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Record number 355675
Title A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling
Author(s) Dorigo, W.A.; Zurita Milla, R.; Wit, A.J.W. de; Brazile, J.; Singh, R.; Schaepman, M.E.
Source International Journal of applied Earth Observation and Geoinformation 9 (2007)2. - ISSN 0303-2434 - p. 165 - 193.
Department(s) Laboratory of Geo-information Science and Remote Sensing
Soil Physics, Ecohydrology and Groundwater Management
Publication type Refereed Article in a scientific journal
Publication year 2007
Keyword(s) radiative-transfer models - leaf-area index - hyperspectral vegetation indexes - hydrologic data assimilation - multiple linear-regression - canopy chlorophyll density - ensemble kalman filter - bidirectional reflectance - soil-moisture - crop models
Abstract During the last 50 years, the management of agroecosystems has been undergoing major changes to meet the growing demand for food, timber, fibre and fuel. As a result of this intensified use, the ecological status of many agroecosystems has been severely deteriorated. Modeling the behavior of agroecosystems is, therefore, of great help since it allows the definition of management strategies that maximize (crop) production while minimizing the environmental impacts. Remote sensing can support such modeling by offering information on the spatial and temporal variation of important canopy state variables which would be very difficult to obtain otherwise. In this paper, we present an overview of different methods that can be used to derive biophysical and biochemical canopy state variables from optical remote sensing data in the VNIR-SWIR regions. The overview is based on an extensive literature review where both statistical¿empirical and physically based methods are discussed. Subsequently, the prevailing techniques of assimilating remote sensing data into agroecosystem models are outlined. The increasing complexity of data assimilation methods and of models describing agroecosystem functioning has significantly increased computational demands. For this reason, we include a short section on the potential of parallel processing to deal with the complex and computationally intensive algorithms described in the preceding sections. The studied literature reveals that many valuable techniques have been developed both for the retrieval of canopy state variables from reflective remote sensing data as for assimilating the retrieved variables in agroecosystem models. However, for agroecosystem modeling and remote sensing data assimilation to be commonly employed on a global operational basis, emphasis will have to be put on bridging the mismatch between data availability and accuracy on one hand, and model and user requirements on the other. This could be achieved by integrating imagery with different spatial, temporal, spectral, and angular resolutions, and the fusion of optical data with data of different origin, such as LIDAR and radar/microwave.
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