An operational method for the disaggregation of land surface temperature to estimate actual evapotranspiration in the arid region of Chile

Olivera-Guerra, Luis; Mattar, Cristián; Merlin, Olivier; Durán‐Alarcón, Claudio; Santamaría-Artigas, Andrés; Fuster, Rodrigo

doi:10.1016/j.isprsjprs.2017.03.014

Cited by 49 publications

(24 citation statements)

References 51 publications

(57 reference statements)

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“…These methods are particularly advantageous in their ability to produce estimates on timescales as short as weeks. The remote sensing approaches generally use surface energy balance arguments to partition the net incoming solar radiation energy into sensible heat flux, ground heat flux, and latent heat flux (energy equivalent of ET mass flux) [6][7][8][9][10]. Different algorithms applied to the same input remote sensing data sets can make predictions of the latent heat…”

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confidence: 99%

Combining Remote Sensing and Water-Balance Evapotranspiration Estimates for the Conterminous United States

2017

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Evapotranspiration (ET) is a key component of the hydrologic cycle, accounting for~70% of precipitation in the conterminous U.S. (CONUS), but it has been a challenge to predict accurately across different spatio-temporal scales. The increasing availability of remotely sensed data has led to significant advances in the frequency and spatial resolution of ET estimates, derived from energy balance principles with variables such as temperature used to estimate surface latent heat flux. Although remote sensing methods excel at depicting spatial and temporal variability, estimation of ET independently of other water budget components can lead to inconsistency with other budget terms. Methods that rely on ground-based data better constrain long-term ET, but are unable to provide the same temporal resolution. Here we combine long-term ET estimates from a water-balance approach with the SSEBop (operational Simplified Surface Energy Balance) remote sensing-based ET product for 2000-2015. We test the new combined method, the original SSEBop product, and another remote sensing ET product (MOD16) against monthly measurements from 119 flux towers. The new product showed advantages especially in non-irrigated areas where the new method showed a coefficient of determination R 2 of 0.44, compared to 0.41 for SSEBop or 0.35 for MOD16. The resulting monthly data set will be a useful, unique contribution to ET estimation, due to its combination of remote sensing-based variability and ground-based long-term water balance constraints.Keywords: evapotranspiration; water resources; remote sensing; water balance; evaporation; transpiration Background and RationaleEvapotranspiration (ET) is the quantity of water that includes net water evaporation and plant transpiration. It plays a significant role in the hydrologic cycle, returning about 70% of precipitation across the conterminous United States (CONUS) to the atmosphere [1,2]. Because it is generally the second largest component of the water budget, following precipitation, researchers and water resource managers require accurate and reliable ET estimates to understand water availability and distribution for both short and long-term water resources management.The use of remote sensing-based methods of estimating ET has increased in recent years [3], and has proven useful in various applications such as agricultural water use monitoring and estimation [4], and hydrologic simulation [5]. These methods are particularly advantageous in their ability to produce estimates on timescales as short as weeks. The remote sensing approaches generally use surface energy balance arguments to partition the net incoming solar radiation energy into sensible heat flux, ground heat flux, and latent heat flux (energy equivalent of ET mass flux) [6][7][8][9][10]. Different algorithms applied to the same input remote sensing data sets can make predictions of the latent heat

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confidence: 99%

Combining Remote Sensing and Water-Balance Evapotranspiration Estimates for the Conterminous United States

2017

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“…The Chimbarongo site has been also used for several comparisons of water and mass balance [28,29] and also can be used for testing new evapotranspiration methods over heterogeneous agricultural sites. In the case of Copiapó valley sites, those measurements were used to develop and validate a disaggregation method for LST that was applied to an operational evapotranspiration estimation method for arid and semi-arid regions [47]. Several other applications in soil moisture, thermal remote sensing and surface energy balance can be performed by using LAB-net.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

The LAB-Net Soil Moisture Network: Application to Thermal Remote Sensing and Surface Energy Balance

