Components of near-surface energy balance derived from satellite soundings – Part 2: Noontime latent heat  flux

Mallick, Kaniska; Jarvis, Andrew; Wohlfahrt, Georg; Kiely, Gerard; Hirano, Takashi; Miyata, Akira; Yamamoto, Susumu; Hoffmann, Lucien

doi:10.5194/bg-11-7369-2014

Cited by 5 publications

(4 citation statements)

References 79 publications

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“…BREB‐closure correction was found to fail under hot, dry conditions in some previous studies. This is due to the combination of extremely high evaporative potential and sensible heat entrainment from boundary layer desaturating the surface and causing the surface‐to‐air vapor pressure gradient to reverse (Mallick et al, ; McHugh et al, ; Perez et al, ), a condition that prevails in the arid and semi‐arid ecosystems during most part the year. The assumption of scalar similarity for heat and water vapor is violated in these conditions and g A of heat flux can be two to three times higher than g A of the water vapor flux (Katul et al, ).…”

Section: Discussionmentioning

confidence: 99%

Bridging Thermal Infrared Sensing and Physically‐Based Evapotranspiration Modeling: From Theoretical Implementation to Validation Across an Aridity Gradient in Australian Ecosystems

Mallick

Toivonen

Trebs

et al. 2018

Water Resources Research

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Thermal infrared sensing of evapotranspiration (E) through surface energy balance (SEB) models is challenging due to uncertainties in determining the aerodynamic conductance (g A ) and due to inequalities between radiometric (T R ) and aerodynamic temperatures (T 0 ). We evaluated a novel analytical model, the Surface Temperature Initiated Closure (STIC1.2), that physically integrates T R observations into a combined Penman-Monteith Shuttleworth-Wallace (PM-SW) framework for directly estimating E, and overcoming the uncertainties associated with T 0 and g A determination. An evaluation of STIC1.2 against high temporal frequency SEB flux measurements across an aridity gradient in Australia revealed a systematic error of 10-52% in E from mesic to arid ecosystem, and low systematic error in sensible heat fluxes (H) (12-25%) in all ecosystems. Uncertainty in T R versus moisture availability relationship, stationarity assumption in surface emissivity, and SEB closure corrections in E were predominantly responsible for systematic E errors in arid and semi-arid ecosystems. A discrete correlation (r) of the model errors with observed soil moisture variance (r 5 0.33-0.43), evaporative index (r 5 0.77-0.90), and climatological dryness (r 5 0.60-0.77) explained a strong association between ecohydrological extremes and T R in determining the error structure of STIC1.2 predicted fluxes. Being independent of any leaf-scale biophysical parameterization, the model might be an important value addition in working group (WG2) of the Australian Energy and Water Exchange (OzEWEX) research initiative which focuses on observations to evaluate and compare biophysical models of energy and water cycle components.Plain Language Summary Evapotranspiration modeling and mapping in arid and semi-arid ecosystems are uncertain due to empirical approximation of surface and atmospheric conductances. Here we demonstrate the performance of a fully analytical model which is independent of any leaf-scale empirical parameterization of the conductances and can be potentially used for continental scale mapping of ecosystem water use as well as water stress using thermal remote sensing satellite data.

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Section: Discussionmentioning

confidence: 99%

Bridging Thermal Infrared Sensing and Physically‐Based Evapotranspiration Modeling: From Theoretical Implementation to Validation Across an Aridity Gradient in Australian Ecosystems

Mallick

Toivonen

Trebs

et al. 2018

Water Resources Research

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“…This approach simultaneously captures the critical feedbacks between g A , g S , T 0 , and VPD surrounding the evaporating surface. The estimates of conductances, ET, and associated surface flux components obtained through STIC are independent of any land-surface parameterization [128,129], thus having the potential to overcome some of the major stumbling blocks of the currently available thermal ET algorithms.…”

