Monitoring daily evapotranspiration over two California vineyards using Landsat 8 in a multi-sensor data fusion approach

Semmens, K. A.; Anderson, Martha C.; Kustas, William P.; Gao, Feng; Alfieri, Joseph G.; McKee, L.; Prueger, John H.; Hain, Christopher; Cammalleri, Carmelo; Yang, Yun; Xia, Ting; Sánchez, Luis A.; Alsina, María Mar; Vélez, Mónica

doi:10.1016/j.rse.2015.10.025

Cited by 225 publications

(204 citation statements)

References 50 publications

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“…An important next step would be to combine and fuse observations from other sensors such as Sentinel-2 and VIIRS to increase the temporal frequency of remote sensing based ET estimate. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), for example, showed the success and promises of using data fusion to derive Landsat-like daily ET estimates at the field scale [10,52]. Other methods have also been developed to improve the accuracy of three hourly or daily ET mapping, including a combination of the feedback model (GG model) with SEBAL [53].…”

Section: Conclusion and Discussionmentioning

confidence: 99%

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Evapotranspiration Estimate over an Almond Orchard Using Landsat Satellite Observations

Jin

Kandelous

et al. 2017

Remote Sensing

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California growers face challenges with water shortages and there is a strong need to use the least amount of water while optimizing yield. Timely information on evapotranspiration (ET), a dominant component of crop consumptive water use, is critical for growers to tailor irrigation management based on in-field spatial variability and in-season variations. We evaluated the performance of a remote sensing-based approach, Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC), in mapping ET over an almond orchard in California, driven by Landsat satellite observations. Reference ET from a network of weather stations over well-watered grass (ET o ) was used for the internal calibration and for deriving ET at daily and extended time period, instead of alfalfa based reference evapotranspiration (ET r ). Our study showed that METRIC daily ET estimates during Landsat overpass dates agreed well with the field measurements. During 2009-2012, a root mean square error (RMSE) of 0.53 mm/day and a coefficient of determination (R 2 ) of 0.87 were found between METRIC versus observed daily ET. Monthly ET estimates had a higher accuracy, with a RMSE of 12.08 mm/month, a R 2 of 0.90, and a relatively small relative mean difference (RMD) of 9.68% during 2009-2012 growing seasons. Net radiation and Normalized Difference Vegetation Index (NDVI) from remote sensing observations were highly correlated with spatial and temporal ET estimates. An empirical model was developed to estimate daily ET using NDVI, net radiation (R n ), and vapor pressure deficit (VPD). The validation showed that the accuracy of this easy-to-use empirical method was slightly lower than that of METRIC but still reasonable, with a RMSE of 0.71 mm/day when compared to ground measurements. The remote sensing based ET estimate will support a variety of State and local interests in water use and irrigation management, for both planning and regulatory/compliance purposes, and it provides the farmers observation-based guidance for site-specific and time-sensitive irrigation management.

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Section: Conclusion and Discussionmentioning

confidence: 99%

“…New approaches to take advantage of remote sensing observations to map ET have been developed, especially in recent decades, due to increasingly available free satellite observations from multiple sensors [7][8][9][10]. Empirical algorithms were built to relate ET to remotely sensed vegetation indices and weather data [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Evapotranspiration Estimate over an Almond Orchard Using Landsat Satellite Observations

Jin

Kandelous

et al. 2017

Remote Sensing

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“…LST captures signals of crop stress and variable soil evaporation that are often missed by crop coefficient remote sensing techniques, which are based on empirical regressions with reflectancebased vegetation indices. Furthermore, diagnostic estimates of ET from the surface energy balance provide an independent estimate of landscape water use that is a valuable benchmark for comparison with estimates based on water balance or hydrologic modeling (Hain et al, 2015;Yilmaz et al, 2014). Finally, the range in spatial resolution and coverage of existing TIR data sources enables mapping of ET from the plot or field scale (< 100 m resolution) up to continental or global coverage at 1-5 km resolution.…”

Section: Introductionmentioning

confidence: 99%

“…Cammalleri et al (2013) proposed a data fusion method to combine ET estimates derived from geostationary, MODIS and Landsat TIR data, attempting to exploit the spatiotemporal advantages of each class of satellite to map daily ET at a sub-field scale. This ET fusion approach has been successfully applied over rainfed and irrigated corn, soybean and cotton fields (Cammalleri et al, , 2014, as well as irrigated vineyards (Semmens et al, 2015). The work described here constitutes the first application to forest land cover types, representing a substantially different roughness and physiological regime than that of shorter crops.…”

Section: Introductionmentioning

confidence: 99%

Daily Landsat-scale evapotranspiration estimation over a forested landscape in North Carolina, USA, using multi-satellite data fusion