Mattar

Santamaría-Artigas

Durán‐Alarcón

et al. 2016

Data

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A set of Essential Climate Variables (ECV) have been defined to be monitored by current and new remote sensing missions. The ECV retrieved at global scale need to be validated in order to provide reliable products to be used in remote sensing applications. For this, test sites are required to use in calibration and validation of the remote sensing approaches in order to improve the ECV retrievals at global scale. The southern hemisphere presents scarce test sites for calibration and validation field campaigns that focus on soil moisture and land surface temperature retrievals. In Chile, remote sensing applications related to soil moisture estimates have increased during the last decades because of the drought and water use conflicts that generate a strong interest on improved water demand estimates. This work describes the Laboratory for Analysis of the Biosphere (LAB)-NETwork, called herein after 'LAB-net', which was designed to be the first network in Chile for remote sensing applications. The test sites were placed in four sites with different cover types: vineyards and olive orchards located in the semi-arid region of Atacama, an irrigated raspberry crop in the Mediterranean climate zone of Chimbarongo, and a rainfed pasture in the south of Chile. Over each site, well implemented meteorological and radiative flux instrumentation was installed and continuously recorded the following parameters: soil moisture and temperature at two ground levels (10 and 20 cm), air temperature and relative humidity, net radiation, global radiation, radiometric temperature (8-14 µm), rainfall and soil heat flux. The LAB-net data base post-processing procedure is also described here. As an application, surface remote sensing products such as soil moisture data derived from the Soil Moisture Ocean Salinity (SMOS) and Land Surface Temperature (LST) extracted from the MODIS-MOD11A1 and GOES LST from Copernicus products were compared to in situ data in Oromo LAB-net site. Moreover, land surface energy flux estimation is also shown as an application of LAB-net data base. These applications revealed a good performance between in situ and remote sensing data. LAB-net data base also contributes to provide suitable information for land surface energy budget and therefore water resources management at cultivars scale. The data based generated by LAB-net is freely available for any research or scientific purpose related to current and future remote sensing applications.

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“…For the first category, ET at a high spatio-temporal resolution is estimated by different ET models using intermediate variables that are closely related to ET, such as the reference ET fraction (ET r F), normalized differential vegetation index (NDVI) and land surface temperature (LST). These intermediate variables can be fused using a regression model [12,25] or different spatio-temporal data fusion models [23,26,27]. For the second category, different spatio-temporal data fusion models are directly applied to fuse ET.…”

Section: Introductionmentioning

confidence: 99%

An Improved Spatio-Temporal Adaptive Data Fusion Algorithm for Evapotranspiration Mapping

Wang

Tang

et al. 2019

Remote Sensing

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Continuous high spatio-temporal resolution monitoring of evapotranspiration (ET) is critical for water resource management and the quantification of irrigation water efficiency at both global and local scales. However, available remote sensing satellites cannot generally provide ET data at both high spatial and temporal resolutions. Data fusion methods have been widely applied to estimate ET at a high spatio-temporal resolution. Nevertheless, most fusion methods applied to ET are initially used to integrate land surface reflectance, the spectral index and land surface temperature, and few studies completely consider the influencing factor of ET. To overcome this limitation, this paper presents an improved ET fusion method, namely, the spatio-temporal adaptive data fusion algorithm for evapotranspiration mapping (SADFAET), by introducing critical surface temperature (the corresponding temperature to decide soil moisture), importing the weights of surface ET-indicative similarity (the influencing factor of ET, which is estimated from remote sensing data) and modifying the spectral similarity (the differences in spectral characteristics of different spatial resolution images) for the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). We fused daily Moderate Resolution Imaging Spectroradiometer (MODIS) and periodic Landsat 8 ET data in the SADFAET for the experimental area downstream of the Heihe River basin from April to October 2015. The validation results, based on ground-based ET measurements, indicated that the SADFAET could successfully fuse MODIS and Landsat 8 ET data (mean percent error: −5%), with a root mean square error of 45.7 W/m2, whereas the ESTARFM performed slightly worse, with a root mean square error of 50.6 W/m2. The more physically explainable SADFAET could be a better alternative to the ESTARFM for producing ET at a high spatio-temporal resolution.

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An operational method for the disaggregation of land surface temperature to estimate actual evapotranspiration in the arid region of Chile

Cited by 49 publications

References 51 publications

Combining Remote Sensing and Water-Balance Evapotranspiration Estimates for the Conterminous United States

Combining Remote Sensing and Water-Balance Evapotranspiration Estimates for the Conterminous United States

The LAB-Net Soil Moisture Network: Application to Thermal Remote Sensing and Surface Energy Balance

An Improved Spatio-Temporal Adaptive Data Fusion Algorithm for Evapotranspiration Mapping

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