Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors

The ability to accurately and rapidly acquire leaf area index (LAI) is an indispensable component of process-based ecological research facilitating the understanding of gas-vegetation exchange phenomenon at an array of spatial scales from the leaf to the landscape. However, LAI is difficult to directly acquire for large spatial extents due to its time consuming and work intensive nature. Such efforts have been significantly improved by the emergence of optical and active remote sensing techniques. This paper reviews the definitions and theories of LAI measurement with respect to direct and indirect methods. Then, the methodologies for LAI retrieval with regard to the characteristics of a range of remotely sensed datasets are discussed. Remote sensing indirect methods are subdivided into two categories of passive and active remote sensing, which are further categorized as terrestrial, aerial and satellite-born platforms. Due to a wide variety in spatial resolution of remotely sensed data and the requirements of ecological modeling, the scaling issue of LAI is discussed and special consideration is given to extrapolation of measurement to landscape and regional levels

A method for the atmospheric correction of ENVISAT/MERIS data over land targets

An atmospheric correction algorithm for the retrieval of land surface reflectance from imagery acquired by the Medium Resolution Imaging Spectrometer (MERIS) on-board the European Space Agency (ESA) ENVIronmental SATellite (ENVISAT) platform has been implemented. The algorithm is designed to estimate the main atmospheric parameters needed in the correction, aerosol and water vapour contents, from the image itself, leading to an optimal characterization of the atmospheric state at the time of image acquisition. Once the atmospheric state has been defined, a second step deals with the retrieval of surface reflectance, accounting for the contribution of surface elevation and roughness as well as the atmospheric adjacency effect. The first part of this paper is devoted to the description of the method, outlining the main steps in the atmospheric characterization and in the subsequent surface reflectance retrieval. The validation task is detailed in the second part. Aerosol Optical Thickness (AOT) and water vapour content from different sites of the AErosol RObotic NETwork (AERONET) have been compared with the method's retrievals. Root Mean Square Errors (RMSEs) equal to 0.085, 0.065 and 0.048 are found for AOT at 440, 550 and 870 nm, respectively. Comparison with in situ measurements shows a satisfactory performance except for episodes of Saharan dust intrusions. For water vapour, a high correlation between MERIS and AERONET results is found, although the RMSE equals 0.316 g cm-2 due to a systematic overestimation when compared with AERONET data. It decreases to 0.098 when the retrievals are compared with the ESA water vapour level 2 product

Seasonal variations of Leaf Area Index of agricultural fields retrieved from Landsat data

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

One the wet tropospheric correction for coastal and inland altimetry

L'objectif de la thèse est de développer des méthodes de cartographie et de suivi des cultures basées sur des données de télédétection, radar et optique. Les résultats pourront être combinés avec d'autres techniques, notamment avec des modèles de croissance des cultures, pour améliorer la prévision des récoltes. Quatre instruments différents, 3 sur satellite (LANDSAT-TM, ENVISAT-MERIS, ENVISAT-ASAR) et 1 aéroporté (AIRSAR) sont utilisés dans trois régions d'étude en Europe (Barrax, Toulouse et Flevoland). Les travaux sont présentés en deux parties, optique et radar. Dans la première partie, les données LANDSAT sont utilisées pour l'inversion du LAI à Barrax (Castilla-La Mancha) à l'aide du modèle de transfert radiatif PROSPECT+SAIL. Les résultats sont validés avec des mesures expérimentales acquises au cours de la campagne sur le terrain ESA SPARC-2003, montrant une bonne corrélation. Une méthode est ensuite proposée pour inverser le LAI et la chlorophylle à partir de données MERIS. La méthode implique une inversion du modèle, PROSPECT+SAIL avec une contrainte temporelle (une courbe pour l'ensemble du cycle de culture est inversée). Les résultats montrent que cette méthode fonctionne mieux que les inversions date par date. Toutefois, l'inversion de la chlorophylle nécessite encore une étude plus approfondie. Dans la partie radar, une méthode de classification basée sur les connaissances des mécanismes de rétrodifusion est proposée. Elle utilise des données polarimétriques en bande C de l'instrument AIRSAR. La méthode est appliquée à des images dans le Flevoland (Pays-Bas). Les résultats indiquent que ces méthodes peuvent être plus robustes que les méthodes statistiques usuelles...TOULOUSE3-BU Sciences (315552104) / SudocSudocFranceF