Dominique Courault

Estimation of Evapotranspiration and Photosynthesis by Assimilation of Remote Sensing Data into SVAT Models

Abstract Estimation of evapotranspiration and photosynthesis from remote sensing data frequently use soil–vegetation–atmosphere transfer models (SVAT models). These models compute energy and mass transfers using descriptions of turbulent, radiative, and water exchanges, as well as a description of stomatal control in relation with water vapor transfers and photosynthesis. Remote sensing data may provide information that is useful for driving SVAT models (e.g., surface temperature, surface soil moisture, canopy structure, solar radiation absorption, or albedo). Forcing or recalibration methods may be employed to combine remote sensing data and SVAT models. In this article a review of SVAT models and remote sensing estimation of energy and mass fluxes is presented. Examples are given based on our work on two different SVAT models. Eventually, some of the difficulties in the combined use of multispectral remote sensing data and SVAT models are discussed.

Munsell soil color and soil reflectance in the visible spectral bands of landsat MSS and TM data

Abstract Color is widely used for soil characterization in the field and for soil classification. Standardized soil color notation is usually achieved by comparison with Munsell color charts. These raw color data are generally not easily related to soil spectral properties. The spectral reflectance curves of 84 soil samples were measured with a spectrophotometer in the laboratory. For each sample, chromaticity coordinates were computed according to CIE standard methods and expressed in RGB (Red, Green, Blue) notation using colorimetric equations. RGB values appear to be strongly correlated with the soil reflectance measured in the corresponding spectral bands of Landsat sensors. RGB coordinates were also estimated from Munsell data using convertion tables. Despite their low precision, the estimated color coordinates are significantly correlated with the reflectance values in the Thematic Mapper visible bands. These results allow the interpretation of published data and further development of soil color remote sensing.

Spatial interpolation of air temperature according to atmospheric circulation patterns in southeast France

A method is proposed for the interpolation of daily maximum and minimum air temperatures (Tx and Tn, respectively) at the regional scale, taking into account the atmospheric circulation patterns (CPs). The study region was in southeast France (150×250 km2). Daily temperatures measured at 152 meteorological stations were available, and CPs from automatic classification performed every day by Meteo France from forecast model outputs were used. For the whole of Europe, ten classes centred on France are defined. A geostatistical approach (ordinary kriging) was chosen, because it permits the mapping of the estimation variance for interpolation and consideration of several days at once. Different data processing methods were compared: raw temperatures without correction; temperatures converted to sea level by applying a constant or varying coefficient according to the CP; and sorting days in relation to CP and season. Cross-validation analyses were performed, separating the data set in two independent parts (62 stations for the model calibration and 90 for the validation). These sets of data showed errors from 0.6° to 2°C. An improvement of 0.5°C was observed for the maximum temperature if it was corrected with regard to the elevation and the days sorted according to CP; however, correction of the elevation had a greater effect on improving the results than does CP sorting. The results obtained for the minimum temperatures fluctuate more from day to day. Copyright

Surface temperature and evapotranspiration: Application of local scale methods to regional scales using satellite data

Abstract Remotely sensed surface temperatures have proven useful for monitoring evapotranspiration (ET) rates and crop water use because of their direct relationship with sensible and latent energy exchange processes. Procedures for using the thermal infrared (IR) obtained with hand-held radiometers deployed at ground level are now well established and even routine for many agricultural research and management purposes. The availability of IR from meteorological satellites at scales from 1 km (NOAA-AVHRR) to 5 km (METEOSAT) permits extension of local, ground-based approaches to larger scale crop monitoring programs. Regional observations of surface minus air temperature (i.e., the stress degree day) and remote estimates of daily ET were derived from satellite data over sites in France, the Sahel, and North Africa and summarized here. Results confirm that similar approaches can be applied at local and regional scales despite differences in pixel size and heterogeneity. This article analyzes methods for obtaining these data and outlines the potential utility of satellite data for operational use at the regional scale.

