Assessment of clear and cloudy sky parameterizations for daily downwelling longwave radiation over different land surfaces in Florida, USA

Clear sky downwelling longwave radiation (Rldc) and cloudy sky downwelling longwave radiation (Rld) formulas were tested across eleven sites in Florida. The Brunt equation, using air vapor pressure and temperature measurements, provides the best Rldc estimates with a root mean square error of less than around 12 Wm−2 across all sites. The Crawford and Duchon\u27s cloudiness factor with Brunt equation is recommended for Rld calculations. This combined approach requires no local calibration and estimates Rld with a root mean square error of less than around 13 Wm−2 and squared correlation coefficients that typically exceed 0.9

Aerodynamic Methods for Estimating Turbulent Fluxes

The exchange of energy and mass between a surface and the lowest region of the troposphere is a complex process that governs many hydrological, agricultural, and atmospheric processes. The layer of air directly affected by surface– atmosphere exchanges is strongly influenced by turbulent processes at the surface–atmosphere boundary and extends upward into the atmosphere to a height of approximately 1 km. This region is commonly referred to as the atmospheric boundary layer (ABL) that is uniquely characterized by turbulence resulting from mechanical (wind shear) and buoyancy (thermal) forces at or near the surface. Methods have been developed to evaluate energy/mass (heat, water vapor, trace gases, and pollutants) exchanges between the ABL and the underlying surface. In this chapter, we describe the flux gradient approach for estimating mass and energy fluxes under the rubric of aerodynamic methods. We provide some historical perspective, present fundamental equations in the context of Monin-Obukhov similarity theory and introduce recent developments of an alternative method to compute heat and water vapor fluxes using turbulence variance statistics

Aerodynamic Methods for Estimating Turbulent Fluxes

The exchange of energy and mass between a surface and the lowest region of the troposphere is a complex process that governs many hydrological, agricultural, and atmospheric processes. The layer of air directly affected by surface– atmosphere exchanges is strongly influenced by turbulent processes at the surface–atmosphere boundary and extends upward into the atmosphere to a height of approximately 1 km. This region is commonly referred to as the atmospheric boundary layer (ABL) that is uniquely characterized by turbulence resulting from mechanical (wind shear) and buoyancy (thermal) forces at or near the surface. Methods have been developed to evaluate energy/mass (heat, water vapor, trace gases, and pollutants) exchanges between the ABL and the underlying surface. In this chapter, we describe the flux gradient approach for estimating mass and energy fluxes under the rubric of aerodynamic methods. We provide some historical perspective, present fundamental equations in the context of Monin-Obukhov similarity theory and introduce recent developments of an alternative method to compute heat and water vapor fluxes using turbulence variance statistics

Estimación de flujos de energía utilizando un modelo micrometeorológico e imágenes de satélite

El creciente interés de las comunidades científicas meteorológicas, climáticas e hidrológicas por los distintos componentes del balance energético de superficie, y especialmente por la evapotranspiración, ha fomentado el desarrollo de distintos modelos micrometeorológicos para evaluar los flujos de energía de superficie a escala local. Los recientes avances en las técnicas de teledetección satelitaria podrían permitir el seguimiento de estos flujos de energía de superficie sobre zonas extensas. Sin embargo, la mayoría de los modelos actuales requieren calibraje in situ o parámetros derivados empíricamente, cosa que limita su aplicación operacional a gran escala. El objetivo de este trabajo es presentar una aproximación micrometeorológica que potencialmente podría ser usada de modo operacional junto con las imágenes de satélite para hacer un seguimiento de los flujos de energía de superficie a escala regional. En primer lugar, introducimos el marco y los detalles del modelo micrometeorológico propuesto, basado en una representación de parcela de dos fuentes del sistema de suelo-vegetación-atmósfera. La viabilidad del modelo se explora a escala local usando datos recogidos de dos ecosistemas completamente distintos. Por un lado, datos recogidos de un cultivo de maíz en Beltsville, Maryland, EEUU, durante la estación de crecimiento del verano del año 2004. Por el otro, datos de una campaña experimental realizada en un bosque boreal de Finlandia en el 2002. La comparación de los resultados con las medidas del suelo muestra un error de entre 15 y 60 W m-2 para la recuperación de la radiación neta, el flujo del calor del suelo, y los flujos de calor sensible y latentes en los dos [email protected]; [email protected]

Diurnal and Directional Responses of Chlorophyll Fluorescence and the PRI in a Cornfield

