A simulation of soil water content based on remote sensing in a semi-arid Mediterranean agricultural landscape

Sánchez, Nilda; Martínez-Fernández, José; Rodríguez-Ruiz, M.; Torres, Emy Shilena; Calera, Alfonso

doi:10.5424/sjar/2012102-611-11

Cited by 11 publications

(6 citation statements)

References 36 publications

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“…Within the six approaches classified by Wang-Erlandsson et al [ 94 ], for the estimation of root zone water storage capacity, RS-based studies are generally based on field observations and look up tables [ 95 , 96 , 97 , 98 ]. Nevertheless, some recent studies propose the optimization-calibration and inverse modelling approaches with diverse purposes.…”

Section: Remote Sensing-based Estimates Of Evapotranspirationmentioning

confidence: 99%

Remote Sensing for Crop Water Management: From ET Modelling to Services for the End Users

Calera

Campos

Osann

et al. 2017

Sensors

198

120

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The experiences gathered during the past 30 years support the operational use of irrigation scheduling based on frequent multi-spectral image data. Currently, the operational use of dense time series of multispectral imagery at high spatial resolution makes monitoring of crop biophysical parameters feasible, capturing crop water use across the growing season, with suitable temporal and spatial resolutions. These achievements, and the availability of accurate forecasting of meteorological data, allow for precise predictions of crop water requirements with unprecedented spatial resolution. This information is greatly appreciated by the end users, i.e., professional farmers or decision-makers, and can be provided in an easy-to-use manner and in near-real-time by using the improvements achieved in web-GIS methodologies (Geographic Information Systems based on web technologies). This paper reviews the most operational and explored methods based on optical remote sensing for the assessment of crop water requirements, identifying strengths and weaknesses and proposing alternatives to advance towards full operational application of this methodology. In addition, we provide a general overview of the tools, which facilitates co-creation and collaboration with stakeholders, paying special attention to these approaches based on web-GIS tools.

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Section: Remote Sensing-based Estimates Of Evapotranspirationmentioning

confidence: 99%

Remote Sensing for Crop Water Management: From ET Modelling to Services for the End Users

Calera

Campos

Osann

et al. 2017

Sensors

198

120

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“…The real or actual evapotranspiration (ET a ) during 2014 is obtained from the HidroMORE hydrological model [42,43]. It is an operational distributed model based on the water balance equation [50].…”

Section: Hidromore Modelmentioning

confidence: 99%

“…The seasonal dependence of this relationship is evaluated by means of SM and LST temporal correlation using a modeled ET as supporting information. The Hydrological Model for Operational estimates of Recharge and actual Evapotransporation (HidroMORE) is used to generate the modeled ET [42,43]. Additionally, a method to estimate the critical SM from the SM-LST coupling/decoupling transitions has been developed and effectively applied to in situ and spaceborne measurements.…”

Section: Introductionmentioning

confidence: 99%

Multi-Temporal Evaluation of Soil Moisture and Land Surface Temperature Dynamics Using in Situ and Satellite Observations

