Search

Since it’s launch, the ESA’s Soil Moisture and Ocean Sali nity (SMOS) satellite, is delivering new data from its L-Band 1.4Ghz 2D interferometer [1]. The observations from SMOS are used to retrieve soil moisture in the first centimeters and ocean salinity at the su rface of the water. The observations are multi-angular with a 3 days maximum revisit time. The spatial resolution of SMOS data is 40km. In this paper we present on event detection algorithm implemented at CATDS (Centre Aval de Traitement des Donnees SMOS) the CNES level 3 and level 4 SMOS enter. This algorithm is a three stage change detection algorithm. At stage one the possibility/probability of occurrence of the event is evaluated. This is done via spatio-temporal constraints maps. These maps are obtained from the analysis of NSIDC‘s freezing index products over the last century. Climate data from ancillary files are tested will taking into consideration the uncertainty of the data. Some selected retrieved variables are also tested. At stage two a time series analys is is applied. In the current version of the algorithm a direct change detection algorithm is used . The tests make use of availa ble variables of polarization index, retrieved soil moisture Finally at stage three a simple fuzzy logic approach is used to decide if the event occurred. This approaches takes into consideration the separation time of the data. Ascending and descending orbits are taken into consideration. In this study freezing detection is presented over central CONUS. The temporal and angular signature of SMOS will be presented. Comparison is done with the SCAN network Keywords: SMOS, L-Band, soil moisture, calibrati on/validation, event detection, freeze

The Soil Moisture and Ocean Salinity (SMOS) satellite is a 2D interferometer in L-band (1.4 GHz). Over land, it enables to probe the earth surface emissivity related to soil moisture in the first centimeters of the soil, with an aimed accuracy better than 4m 3 .m -3 and an average spatial resolution of 40km. The European Space Agency's (ESA) ground segment provides half-orbit soil moisture products at level 2. The Centre National d'Etudes Spatiales (CNES) in France has developed the CATDS (Centre Aval de Traitement des Donnees SMOS) ground segment in order to produce global maps, known as level 3 and 4 products. Over land, the algorithm is based on the level 2 soil moisture ESA's prototype. The major enhancement of the CATDS concerns the use of multi-orbit retrieval. The level 3 Soil moisture (SM) products are global maps of soil moisture, and other geophysical products (vegetation optical thickness, albedo, soil dielectric constant or surface temperature). For a particular point, the revisit time is between 1 and 3 days, and the entire Earth's surface is covered by SMOS field of view in 3 days. Level 3 SM products are available over different time periods. First, a 1 day global product is generated for each day. Then, 1 day global maps are aggregated in 3-day global products, 10- day and monthly products.

The SMOS (Soil Moisture and Ocean Salinity) satellite provides soil moisture data at about 40 km resolution globally. Validation of SMOS data using in situ measurements is complicated due to the large integrated scale of remote sensing observations. Nevertheless, different approaches can be used to circumvent the direct comparison. One is to upscale ground measurements using aggregation rules. Another is to downscale (or disaggregate) remote sensing data at the representativeness scale of ground measurements. This study combines both approaches to make ground and remote sensing data match at an intermediate spatial scale. On one hand, the local-scale in situ soil moisture data collected during the first AACES (Australian Airborne Calibration/validation Experiments for SMOS) are aggregated to 4 km resolution. On the other hand, a disaggregation methodology of SMOS data based on 1 km resolution MODIS (MODerate resolution Imaging Spectroradiometer) data is implemented at 4 km resolution over the Murrumbidgee catchment, the site of the AACES campaign. Results indicate a correlation coefficient between disaggregated and ground observations of 0.92. The y-intercept of the linear regression between disaggregated and ground observations is very close to 0. However, the slope of that line is 0.44 only. This seems to highlight an issue with either the dielectric constant model or the roughness parameter value currently used in the SMOS retrieval algorithm.

The issue of deriving cross-scale aggregation rules has been heavily investigated during the past two decades. The widely used approach consists of formulating grid-scale surface fluxes using the same equations that govern the patch-scale behavior but whose arguments are the aggregate expressions of those at the patch-scale. Such approach has been used in the past derive area-averaged or effective radiative surface temperature as it might be observed using low spatial resolution satellite data. The problem however is such satellite data exhibits large directional effect and none the past studies have addressed this issue. The present work tackles this issue of the combined effects of surface heterogeneity and view angle variations on surface temperature measurements. The directional effects are modeled on surfaces having a known heterogeneity. Then, the angular properties of local surfaces, assumed homogeneous, are calculated according a multiple scattering model. By applying the principle of aggregation, the equivalent angular radiance of the whole heterogeneous scene is then defined. This made it possible to show that this radiance is particularly sensitive to the directional effects, in particular when the spatial variation of surface temperature is significant and when there is a vegetation component in the heterogeneous land surface. The structure of the vegetation component is also a significant factor of directional effect on equivalent angular radiance.

