Catherine Ottlé

Surface Urban Heat Island Across 419 Global Big Cities

Urban heat island is among the most evident aspects of human impacts on the earth system. Here we assess the diurnal and seasonal variation of surface urban heat island intensity (SUHII) defined as the surface temperature difference between urban area and suburban area measured from the MODIS. Differences in SUHII are analyzed across 419 global big cities, and we assess several potential biophysical and socio-economic driving factors. Across the big cities, we show that the average annual daytime SUHII (1.5 ± 1.2 °C) is higher than the annual nighttime SUHII (1.1 ± 0.5 °C) (P < 0.001). But no correlation is found between daytime and nighttime SUHII across big cities (P = 0.84), suggesting different driving mechanisms between day and night. The distribution of nighttime SUHII correlates positively with the difference in albedo and nighttime light between urban area and suburban area, while the distribution of daytime SUHII correlates negatively across cities with the difference of vegetation cover and activity between urban and suburban areas. Our results emphasize the key role of vegetation feedbacks in attenuating SUHII of big cities during the day, in particular during the growing season, further highlighting that increasing urban vegetation cover could be one effective way to mitigate the urban heat island effect.

Thermal remote sensing of land surface temperature from satellites: Current status and future prospects

Abstract In this paper we review the current status for deriving land surface temperatures (LSTs) by remote sensing from satellites in the thermal infrared. Because of its widespread use and global applicability, we concentrate on the Advanced Very High Resolution Radiometer (AVHRR). The theoretical framework and methodologies used to derive LSTs are reviewed and amplified. Practical algorithms are described and their accuracy and application critically evaluated through sensitivity studies and by inter‐comparison. The important effects of the atmosphere, surface emissivity and instrument noise are considered and the current practice for removing these effects is specified. The accuracy currently attainable from the AVHRR for the LST algorithms studied lies between 1 and 2 K, depending critically upon the surface characteristics and the atmospheric structure. Suggestions about what improvements could be made to reduce the errors in LST estimation from space and the directions of future research are summar...

The AMMA Land Surface Model Intercomparison Project (ALMIP)

The rainfall over West Africa has been characterized by extreme variability in the last half-century, with prolonged droughts resulting in humanitarian crises. There is, therefore, an urgent need to better understand and predict the West African monsoon (WAM), because social stability in this region depends to a large degree on water resources. The economies are primarily agrarian, and there are issues related to food security and health. In particular, there is a need to better understand land–atmosphere and hydrological processes over West Africa because of their potential feedbacks with the WAM. This is being addressed through a multiscale modeling approach using an ensemble of land surface models that rely on dedicated satellite-based forcing and land surface parameter products, and data from the African Multidisciplinary Monsoon Analysis (AMMA) observational field campaigns. The AMMA land surface model (LSM) Intercomparison Project (ALMIP) offline, multimodel simulations comprise the equivalent of a multimodel reanalysis product. They currently represent the best estimate of the land surface processes over West Africa from 2004 to 2007. An overview of model intercomparison and evaluation is presented. The far-reaching goal of this effort is to obtain better understanding and prediction of the WAM and the feedbacks with the surface. This can be used to improve water management and agricultural practices over this region.

Assimilation of soil moisture inferred from infrared remote sensing in a hydrological model over the HAPEX-MOBILHY region

Abstract Hydrological models are generally unable to simulate correctly water exchanges at the soil-atmosphere interface and the time evolution of surface soil humidity. This drawback often leads to poor simulation of water flows after periods of low flows because of the wrong estimation of the surface soil water content. In this paper, we describe how remote sensing can be used to account for the vegetation in the estimation of the actual evapotranspiration, and to estimate soil moisture regularly throughout the year and use it to correct the model simulation. This work has been done on the Adour river basin, in the framework of the HAPEX-MOBILHY experiment. The results presented show the improvements that result from the use of remote sensing data in hydrological modelling: better simulation of the soil moisture and of water flows at the outlets, and more realistic calculation of evaporation.

Analytical parameterization of canopy directional emissivity and directional radiance in the thermal infrared. Application on the retrieval of soil and foliage temperatures using two directional measurements

Abstract This work is aimed at deriving canopy component (soil and foliage) temperatures from remote sensing measurements. A simulation study above sparse, partial and dense vegetation canopies has been performed to improve the knowledge of the behaviour of the composite radiative temperature and emissivity. Canopy structural parameters have been introduced in the analytical parameterization of the directional canopy emissivity and directional canopy radiance:namely, the leaf area index (LAI), directional gap fraction and angular cavity effect coefficient. The parameterization has been physically defined allowing its extension to a wide range of Leaf Inclination Distribution Functions (LIDF). When single values are used as leaves and soil temperatures, they prove to be retrieved with insignificant errors from two directional measurements of the canopy radiance (namely at 0 and 55 from nadir), provided that the canopy structure parameters are known. A sensitivity study to the different parameters shows the...

