John R. Mecikalski

Mapping daily evapotranspiration at field to continental scales using geostationary and polar orbiting satellite imagery

Thermal infrared (TIR) remote sensing of landsurface temperature (LST) provides valuable information about the sub-surface moisture status required for estimating evapotranspiration (ET) and detecting the onset and severity of drought. While empirical indices measuring anomalies in LST and vegetation amount (e.g., as quantified by the Normalized Difference Vegetation Index; NDVI) have demonstrated utility in monitoring ET and drought conditions over large areas, they may provide ambiguous results when other factors (e.g., air temperature, advection) are affecting plant functioning. A more physically based interpretation of LST and NDVI and their relationship to subsurface moisture conditions can be obtained with a surface energy balance model driven by TIR remote sensing. The Atmosphere-Land Exchange Inverse (ALEXI) model is a multi-sensor TIR approach to ET mapping, coupling a two-source (soil + canopy) land-surface model with an atmospheric boundary layer model in time-differencing mode to routinely and robustly map daily fluxes at continental scales and 5 to 10-km resolution using thermal band imagery and insolation estimates from geostationary satellites. A related algorithm (DisALEXI) spatially disaggregates ALEXI fluxes down to finer spatial scales using moderate resolution TIR imagery from polar orbiting satellites. An overview of this modeling approach is presented, along with strategies Correspondence to: M. C. Anderson (martha.anderson@ars.usda.gov) for fusing information from multiple satellite platforms and wavebands to map daily ET down to resolutions on the order of 10 m. The ALEXI/DisALEXI model has potential for global applications by integrating data from multiple geostationary meteorological satellite systems, such as the US Geostationary Operational Environmental Satellites, the European Meteosat satellites, the Chinese Fen-yung 2B series, and the Japanese Geostationary Meteorological Satellites. Work is underway to further evaluate multi-scale ALEXI implementations over the US, Europe, Africa and other continents with geostationary satellite coverage.

Evaluation of Drought Indices Based on Thermal Remote Sensing of Evapotranspiration over the Continental United States

AbstractThe reliability of standard meteorological drought indices based on measurements of precipitation is limited by the spatial distribution and quality of currently available rainfall data. Furthermore, they reflect only one component of the surface hydrologic cycle, and they cannot readily capture nonprecipitation-based moisture inputs to the land surface system (e.g., irrigation) that may temper drought impacts or variable rates of water consumption across a landscape. This study assesses the value of a new drought index based on remote sensing of evapotranspiration (ET). The evaporative stress index (ESI) quantifies anomalies in the ratio of actual to potential ET (PET), mapped using thermal band imagery from geostationary satellites. The study investigates the behavior and response time scales of the ESI through a retrospective comparison with the standardized precipitation indices and Palmer drought index suite, and with drought classifications recorded in the U.S. Drought Monitor for the 2000–0...

Estimating fluxes on continental scales using remotely sensed data in an atmospheric-land exchange model

A simple model of energy exchange between the land surface and the atmospheric boundary layer, driven by input that can be derived primarily through remote sensing, is described and applied over continental scales at a horizontal resolution of 10 km. Surface flux partitioning into sensible and latent heating is guided by time changes in land surface brightness temperatures, which can be measured from a geostationary satellite platform such as the Geostationary Operational Environmental Satellite. Other important inputs, including vegetation cover and type, can be derived using the Normalized Difference Vegetation Index in combination with vegetation and land use information. Previous studies have shown that this model performs well on small spatial scales, in comparison with surface flux measurements acquired during several field experiments. However, because the model requires only a modicum of surface-based measurements and is designed to be computationally efficient, it is particularly well suited for regional- or continental-scale applications. The input data assembly process for regional-scale applications is outlined. Model flux estimates for the central United States are compared with climatological moisture and vegetation patterns, as well as with surface-based flux measurements acquired during the Southern Great Plains (SGP-97) Hydrology Experiment. These comparisons are quite promising.

A Multiscale Remote Sensing Model for Disaggregating Regional Fluxes to Micrometeorological Scales

Abstract Disaggregation of regional-scale (103 m) flux estimates to micrometeorological scales (101–102 m) facilitates direct comparison between land surface models and ground-based observations. Inversely, it also provides a means for upscaling flux-tower information into a regional context. The utility of the Atmosphere–Land Exchange Inverse (ALEXI) model and associated disaggregation technique (DisALEXI) in effecting regional to local downscaling is demonstrated in an application to thermal imagery collected with the Geostationary Operational Environmental Satellite (GOES) (5-km resolution) and Landsat (60-m resolution) over the state of Oklahoma on 4 days during 2000–01. A related algorithm (DisTrad) sharpens thermal imagery to resolutions associated with visible–near-infrared bands (30 m on Landsat), extending the range in scales achievable through disaggregation. The accuracy and utility of this combined multiscale modeling system is evaluated quantitatively in comparison with measurements made with...