Section: Physically-based Approachmentioning

confidence: 99%

Challenges and Future Perspectives of Multi-/Hyperspectral Thermal Infrared Remote Sensing for Crop Water-Stress Detection: A Review

et al. 2019

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Thermal infrared (TIR) multi-/hyperspectral and sun-induced fluorescence (SIF) approaches together with classic solar-reflective (visible, near-, and shortwave infrared reflectance (VNIR)/SWIR) hyperspectral remote sensing form the latest state-of-the-art techniques for the detection of crop water stress. Each of these three domains requires dedicated sensor technology currently in place for ground and airborne applications and either have satellite concepts under development (e.g., HySPIRI/SBG (Surface Biology and Geology), Sentinel-8, HiTeSEM in the TIR) or are subject to satellite missions recently launched or scheduled within the next years (i.e., EnMAP and PRISMA (PRecursore IperSpettrale della Missione Applicativa, launched on March 2019) in the VNIR/SWIR, Fluorescence Explorer (FLEX) in the SIF). Identification of plant water stress or drought is of utmost importance to guarantee global water and food supply. Therefore, knowledge of crop water status over large farmland areas bears large potential for optimizing agricultural water use. As plant responses to water stress are numerous and complex, their physiological consequences affect the electromagnetic signal in different spectral domains. This review paper summarizes the importance of water stress-related applications and the plant responses to water stress, followed by a concise review of water-stress detection through remote sensing, focusing on TIR without neglecting the comparison to other spectral domains (i.e., VNIR/SWIR and SIF) and multi-sensor approaches. Current and planned sensors at ground, airborne, and satellite level for the TIR as well as a selection of commonly used indices and approaches for water-stress detection using the main multi-/hyperspectral remote sensing imaging techniques are reviewed. Several important challenges are discussed that occur when using spectral emissivity, temperature-based indices, and physically-based approaches for water-stress detection in the TIR spectral domain. Furthermore, challenges with data processing and the perspectives for future satellite missions in the TIR are critically examined. In conclusion, information from multi-/hyperspectral TIR together with those from VNIR/SWIR and SIF sensors within a multi-sensor approach can provide profound insights to actual plant (water) status and the rationale of physiological and biochemical changes. Synergistic sensor use will open new avenues for scientists to study plant functioning and the response to environmental stress in a wide range of ecosystems.

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“…where T s is the day-night surface temperature change, b 1 = t/c and b 2 = − (t) t/c. If we assume that the system is approximately in equilibrium over a 24 h cycle, and that ≈ 0 at 01:30 LT (for all 30 sites analysed in this study (01:30 LT) < 0.05 (13:30 LT); see also Tamai et al, 1998;Mamadou et al, 2014), then this gives the following simultaneous equations:…”

Section: Surface Heat Capacity Ground Heat Flux and Net Available Enmentioning

confidence: 99%

Components of near-surface energy balance derived from satellite soundings – Part 1: Noontime net available energy

et al. 2015

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This paper introduces a relatively simple method for recovering global fields of monthly midday (13:30 LT) near-surface net available energy (the sum of the sensible and latent heat flux or the difference between the net radiation and surface heat accumulation) using satellite visible and infrared products derived from the AIRS (Atmo-spheric Infrared Sounder) and MODIS (MODerate Resolution Imaging Spectroradiometer) platforms. The method fo-cuses on first specifying net surface radiation by considering its various shortwave and longwave components. This was then used in a surface energy balance equation in conjunction with satellite day-night surface temperature difference to derive 12 h discrete time estimates of surface system heat capacity and heat accumulation, leading directly to retrieval for surface net available energy. Both net radiation and net available energy estimates were evaluated against ground truth data taken from 30 terrestrial tower sites affiliated with the FLUXNET network covering 7 different biome classes. This revealed a relatively good agreement between the satellite and tower data, with a pooled root-mean-square deviation of 98 and 72 W m −2 for monthly 13:30 LT net radiation and net available energy, respectively, although both quantities were underestimated by approximately 25 and 10 %, respectively , relative to the tower observation. Analysis of the individual shortwave and longwave components of the net radiation revealed the downwelling shortwave radiation to be main source of this systematic underestimation.

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Components of near-surface energy balance derived from satellite soundings – Part 2: Noontime latent heat flux

Cited by 5 publications

References 79 publications

Bridging Thermal Infrared Sensing and Physically‐Based Evapotranspiration Modeling: From Theoretical Implementation to Validation Across an Aridity Gradient in Australian Ecosystems

Bridging Thermal Infrared Sensing and Physically‐Based Evapotranspiration Modeling: From Theoretical Implementation to Validation Across an Aridity Gradient in Australian Ecosystems

Challenges and Future Perspectives of Multi-/Hyperspectral Thermal Infrared Remote Sensing for Crop Water-Stress Detection: A Review

Components of near-surface energy balance derived from satellite soundings – Part 1: Noontime net available energy

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