Spatial interpolation of air temperature using environmental context: Application to a crop model

The air temperature is one of the main input data in models for water balance monitoring or crop models for yield prediction. The different phenological stages of plant growth are generally defined according to cumulated air temperature from the sowing date. When these crop models are used at the regional scale, the meteorological stations providing input climatic data are not spatially dense enough or in a similar environment to reflect the crop local climate. Hence spatial interpolation methods must be used. Climatic data, particularly air temperature, are influenced by local environment. Measurements show that the air above dry surfaces is warmer than above wet areas. We propose a method taking into account the environment of the meteorological stations in order to improve spatial interpolation of air temperature. The aim of this study is to assess the impact of these “corrected climatic data” in crop models. The proposed method is an external drift kriging where the Kriging system is modified to correct local environment effects. The environment of the meteorological stations was characterized using a land use map summarized in a small number of classes considered as a factor influencing local temperature. This method was applied to a region in south-east France (150×250 km) where daily temperatures were measured on 150 weather stations for two years. Environment classes were extracted from the CORINE Landcover map obtained from remote sensing data. Categorical external drift kriging was compared to ordinary kriging by a cross validation study. The gain in precision was assessed for different environment classes and for summer days. We then performed a sensitivity study of air temperature with the crop model STICS. The influence of interpolation corrections on the main outputs as yield or harvest date is discussed. We showed that the method works well for air temperature in summer and can lead to significant correction for yield prediction. For example, we observed by cross validation a bias reduction of 0.5 to 1.0°C (exceptionally 2.5°C for some class), which corresponds to differences in yield prediction from 0.6 to 1.5 t/ha.

Assessing the Potentialities of FORMOSAT-2 Data for Water and Crop Monitoring at Small Regional Scale in South-Eastern France.

Water monitoring at the scale of a small agricultural region is a key point to insure a good crop development particularly in South-Eastern France, where extreme climatic conditions result in long dry periods in spring and summer with very sparse precipitation events, corresponding to a crucial period of crop development. Remote sensing with the increasing imagery resolution is a useful tool to provide information on plant water status over various temporal and spatial scales. The current study focussed on assessing the potentialities of FORMOSAT-2 data, characterized by high spatial (8m pixel) and temporal resolutions (1-3 day/time revisit), to improve crop modeling and spatial estimation of the main land properties. Thirty cloud free images were acquired from March to October 2006 over a small region called Crau-Camargue in SE France, while numerous ground measurements were performed simultaneously over various crop types. We have compared two models simulating energy transfers between soil, vegetation and atmosphere: SEBAL and PBLs. Maps of evapotranspiration were analyzed according to the agricultural practices at field scale. These practices were well identified from FORMOSAT-2 images, which provided accurate input surface parameters to the SVAT models.

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

Irrigated Grassland Monitoring Using a Time Series of TerraSAR-X and COSMO-SkyMed X-Band SAR Data

The objective of this study was to analyze the sensitivity of radar signals in the X-band in irrigated grassland conditions. The backscattered radar signals were analyzed according to soil moisture and vegetation parameters using linear regression models. A time series of radar (TerraSAR-X and COSMO-SkyMed) and optical (SPOT and LANDSAT) images was acquired at a high temporal frequency in 2013 over a small agricultural region in southeastern France. Ground measurements were conducted simultaneously with the satellite data acquisitions during several grassland growing cycles to monitor the evolution of the soil and vegetation characteristics. The comparison between the Normalized Difference Vegetation Index (NDVI) computed from optical images and the in situ Leaf Area Index (LAI) showed a logarithmic relationship with a greater scattering for the dates corresponding to vegetation well developed before the harvest. The correlation between the NDVI and the vegetation parameters (LAI, vegetation height, biomass, and vegetation water content) was high at the beginning of the growth cycle. This correlation became insensitive at a certain threshold corresponding to high vegetation (LAI ~2.5 m2/m2). Results showed that the radar signal depends on variations in soil moisture, with a higher sensitivity to soil moisture for biomass lower than 1 kg/m². HH and HV polarizations had approximately similar sensitivities to soil moisture. The penetration depth of the radar wave in the X-band was high, even for dense and high vegetation; flooded areas were visible in the images with higher detection potential in HH polarization than in HV polarization, even for vegetation heights reaching 1 m. Lower sensitivity was observed at the X-band between the radar signal and the vegetation parameters with very limited potential of the X-band to monitor grassland growth. These results showed that it is possible to track gravity irrigation and soil moisture variations from SAR X-band images acquired at high spatial resolution (an incidence angle near 30°).