Effects of Vegetation Clumping on Two–Source Model Estimates of Surface Energy Fluxes from an Agricultural Landscape during SMACEX

Abstract The effects of nonrandom leaf area distributions on surface flux predictions from a two-source thermal remote sensing model are investigated. The modeling framework is applied at local and regional scales over the Soil Moisture–Atmosphere Coupling Experiment (SMACEX) study area in central Iowa, an agricultural landscape that exhibits foliage organization at a variety of levels. Row-scale clumping in area corn- and soybean fields is quantified as a function of view zenith and azimuth angles using ground-based measurements of canopy architecture. The derived clumping indices are used to represent subpixel clumping in Landsat cover estimates at 30-m resolution, which are then aggregated to the 5-km scale of the regional model, reflecting field-to-field variations in vegetation amount. Consideration of vegetation clumping within the thermal model, which affects the relationship between surface temperature and leaf area inputs, significantly improves model estimates of sensible heating at both local a...

Forecasting convective initiation by monitoring the evolution of moving cumulus in daytime GOES imagery

Abstract This study identifies the precursor signals of convective initiation within sequences of 1-km-resolution visible (VIS) and 4–8-km infrared (IR) imagery from the Geostationary Operational Environmental Satellite (GOES) instrument. Convective initiation (CI) is defined for this study as the first detection of Weather Surveillance Radar-1988 Doppler (WSR-88D) reflectivities ≥35 dBZ produced by convective clouds. Results indicate that CI may be forecasted ∼30–45 min in advance through the monitoring of key IR fields for convective clouds. This is made possible by the coincident use of three components of GOES data: 1) a cumulus cloud “mask” at 1-km resolution using VIS and IR data, 2) satellite-derived atmospheric motion vectors (AMVs) for tracking individual cumulus clouds, and 3) IR brightness temperature (TB) and multispectral band-differencing time trends. In effect, these techniques isolate only the cumulus convection in satellite imagery, track moving cumulus convection, and evaluate various IR...

Estimating Land Surface Energy Budgets From Space: Review and Current Efforts at the University of Wisconsin—Madison and USDA–ARS

Abstract Since the advent of the meteorological satellite, a large research effort within the community of earth scientists has been directed at assessing the components of the land surface energy balance from space. The development of these techniques from first efforts to the present time are reviewed, and the integrated system used to estimate the radiative and turbulent land surface fluxes is described. This system is now running in real time over the continental United States at a resolution of 10 km, producing daily and time-integrated flux components.

Retrieval of an Available Water-Based Soil Moisture Proxy from Thermal Infrared Remote Sensing. Part I: Methodology and Validation

Abstract A retrieval of available water fraction ( fAW) is proposed using surface flux estimates from satellite-based thermal infrared (TIR) imagery and the Atmosphere–Land Exchange Inversion (ALEXI) model. Available water serves as a proxy for soil moisture conditions, where fAW can be converted to volumetric soil moisture through two soil texture dependents parameters—field capacity and permanent wilting point. The ability of ALEXI to provide valuable information about the partitioning of the surface energy budget, which can be largely dictated by soil moisture conditions, accommodates the retrieval of an average fAW over the surface to the rooting depth of the active vegetation. For this method, the fraction of actual to potential evapotranspiration ( fPET) is computed from an ALEXI estimate of latent heat flux and potential evapotranspiration (PET). The ALEXI-estimated fPET can be related to fAW in the soil profile. Four unique fPET to fAW relationships are proposed and validated against Oklahoma Meso...

Validation of GOES-Based Insolation Estimates Using Data from the U.S. Climate Reference Network

Reliable procedures that accurately map surface insolation over large domains at high spatial and temporal resolution are a great benefit for making the predictions of potential and actual evapotranspiration that are required by a variety of hydrological and agricultural applications. Here, estimates of hourly and daily integrated insolation at 20-km resolution, based on Geostationary Operational Environmental Satellite (GOES) visible imagery are compared to pyranometer measurements made at 11 sites in the U.S. Climate Reference Network (USCRN) over a continuous 15-month period. Such a comprehensive survey is necessary in order to examine the accuracy of the satellite insolation estimates over a diverse range of seasons and land surface types. The relatively simple physical model of insolation that is tested here yields good results, with seasonally averaged model errors of 62 (19%) and 15 (10% )Wm 2 for hourly and daily-averaged insolation, respectively, including both clear- and cloudy-sky conditions. This level of accuracy is comparable, or superior, to results that have been obtained with more complex models of atmospheric radiative transfer. Model performance can be improved in the future by addressing a small elevation-related bias in the physical model, which is likely the result of inaccurate model precipitable water inputs or cloud-height assessments.

Application of Satellite-Derived Atmospheric Motion Vectors for Estimating Mesoscale Flows

Abstract This study demonstrates methods to obtain high-density, satellite-derived atmospheric motion vectors (AMV) that contain both synoptic-scale and mesoscale flow components associated with and induced by cumuliform clouds through adjustments made to the University of Wisconsin—Madison Cooperative Institute for Meteorological Satellite Studies (UW-CIMSS) AMV processing algorithm. Operational AMV processing is geared toward the identification of synoptic-scale motions in geostrophic balance, which are useful in data assimilation applications. AMVs identified in the vicinity of deep convection are often rejected by quality-control checks used in the production of operational AMV datasets. Few users of these data have considered the use of AMVs with ageostrophic flow components, which often fail checks that assure both spatial coherence between neighboring AMVs and a strong correlation to an NWP-model first-guess wind field. The UW-CIMSS algorithm identifies coherent cloud and water vapor features (i.e....