Determining the health and vigor of vegetation using high spectral resolution remote sensing is an important goal which has application to monitoring agriculture and ecosystem productivity and carbon exchange. Two spectral indices used to assess whether vegetation is performing near-optimally or exhibiting symptoms of environmental stress (e.g., drought or nutrient deficiency, non-optimal temperatures, etc.) are the Photochemical Reflectance Index (PRI) and solar-induced red and far-red Chlorophyll Fluorescence (Fs). Both the PRI and Fs capture the dynamics of photoprotection mechanisms within green foliage: the PRI is based on the association of the reflected radiation in the green spectrum with the xanthophyll cycle, whereas Fs measures the emitted radiation in the red and far-red spectrum. Fs was determined from retrievals in the atmospheric oxygen absorption features centered at 688 and 760 nm using a modified Fraunhofer Line Depth (FLD) method. We previously demonstrated diurnal and seasonal PRI differences for sunlit vs. shaded foliage in a conifer forest canopy, as expressed in the hotspot and darkspot of the Bidirectional Reflectance Function (BRF). In a USDA-ARS experimental field site located in Beltsville, MD, USA, measurements were acquired over a corn crop from a nadir view in 2008 with an ASD FieldSpec Pro (Analytical Spectral Devices, Inc., Boulder, CO, USA) to study the behavior of the PRI for sunlit and shaded foliage as captured in reflectance variations associated with the BRF, in a I m tall canopy in the vegetative growth stage. Those observations were compared to simulations obtained from two radiative transfer models. Measurements were then acquired to examine whether the PRI and Fs were influenced by view zenith and azimuth geometries at different times of day. Those measurements were made in 2010 with the Ocean Optics USB4000 Miniature Fiber Optic Spectrometer (Ocean Optics Inc., Dunedin, Florida, USA) at several times during the day on multiple days throughout the growing season. We found that the PRI consistently had higher values, indicating lower stress, in the BRF darkspot associated with shaded foliage than in the hotspot associated with sunlit foliage. We also found that Fs exhibited differences associated with sunlit and shaded canopy sectors, which were most pronounced for the red/far-red Fs ratio. Values indicated greater physiological stress in afternoons compared to mornings, and in the early senescent canopy as compared to the vegetative growth stage, BRFs for both the PRI and the red/far-red Fs ratio were bowl-shaped for the full azimuth sweep of the canopy. These two spectral indices (PRI, Fs ratio) provided complementary information on the photosynthetic function of the corn canopy

The effect of land-atmosphere feedbacks on the spatial structure of land surface fluxes over heterogeneous terrain

The ability to understand and accurately map land surface fluxes at the spatial resolutions of human activity can support efforts to define the impact of anthropogenic induced land cover changes on hydrological and ecological processes. While remote sensors can map the surface states, the scientific problem arises from an incomplete knowledge of how heterogeneous surface states excite heterogeneity in the states of the lower atmosphere, which feedback on the exchange rates of mass, energy, and momentum across these heterogeneous land surfaces. Through the development and implementation of a framework for merging remotely sensed land surface data into a Large Eddy Simulation (LES) model of the atmospheric boundary layer, a procedure now exists for evaluating the typical ecohydrological modeling assumption of homogeneous atmospheric variables (i.e. decoupled from surface heterogeneity) over a study region. The strength of the feedback effects (or surface-air state coupling), with particular attention to the effect of variability of surface states on atmospheric properties in the surface layer, has been shown in our previous work to depend on both the length scales of the surface features [Albertson et al., 2001] and the magnitude of the contrast in surface states across the features [Kustas and Albertson, 2003]. Ignoring consideration of the feedback effects can lead to erroneous flux estimation since most landscapes are inherently heterogeneous. In this talk we examine new results and present a simple scale-dependent means to account for surface-atmosphere coupling in the estimation of land surface fluxes from remotely sensed data over complex terrain

Modeling Surface Energy Fluxes over a Dehesa (Oak Savanna) Ecosystem Using a Thermal Based Two-Source Energy Balance Model (TSEB) I

Savannas are among the most variable, complex and extensive biomes on Earth, supporting livestock and rural livelihoods. These water-limited ecosystems are highly sensitive to changes in both climatic conditions, and land-use/management practices. The integration of Earth Observation (EO) data into process-based land models enables monitoring ecosystems status, improving its management and conservation. In this paper, the use of the Two-Source Energy Balance (TSEB) model for estimating surface energy fluxes is evaluated over a Mediterranean oak savanna (dehesa). A detailed analysis of TSEB formulation is conducted, evaluating how the vegetation architecture (multiple layers) affects the roughness parameters and wind profile, as well as the reliability of EO data to estimate the ecosystem parameters. The results suggest that the assumption of a constant oak leaf area index is acceptable for the purposes of the study and the use of spectral information to derive vegetation indices is sufficiently accurate, although green fraction index may not reflect phenological conditions during the dry period. Although the hypothesis for a separate wind speed extinction coefficient for each layer is partially addressed, the results show that taking a single oak coefficient is more precise than using bulk system coefficient. The accuracy of energy flux estimations, with an adjusted Priestley–Taylor coefficient (0.9) reflecting the conservative water-use tendencies of this semiarid vegetation and a roughness length formulation which integrates tree structure and the low fractional cover, is considered adequate for monitoring the ecosystem water use (RMSD ~40W m-2)

