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The derivation of leaf area index (LAI) from satellite optical data has been the subject of a large amount of work. In contrast, few papers have addressed the effective model inversion of high resolution satellite images for a complete series of data for the various crop species in a given region. The present study is focused on the assessment of a LAI model inversion approach applied to multitemporal optical data, over an agricultural region having various crop types with different crop calendars. Both the inversion approach and data sources are chosen because of their wide use. Crops in the study region (Barrax, Castilla-La Mancha, Spain) include: cereal, corn, alfalfa, sugar beet, onion, garlic, papaver. Some of the crop types (onion, garlic, papaver) have not been addressed in previous studies. We use in-situ measurement sets and literature values as a priori data in the PROSPECT + SAIL models to produce Look Up Tables (LUTs). Those LUTs are subsequently used to invert Landsat-TM and Landsat-ETM+ image series (12 dates from March to September 2003). The Look Up Tables are adapted to different crop types, identified on the images by ground survey and by Landsat classification. The retrieved LAI values are compared to in-situ measurements available from the campaign conducted in mid July-2003. Very good agreement (a high linear correlation) is obtained for LAI values from 0.1 to 6.0. LAI maps are then produced for each of the 12 dates. The LAI temporal variation shows consistency with the crop phenological stages. The inversion method is favourably compared to a method relying on the empirical relationship between LAI and NDVI from Landsat data. This offers perspectives for future optical satellite data that will ensure high resolution and high temporal frequency.

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Soil moisture and ocean salinity at surface level can be measured by passive microwave remote sensing at L‐band. To provide global coverage data of soil moisture and ocean salinity with three‐day revisit time, the Earth Explorer Opportunity Mission SMOS (Soil Moisture and Ocean Salinity) was selected by ESA (European Space Agency) in May 1999. SMOS' single payload is a Y‐shaped 2‐D aperture synthesis interferometric radiometer called MIRAS (Microwave Imaging Radiometer by Aperture Synthesis). SMOS presents some particular imaging peculiarities: variation of incidence and azimuth angles, different radiometric sensitivity and accuracy at each direction (pixels), and geometric polarization mixing. Therefore, the accuracy of the geophysical parameter retrieval depends on the knowledge of the angular dependence of the emissivity over a wide range of incidence and azimuth angles. The accuracy of the sea surface salinity retrievals depends on our capability to correct the wind‐induced variation of the brightness temperatures. To better understand wind effects, ESA sponsored the WInd and Salinity Experiment 2000 (WISE‐2000) from November 15, 2000, to January 16, 2001, in the Casablanca oil rig, at 40 km off the coast of Tarragona (Spain). This paper is divided into two parts. First, it presents the derived sensitivities of the brightness temperatures at vertical and horizontal polarizations with wind speed, and compares to Hollinger's measurements and numerical simulations. Second, these results are applied to the SMOS sea surface salinity (SSS) retrieval problem for different tracks within the swath. It is shown that, except for low SSS and sea surface temperature (SST), the retrieved SSS has a RMS error of approximately 1 psu in one satellite pass.

Ground measurements of thermal infrared emissivities of terrestrial surfaces are required to derive accurate temperatures from radiometric measurements, and also to apply and validate emissivity models using satellite sensor observations. This paper focuses on the demanding aspects that are involved in the field measurement of emissivity using the box method and a hand-held radiometer. Measuring emissivities in field conditions can be hampered by external factors such as wind and solar irradiance. This can increase the time spent on the field campaign but, most importantly, it can cause no-sense fluctuations between consecutive observations. Here we propose original developments for the experimental instrumentation to ensure consistency of measurements. Moreover, we present a dataset of emissivity values for different soils, rocks and vegetation samples measured in the 8-14, 8.2-9.2, 10.5-1 1.5 and 11.5-12.5 pm wavebands.