Bidirectional reflectance of bare soil surfaces in the visible and near‐infrared range

Abstract A deeper understanding of interactions of electromagnetic radiation with interpreted objects, as well as technological advance, is important for a further improvement of remote sensing methods. It also concerns soils, which like many natural objects, show variation in their brightness due to the direction of irradiating solar energy and the direction along which the reflected energy is detected. On the one hand, the knowledge of the interaction mechanisms, verified by laboratory and field measurements of soil spectral properties, enables us to define optimum source and sensor configurations for practical purposes. On the other hand, it makes possible the conversion of the remote sensing data collected with different illumination and viewing conditions to be standardized, which contributes to improved interpretations. The goal of this paper is to review physical principles of surface interactions with radiation in the visible and near‐infrared range, as well as the measurement of soil bidirectiona...

Surface Temperature Downscaling From Multiresolution Instruments Based on Markov Models

The spatial resolution of thermal infrared (TIR) instruments is often not sufficient for many applications, but this low resolution is counterbalanced by the high temporal resolution (for example the SEVIRI instrument onboard the European Meteosat 8 and 9 presents a spatial resolution of 3 km × 3 km at nadir and a temporal resolution of 15 mn). At kilometric scales, the observed pixel is generally heterogeneous in terms of land cover, and the temperatures of the different components may present large discrepancies. This paper presents a methodology to infer the temperatures of the various land cover/use classes composing a mixed pixel, from a whole pixel measurement. To infer intra-pixel temperature, information on the mixture within each low resolution pixel, e.g., the proportions of the land cover types derived from high spatial resolution imaging, account for a first constraint. However, in the absence of supplementary constraints, the number of unknown variables is greater than the number of measurements, and there is not uniqueness of the solution. Thus, we propose to take advantage of a priori knowledge provided by a land surface model (LSM), and of the temporal and spatial correlation features of the surface temperature. We propose a new downscaling method for estimating sub pixel signal. It applies to TIR data and: the inversion procedure provides as a result, the land surface temperature (LST) temporal series of each land cover/use class (called endmember) constituting the coarse resolution pixel. Three kinds of a priori information have been introduced, namely (1) a first guess subpixel temperature derived from the SEtHyS LSM; (2) a Markov Random Chain model of the surface temperature temporal dependencies from times t to t + 1 ; (3) a Markov Random Field model of the spatial dependencies between endmember temperatures. Then, the “Maximum A Posteriori” estimator provides the most likely endmember temperatures, given (1) the observed coarse resolution temperatures, (2) the composition of the pixels in terms of “land cover/land use,” and (3) the LSM first guess subpixel temperature values, (4) the a priori spatial and temporal Markov models. The performance of this new method has been first evaluated on simulated data (random Gaussian variables with means equal to endmember temperatures simulated using LSM). The method accuracy versus the observation errors and the number of endmembers was analyzed. The algorithm was then run on actual data, namely Meteosat SEVIRI Land Surface products acquired over an agricultural region in southeastern France. The performance evaluation was done by comparing the subpixel LST estimations to the high-resolution temperatures provided by the Terra/ASTER instrument. Due to the huge bias between sensors ( ~ 4 K), an intercalibration preprocessing between SEVIRI and ASTER was done. In this case, the achieved RMSE is lower than 2 K.