture sensors and gravimetric measurements to validate the DISPATCH algorithm in two different hydrologic scenarios; (1) when wet conditions are maintained around the field for rainfall events, and (2) when it is local irrigation that maintains wet conditions. Results show that the DISPATCH algorithm is sensitive when soil moisture is homogenized during general rainfall events, but not when local irrigation generates occasional heterogeneity. In order to explain these different behaviours, we have examined the spatial variability scales of NDVI and LST data, which are the variables involved in the downscaling process provided by the MODIS sensor. Sample variograms show that the spatial scales associated with the NDVI and LST properties are too large to represent the variations of the average water content at the site, and this could be a reason for why the DISPATCH algorithm is unable to detect soil moisture increments caused by local irrigation.

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There are different methodologies which are able to measure soil moisture, such as soil moisture sensors ans remote sensing. In this work, we have compared soil moisture sensors ans remote sensing soil moisture data for irrigation management at field scale. One of the main conclusion is that even though soil moisture sensors measure soil moisture in a given point of a field, remote sensing estimations should be used in areas where there is not spatial heterogeneity and they need a validation.
There are different methodologies which are able to measure soil moisture, such as soil moisture...
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