Yang

Anderson

Gao

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. As a primary flux in the global water cycle, evapotranspiration (ET) connects hydrologic and biological processes and is directly affected by water and land management, land use change and climate variability. Satellite remote sensing provides an effective means for diagnosing ET patterns over heterogeneous landscapes; however, limitations on the spatial and temporal resolution of satellite data, combined with the effects of cloud contamination, constrain the amount of detail that a single satellite can provide. In this study, we describe an application of a multi-sensor ET data fusion system over a mixed forested/agricultural landscape in North Carolina, USA, during the growing season of 2013. The fusion system ingests ET estimates from the Two-Source Energy Balance Model (TSEB) applied to thermal infrared remote sensing retrievals of land surface temperature from multiple satellite platforms: hourly geostationary satellite data at 4 km resolution, daily 1 km imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) and biweekly Landsat thermal data sharpened to 30 m. These multiple ET data streams are combined using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) to estimate daily ET at 30 m resolution to investigate seasonal water use behavior at the level of individual forest stands and land cover patches. A new method, also exploiting the STARFM algorithm, is used to fill gaps in the Landsat ET retrievals due to cloud cover and/or the scan-line corrector (SLC) failure on Landsat 7. The retrieved daily ET time series agree well with observations at two AmeriFlux eddy covariance flux tower sites in a managed pine plantation within the modeling domain: US-NC2 located in a mid-rotation (20-year-old) loblolly pine stand and US-NC3 located in a recently clear-cut and replanted field site. Root mean square errors (RMSEs) for NC2 and NC3 were 0.99 and 1.02 mm day −1 , respectively, with mean absolute errors of approximately 29 % at the daily time step, 12 % at the monthly time step and 0.7 % over the full study period at the two flux tower sites. Analyses of water use patterns over the plantation indicate increasing seasonal ET with stand age for young to mid-rotation stands up to 20 years, but little dependence on age for older stands. An accounting of consumptive water use by major land cover classes representative of the modeling domain is presented, as well as relative partitioning of ET between evaporation (E) and transpiration (T ) components obtained with the TSEB. The study provides new insights about the effects of management and land use change on water yield over forested landscapes.

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“…These include the integration of the feedback method that uses the complimentary relationship between actual ET and pan ET [60], data fusion methods (i.e., combining different data sources) [61,62], and the backward-averaged iterative two-source surface temperature and energy balance solution (BAITSSS) algorithm [63].…”

Section: Remote Sensing Datamentioning

confidence: 99%

An Intercomparison of Satellite-Based Daily Evapotranspiration Estimates under Different Eco-Climatic Regions in South Africa

et al. 2017

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Knowledge of evapotranspiration (ET) is essential for enhancing our understanding of the hydrological cycle, as well as for managing water resources, particularly in semi-arid regions. Remote sensing offers a comprehensive means of monitoring this phenomenon at different spatial and temporal intervals. Currently, several satellite methods exist and are used to assess ET at various spatial and temporal resolutions with various degrees of accuracy and precision. This research investigated the performance of three satellite-based ET algorithms and two global products, namely land surface temperature/vegetation index (TsVI), Penman-Monteith (PM), and the Meteosat Second Generation ET (MET) and the Global Land-surface Evaporation: the Amsterdam Methodology (GLEAM) global products, in two eco-regions of South Africa. Daily ET derived from the eddy covariance system from Skukuza, a sub-tropical, savanna biome, and large aperture boundary layer scintillometer system in Elandsberg, a Mediterranean, fynbos biome, during the dry and wet seasons, were used to evaluate the models. Low coefficients of determination (R 2 ) of between 0 and 0.45 were recorded on both sites, during both seasons. Although PM performed best during periods of high ET at both sites, results show it was outperformed by other models during low ET times. TsVI and MET were similarly accurate in the dry season in Skukuza, as GLEAM was the most accurate in Elandsberg during the wet season. The conclusion is that none of the models performed well, as shown by low R 2 and high errors in all the models. In essence, our results conclude that further investigation of the PM model is possible to improve its estimation of low ET measurements.

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Monitoring daily evapotranspiration over two California vineyards using Landsat 8 in a multi-sensor data fusion approach

Cited by 225 publications

References 50 publications

Evapotranspiration Estimate over an Almond Orchard Using Landsat Satellite Observations

Evapotranspiration Estimate over an Almond Orchard Using Landsat Satellite Observations

Daily Landsat-scale evapotranspiration estimation over a forested landscape in North Carolina, USA, using multi-satellite data fusion

An Intercomparison of Satellite-Based Daily Evapotranspiration Estimates under Different Eco-Climatic Regions in South Africa

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