A reliable estimation of primary production of terrestrial ecosystems is often a prerequisite for carrying out land management, while being important also in ecological and climatological studies. At a regional scale, grassland primary production estimates are increasingly being made using satellite data. In a currently used approach, regional Gross, Net and Above-ground Net Primary Productivity (GPP, NPP and ANPP) are derived from the parametric model of Monteith and are calculated as the product of the fraction of incident photosynthetically active radiation absorbed by the canopy (f APAR ) and gross, net and above-ground net production (radiation-use) efficiencies ((epsilon) g , (epsilon) n , (epsilon) an ); f APAR being derived from indices calculated from satellite measured reflectances in the red and near infrared. The accuracy and realism of the primary production values estimated by this approach therefore largely depend on an accurate estimation of (epsilon) g , (epsilon) n and (epsilon) an . However, data are scarce for production efficiencies of semi-arid grasslands, and their time and spatial variations are poorly documented, leading to often large errors on the estimates. In this paper a modeling approach taking into account relevant ecosystem processes and based on extensive field data, is used to estimate sub- seasonal and inter-annual variations of (epsilon) g , (epsilon) n and (epsilon) an of a shortgrass site of Arizona, and to quantitatively explain these variations by these of plant water stress, temperature, leaf aging, and processes such as respiration and changes in allocation pattern. For example, over the 3 study years, the mean (epsilon) g , (epsilon) n , and (epsilon) an were found to be 1.92, 0.74 and 0.29 g DM (MJ APAR) -1 respectively. (epsilon) g and epsilon n exhibited very important inter- annual and seasonal variations mainly due to different water stress conditions during the growing season. Inter-annual variations of (epsilon) an were much less important, while for periods shorter than a growing season, (epsilon) an exhibits very contrasting values from re-growth to senescence. Therefore the calculation of ANPP based on Monteith's approach seems less prone to errors due to environmental effects when computed on an annual basis, whereas for periods shorter than the growing season the computation of either GPP, NPP or ANPP is delicate.

An algorithm based on the Monte Carlo principle is developed to solve the radiative transfer problem in the reflective domain of the solar spectrum and is used to precisely evaluate ground irradiance on a rugged terrain. This method allows to simulate paths of photons inside the earth- atmosphere system without any assumption and calculate the different identified irradiance components and particularly those coming from environment. To establish the relative contribution of each of these terms, several typical relief and atmosphere configurations are considered. In a first step, two ground types simulations assuming lambertian reflectances are computed. Over vegetation-covered hills in the near IR, in the portion badly exposed to the direct solar beam, the environment irradiance contributes more than 20 percent of the total signal received at ground level. When severe slopes and higher reflectance values are considered, this contribution can exceed 60 percent in shadowed areas. These simulations demonstrate the necessity to take into account the high order terms when the region of interest presents important slopes and/or high reflectance ground. the case of non-lambertian reflectances is also dealt and it is shown that in the present configuration lambertian reflectances can be assumed to calculate the environment terms without significant errors on total irradiance, even in the shadow.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Water stored in the soil serves as the reservoir for the evapotranspiration process, thus the interest in trying to map its spatial and temporal variations in experiments studying the soil- plant-atmosphere interactions at the GCM grid scale. During the 8 week intensive observation period (IOP) of HAPEX-Sahel (Hydrologic Atmospheric Pilot Experiment in the Sahel), this was done with two airborne microwave radiometer systems. The five frequency (5 to 90 GHz) PORTOS radiometer on the French ARAT aircraft and the single frequency (1.42 GHz) multibeam pushbroom microwave radiometer (PBMR) on the NASA C-130 were used. These aircraft measurements were supported by ground based observations at the central sites and, because of several rains during the IOP, covered a good range of soil wetness conditions that existed. The PBMR and the 5.05 GHz PORTOS channel in H polarization show a large dynamic range of TB on each day and between different days in response to variations in rainfall and drying conditions ranging from low TBs of 210 to 220 K for the wettest conditions to values of 280 to 290 K for the driest.