et al. 2016

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Soil moisture (SM) is an important component of the Earth's surface water balance and by extension the energy balance, regulating the land surface temperature (LST) and evapotranspiration (ET). Nowadays, there are two missions dedicated to monitoring the Earth's surface SM using L-band radiometers: ESA's Soil Moisture and Ocean Salinity (SMOS) and NASA's Soil Moisture Active Passive (SMAP). LST is remotely sensed using thermal infrared (TIR) sensors on-board satellites, such as NASA's Terra/Aqua MODIS or ESA & EUMETSAT's MSG SEVIRI. This study provides an assessment of SM and LST dynamics at daily and seasonal scales, using 4 years (2011-2014) of in situ and satellite observations over the central part of the river Duero basin in Spain. Specifically, the agreement of instantaneous SM with a variety of LST-derived parameters is analyzed to better understand the fundamental link of the SM-LST relationship through ET and thermal inertia. Ground-based SM and LST measurements from the REMEDHUS network are compared to SMOS SM and MODIS LST spaceborne observations. ET is obtained from the HidroMORE regional hydrological model. At the daily scale, a strong anticorrelation is observed between in situ SM and maximum LST (R ≈ −0.6 to −0.8), and between SMOS SM and MODIS LST Terra/Aqua day (R ≈ − 0.7). At the seasonal scale, results show a stronger anticorrelation in autumn, spring and summer (in situ R ≈ −0.5 to −0.7; satellite R ≈ −0.4 to −0.7) indicating SM-LST coupling, than in winter (in situ R ≈ +0.3; satellite R ≈ −0.3) indicating SM-LST decoupling. These different behaviors evidence changes from water-limited to energy-limited moisture flux across seasons, which are confirmed by the observed ET evolution. In water-limited periods, SM is extracted from the soil through ET until critical SM is reached. A method to estimate the soil critical SM is proposed. For REMEDHUS, the critical SM is estimated to be ∼0.12 m 3 /m 3 , stable over the study period and consistent between in situ and satellite observations. A better understanding of the SM-LST link could not only help improving the representation of LST in current hydrological and climate prediction models, but also refining SM retrieval or microwave-optical disaggregation algorithms, related to ET and vegetation status.

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“…This period is chosen to avoid crops changing in the study area. The composite image of the six NDVIs is the input for the classification, based on a hybrid vector-raster segmentation method [34]. This method isolates the less changing categories (forest-pasture, vineyard, unproductive, and water) by means of the vector information from the geographic information system (GIS) for Agricultural Plots of Castilla y León Region.…”

Section: B Remotely Sensed Datamentioning

confidence: 99%

A Downscaling Approach for SMOS Land Observations: Evaluation of High-Resolution Soil Moisture Maps Over the Iberian Peninsula

Piles

Sánchez

Vall-llossera

et al. 2014

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

166

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The ESA's Soil Moisture and Ocean Salinity (SMOS) mission is the first satellite devoted to measure the Earth's surface soil moisture. It has a spatial resolution of km and a 3-day revisit. In this paper, a downscaling algorithm is presented as a new ability to obtain multiresolution soil moisture estimates from SMOS using visible-to-infrared remotely sensed observations. This algorithm is applied to combine 2 years of SMOS and MODIS Terra/Aqua data over the Iberian Peninsula into fine-scale (1 km) soil moisture estimates. Disaggregated soil moisture maps are compared to 0-5 cm ground-based measurements from the REMEDHUS network. Three matching strategies are employed: 1) a comparison at 40 km spatial resolution is undertaken to ensure SMOS sensitivity is preserved in the downscaled maps; 2) the spatiotemporal correlation of downscaled maps is analyzed through comparison with point-scale observations; and 3) high-resolution maps and ground-based observations are aggregated per land-use to identify spatial patterns related with vegetation activity and soil type. Results show that the downscaling method improves the spatial representation of SMOS coarse soil moisture estimates while maintaining temporal correlation and root mean squared differences with ground-based measurements. The dynamic range of in situ soil moisture measurements is reproduced in the highresolution maps, including stations with different mean soil wetness conditions. Downscaled maps capture the soil moisture dynamics of general land uses, with the exception of irrigated crops. This evaluation study supports the use of this downscaling approach to enhance the spatial resolution of SMOS observations over semi-arid regions such as the Iberian Peninsula.

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A simulation of soil water content based on remote sensing in a semi-arid Mediterranean agricultural landscape

Cited by 11 publications

References 36 publications

Remote Sensing for Crop Water Management: From ET Modelling to Services for the End Users

Remote Sensing for Crop Water Management: From ET Modelling to Services for the End Users

Multi-Temporal Evaluation of Soil Moisture and Land Surface Temperature Dynamics Using in Situ and Satellite Observations

A Downscaling Approach for SMOS Land Observations: Evaluation of High-Resolution Soil Moisture Maps Over the Iberian Peninsula

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