Ana C. T. Pinheiro

Modeling the observed angular anisotropy of land surface temperature in a Savanna

Several recent studies have found that retrievals of land surface temperature (LST) from remote sensing measurements depend upon the angle of observation. To understand, predict, and ultimately correct this sensitivity, simple but physically based models of LST angular anisotropy are needed. In this study, we describe and evaluate the modified geometric projection (MGP) model, a highly parameterized model of scene thermal infrared (TIR) radiance applicable to both homogeneous and discontinuous canopy environments. Based on geometric optics modeling, MPG assumes that the angular anisotropy of TIR radiance over discontinuous canopies is due strictly to the different proportions of scene endmembers (e.g., sunlit tree crowns, background shadows) visible to a sensor at different sun-view geometries. We tested MGP against DART, a rigorous three-dimensional radiative transfer model, and against field-measured data from a southern Africa savanna. For a prescribed set of canopy conditions, MGPs estimates of observable endmember fractions and scene temperatures in the solar principal plane compared well with estimates from DART. We also parameterized MGP with field-measured endmember data for an acacia/combretum savanna near Skukuza, South Africa. We angularly integrated the MGP-predicted radiances and compared the results with measurements of scene hemispherical exitance from a tower-based pyrgeometer. The modeled exitances exhibited the normal diurnal behavior. Model predictions generally agreed with the pyrgeometer measurements; however, model accuracy decreased as the difference in endmember temperatures increased. These tests suggest that the assumptions inherent in the MGP model do not seriously impact the accuracy of the simulated radiances. We conclude that the MGP model accurately captures the predominate thermal emission directionality resulting from discontinuous canopy structure, and could therefore be applied at continental and global scales.

Modeling Angular Dependences in Land Surface Temperatures From the SEVIRI Instrument Onboard the Geostationary Meteosat Second Generation Satellites

Satellite-based estimates of land surface temperature (LST) are widely applied as an input to models. A model output is often very sensitive to error in the input data, and high-quality inputs are therefore essential. One of the main sources of errors in LST estimates is the dependence on vegetation structure and viewing and illumination geometry. Despite this, these effects are not considered in current operational LST products from neither polar-orbiting nor geostationary satellites. In this paper, we simulate the angular dependence that can be expected when estimating LST with the viewing geometry of the geostationary Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager sensor across the African continent and compare it to a normalized view geometry. We use the modified geometric projection model that estimates the scene thermal infrared radiance from a surface covered by different land covers. The results show that the sun-target-sensor geometry plays a significant role in the estimated temperature, with variations strictly due to the angular configuration of more than ±3°C in some cases. On the continental scale, the average error is small except in hot-spot conditions, but large variations occur both geographically and temporally. The sun zenith angle, the amount of vegetation, and the vegetation structure are all shown to affect the magnitude of the errors. The findings highlight the need for taking the angular effects into account when applying LST estimates in models and when comparing LST estimates from different sensors or from different times, both on the daily and seasonal scale.

Modeling and Inversion in Thermal Infrared Remote Sensing over Vegetated Land Surfaces

Thermal Infra Red (TIR) Remote sensing allow spatializing various land surface temperatures: ensemble brightness, radiometric and aerodynamic temperatures, soil and vegetation temperatures optionally sunlit and shaded, and canopy temperature profile. These are of interest for monitoring vegetated land surface processes: heat and mass exchanges, soil respiration and vegetation physiological activity. TIR remote sensors collect information according to spectral, directional, temporal and spatial dimensions. Inferring temperatures from measurements relies on developing and inverting modeling tools. Simple radiative transfer equations directly link measurements and variables of interest, and can be analytically inverted. Simulation models allow linking radiative regime to measurements. They require indirect inversions by minimizing differences between simulations and observations, or by calibrating simple equations and inductive learning methods. In both cases, inversion consists of solving an ill posed problem, with several parameters to be constrained from few information. Brightness and radiometric temperatures have been inferred by inverting simulation models and simple radiative transfer equations, designed for atmosphere and land surfaces. Obtained accuracies suggest refining the use of spectral and temporal information, rather than innovative approaches. Forthcoming challenge is recovering more elaborated temperatures. Soil and vegetation components can replace aerodynamic temperature, which retrieval seems almost impossible. They can be inferred using multiangular measurements, via simple radiative transfer equations previously parameterized from simulation models. Retrieving sunlit and shaded components or canopy temperature profile requires inverting simulation models. Then, additional difficulties are the influence of thermal regime, and the limitations of spaceborne observations which have to be along track due to the temperature fluctuations. Finally, forefront investigations focus on adequately using TIR information with various spatial resolutions and temporal samplings, to monitor the considered processes with adequate spatial and temporal scales. 10.1 Introduction Using TIR remote sensing for environmental issues have been investigated the last three decades. This is motivated by the potential of the spatialized information for documenting the considered processes within and between the Earth system components: cryosphere [1–2], atmosphere [3–6], oceans [7–9], and land surfaces [10]. For the latter, TIR remote sensing is used to monitor forested areas [11–14], urban areas [15–17], and vegetated areas. We focus here on vegetated areas, natural and cultivated. The monitored processes are related to climatology, meteorology, hydrology and agronomy: (1) radiation, heat and water transfers at the soil–vegetation–atmosphere interface [18–24]; (2) interactions between land surface and atmospheric boundary layer [25]; (3) vegetation physiological processes such as transpiration and water consumption, photosynthetic activity and CO2 uptake, vegetation growth and

Improved evapotranspiration estimates to aid water management practices in the Rio Grande River Basin

To improve the efficiency of water management and irrigation scheduling in the Rio Grande River basin, the U.S. Bureau of Reclamation helped create the Agricultural Water Resources Decision Support (AWARDS) system. Through its Evapotranspiration (ET) Toolbox interface, the AWARDS system provides guidance to local farmers on when and where to deliver water to the crops. The ET Toolbox is based on water usage estimates (evapotranspiration and open water evaporation) on a grid cell basis (4 kmtimes4 km). Currently, crop water use estimates are determined using a modified-Penman ET approach. To improve upon this parameterization, we use the Community Land Model (CLM2.0) within the LDAS system downscaled to a 1 km grid cell resolution. Our work aims to improve evapotranspiration and soil moisture estimates in the Rio Grande River basin through the improvement of the CLM2.0 parameterization of surface processes. We specifically intend to assimilate up to four land surface temperature (LST) observations per day from the Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) instruments, at 1 km resolution, into the CLM2.0 land surface model. To verify the performance of the assimilation approach we deploy flux towers at two different sites. We focus our ground data collection on riparian and agricultural areas. Ultimately, our results should improve the daily forecasts of vegetation water requirements and, when integrated into operational decision support tools, aid water resource managers in making flood and drought assessments and predictions

Protect and Monitor Water Resources

NASA’s Applied Sciences Program (ASP) has the primary responsibility to accelerate the use of NASA data and science results in applications and to help solve problems important to society and the economy. The primary goal of the ASP Program is to improve future and current operational systems by infusing them with scientific knowledge of the Earth system gained through space-based observation, assimilation of new observations, and development and deployment of enabling technologies, systems, and capabilities. This paper describes the NASA’s Water Management Applications Program and opportunities for the water resources community to participate.