Atmospheric Correction Inter-comparison eXercise.

The Atmospheric Correction Inter-comparison eXercise (ACIX) is an international initiative with the aim to analyse the Surface Reflectance (SR) products of various state-of-the-art atmospheric correction (AC) processors. The Aerosol Optical Thickness (AOT) and Water Vapour (WV) are also examined in ACIX as additional outputs of an AC processing. In this paper, the general ACIX framework is discussed; special mention is made of the motivation to initiate this challenge, the inter-comparison protocol and the principal results. ACIX is free and open and every developer was welcome to participate. Eventually, 12 participants applied their approaches to various Landsat-8 and Sentinel-2 image datasets acquired over sites around the world. The current results diverge depending on the sensors, products and sites, indicating their strengths and weaknesses. Indeed, this first implementation of processor inter-comparison was proven to be a good lesson for the developers to learn the advantages and limitations of their approaches. Various algorithm improvements are expected, if not already implemented, and the enhanced performances are yet to be investigated in future ACIX experiments.

Relative Radiometric Normalization and Atmospheric Correction of a SPOT 5 Time Series.

Multi-temporal images acquired at high spatial and temporal resolution are an important tool for detecting change and analyzing trends, especially in agricultural applications. However, to insure a reliable use of this kind of data, a rigorous radiometric normalization step is required. Normalization can be addressed by performing an atmospheric correction of each image in the time series. The main problem is the difficulty of obtaining an atmospheric characterization at a given acquisition date. In this paper, we investigate whether relative radiometric normalization can substitute for atmospheric correction. We develop an automatic method for relative radiometric normalization based on calculating linear regressions between unnormalized and reference images. Regressions are obtained using the reflectances of automatically selected invariant targets. We compare this method with an atmospheric correction method that uses the 6S model. The performances of both methods are compared using 18 images from of a SPOT 5 time series acquired over Reunion Island. Results obtained for a set of manually selected invariant targets show excellent agreement between the two methods in all spectral bands: values of the coefficient of determination (r²) exceed 0.960, and bias magnitude values are less than 2.65. There is also a strong correlation between normalized NDVI values of sugarcane fields (r² = 0.959). Despite a relative error of 12.66% between values, very comparable NDVI patterns are observed.

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.

PARASOL in-flight calibration and performance.

Since 18 December 2004, the PARASOL satellite is a member of the so-called A-train atmospheric orbital observatory, flying together with Aqua, Aura, CALIPSO, CLOUDSAT, and OCO satellites. These satellites combine for the first time a full suite of instruments for observing aerosols and clouds, using passive radiometer complementarily with active lidar and radar sounders. The PARASOL payload is extensively derived from the instrument developed for the POLDER programs that performs measurements of bidirectionality and polarization for a very wide field-of-view and for a visible/near-infrared spectral range. An overview of the results obtained during the commissioning phase and the reevaluation after one year in orbit is presented. In-flight calibration methods are briefly described, and radiometric and geometric performances are both evaluated. All algorithms are based on a panel of methods using mainly natural targets previously developed for POLDER missions and adapted or redeveloped in the PARASOL context. Regarding performances, all mission requirements are met except for band 443 (not recommended for use). After one year in orbit, a perfect geometrical stability was found while a slight decrease of the radiometric sensitivity was observed and corrected through an innovative multitemporal algorithm based on observations of bright and scattered convective clouds. The scientific exploitation of PARASOL has now begun, particularly by coupling these specific observations with other A-train sensor measurements.