Estimation of land surface temperature with NOAA9 data

Abstract Different methods for estimating land surface temperature for NOAA satellites data are presented. All these methods use the infrared channels of the Advanced Very High Resolution Radiometer (AVHRR). The single channel method using radiosounding data and a radiative transfer model is compared to the split window method and to other methods using additional High Resolution Infrared Radiation Sounder (HIRS) data. The results show the superiority of the split window method when the spectral variation of the surface emissivity between the two channels 4 and 5 of the AVHRR is negligible.

Simulation of the water budget and the river flows of the Rhone basin

The paper describes the implementation of a macroscale hydrological model in the Rhone river basin. The hydrological model is coupled with a soil -vegetation - atmosphere transfer scheme in order to resolve the daily cycle of the surface energy balance and the water budget. The water surface routing and the water table evolution are computed in the hydrological model with a daily time step. First, the important database collected in the Rhone basin on soil, vegetation, hydrological regimes, and atmospheric variables is briefly described. The coupled model is forced by observed atmospheric quantities during 1 year. The simulation results are discussed with respect to stream flows, soil water content, runoff, and surface fluxes. The simulation clearly shows the importance of topography and snow on the hydrological regime of the Rhone and its tributaries. The simulated spatial variability of evaporation and total runoff are very large within the basin. Small annual evaporation and large runoff are found in the Alps because of the snow processes. On the other hand, the areas experiencing Mediterranean climate conditions (large annual global radiation, low precipitation) are characterized by negligible annual runoff. The simulation is used as a reference to test aggregation methods accounting for the subgrid variability of surface processes within a large area (128 km by 128 km). It is shown that the aggregated surface fluxes, drainage and runoff can be computed with an error lower than 5%, provided that the subgrid variability of precipitation, runoff, and vegetation is taken into account. If these subgrid processes are not aggregated, the errors in the simulation of the various terms of the water balance may exceed the annual reference by 20%.

Effect of atmospheric absorption and surface emissivity on the determination of land surface temperature from infrared satellite data

Abstract Infrared satellite data can be used to determine land surface temperatures but they have to be corrected from atmospheric absorption and soil emissivity. To correct for atmospheric absorption, we present a method that involves the LOWTRAN-6 radiative transfer model and Chedin and Scotts Improved Initialization Inversion procedure to retrieve atmospheric profiles of temperature and moisture. To correct for soil emissivity, no method is proposed, but the emissivity effect is thoroughly investigated. We show, by comparing surface temperatures derived in the two NOAA-9 Advanced Very High Resolution Radiometer thermal infrared channels, that the soil emissivity presents definite spectral variations. Consequently, the split-window techniques currently applied to retrieve surface temperature over the ocean cannot be readily applied over land.

Surface soil moisture estimation from the synergistic use of the (multi-incidence and multi-resolution) active microwave ERS Wind Scatterometer and SAR data

This paper presents an original methodology to retrieve surface (<5 cm) soil moisture over low vegetated regions using the two active microwave instruments of ERS satellites. The developed algorithm takes advantage of the multi-angular configuration and high temporal resolution of the Wind Scatterometer (WSC) combined with the SAR high spatial resolution. As a result, a mixed target model is proposed. The WSC backscattered signal may be represented as a combination of the vegetation and bare soil contributions weighted by their respective fractional covers. Over our temperate regions and time periods of interest, the vegetation signal is assumed to be principally due to forests backscattered signal. Then, thanks to the high spatial resolution of the SAR instrument, the forest contribution may be quantified from the analysis of the SAR image, and then removed from the total WSC signal in order to estimate the soil contribution. Finally, the Integral Equation Model (IEM, [IEEE Transactions on Geoscience and Remote Sensing, 30 (2), (1992) 356]) is used to estimate the effect of surface roughness and to retrieve surface soil moisture from the WSC multi-angular measurements. This methodology has been developed and applied on ERS data acquired over three different Seine river watersheds in France, and for a 3-year time period. The soil moisture estimations are compared with in situ ground measurements. High correlations (R 2 greater than 0.8) are observed for the three study watersheds with a root mean square

Sequential Assimilation of ERS-1 SAR Data into a Coupled Land Surface-Hydrological Model Using an Extended Kalman Filter

Abstract A first attempt to sequentially assimilate European Space Agency (ESA) Remote Sensing Satellite (ERS) synthetic aperture radar (SAR) estimations of surface soil moisture in the production scheme of a lumped rainfall–runoff model has been conducted. The methodology developed is based on the use of an extended Kalman filter to assimilate the SAR retrievals in a land surface scheme (a two-layer hydrological model). This study was performed in the Orgeval agricultural river basin (104 km2), a subcatchment of the Marne River, 70 km east of Paris, France. Assimilation was tested over a 2-yr period (1996 and 1997), corresponding to 25 SAR measurements. The improvements observed in simulating flood events demonstrate the potential of sequential assimilation techniques for monitoring surface functioning models with remote sensing data. It was demonstrated that the method could correct for some errors or uncertainties in the input data (precipitation and evapotranspiration), provided that these errors are ...