Utility of thermal sharpening over Texas high plains irrigated agricultural fields

Irrigated crop production in the Texas high plains (THP) is dependent on water extracted from the Ogallala Aquifer, an area suffering from sever water shortage. Water management in this area is therefore highly important. Thermal satellite imagery at high temporal (~daily) and high spatial (~100 m) resolutions could provide important surface boundary conditions for vegetation stress and water use monitoring, mainly through energy balance models such as DisALEXI. At present, however, no satellite platform collects such high spatiotemporal resolution data. The objective of this study is to examine the utility of an image sharpening technique (TsHARP) for retrieving land surface temperature at high spatial resolution (down to 60 m) from moderate spatial resolution (1 km) imagery, which is typically available at higher (~daily) temporal frequency. A simulated sharpening experiment was applied to Landsat 7 imagery collected over the THP in September 2002 to examine its utility over both agricultural and natural vegetation cover. The Landsat thermal image was aggregated to 960 m resolution and then sharpened to its native resolution of 60 m and to various intermediate resolutions. The algorithm did not provide any measurable improvement in estimating high-resolution temperature distributions over natural land cover. In contrast, TsHARP was shown to retrieve high-resolution temperature information with good accuracy over much of the agricultural area within the scene. However, in recently irrigated fields, TsHARP could not reproduce the temperature patterns. Therefore we conclude that TsHARP is not an adequate substitute for 100-m-scale observations afforded by the current Landsat platforms. Should the thermal imager be removed from follow-on Landsat platforms, we will lose valuable capacity to monitor water use at the field scale, particularly in many agricultural regions where the typical field size is ~100 X 100 m. In this scenario, sharpened thermal imagery from instruments like MODIS or VIIRS would be the suboptimal alternative

Utility of thermal sharpening over Texas high plains irrigated agricultural fields

Irrigated crop production in the Texas high plains (THP) is dependent on water extracted from the Ogallala Aquifer, an area suffering from sever water shortage. Water management in this area is therefore highly important. Thermal satellite imagery at high temporal (~daily) and high spatial (~100 m) resolutions could provide important surface boundary conditions for vegetation stress and water use monitoring, mainly through energy balance models such as DisALEXI. At present, however, no satellite platform collects such high spatiotemporal resolution data. The objective of this study is to examine the utility of an image sharpening technique (TsHARP) for retrieving land surface temperature at high spatial resolution (down to 60 m) from moderate spatial resolution (1 km) imagery, which is typically available at higher (~daily) temporal frequency. A simulated sharpening experiment was applied to Landsat 7 imagery collected over the THP in September 2002 to examine its utility over both agricultural and natural vegetation cover. The Landsat thermal image was aggregated to 960 m resolution and then sharpened to its native resolution of 60 m and to various intermediate resolutions. The algorithm did not provide any measurable improvement in estimating high-resolution temperature distributions over natural land cover. In contrast, TsHARP was shown to retrieve high-resolution temperature information with good accuracy over much of the agricultural area within the scene. However, in recently irrigated fields, TsHARP could not reproduce the temperature patterns. Therefore we conclude that TsHARP is not an adequate substitute for 100-m-scale observations afforded by the current Landsat platforms. Should the thermal imager be removed from follow-on Landsat platforms, we will lose valuable capacity to monitor water use at the field scale, particularly in many agricultural regions where the typical field size is ~100 X 100 m. In this scenario, sharpened thermal imagery from instruments like MODIS or VIIRS would be the suboptimal alternative

Utility of thermal image sharpening for monitoring field-scale evapotranspiration over rainfed and irrigated agricultural regions

The utility of a thermal image sharpening algorithm (TsHARP) in providing fine resolution land surface temperature data to a Two-Source-Model for mapping evapotranspiration (ET) was examined over two agricultural regions in the U.S. One site is in a rainfed corn and soybean production region in central Iowa. The other lies within the Texas High Plains, an irrigated agricultural area. It is concluded that in the absence of fine (sub-field scale) resolution thermal data, TsHARP provides an important tool for monitoring ET over rainfed agricultural areas. In contrast, over irrigated regions, TsHARP applied to kilometer-resolution thermal imagery is unable to provide accurate fine resolution land surface temperature due to significant sub-pixel moisture variations that are not captured in the sharpening procedure. Consequently, reliable estimation of ET and crop stress requires thermal imagery acquired at high spatial resolution, resolving the dominant length-scales of moisture variability present within the landscape