A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling

W.A. Dorigo, R. Zurita Milla, A.J.W. de Wit, J. Brazile, R. Singh, M.E. Schaepman

Research output: Contribution to journalArticleAcademicpeer-review

336 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)165-193
Number of pages29
JournalInternational Journal of applied Earth Observation and Geoinformation
Volume9
Issue number2
DOIs
Publication statusPublished - 2007

Fingerprint

agricultural ecosystem
data assimilation
Remote sensing
remote sensing
modeling
canopy
Timber
Crops
Environmental impact
Radar
Fusion reactions
Microwaves
Availability
literature review
crop production
timber
Fibers
temporal variation
imagery
environmental impact

Keywords

  • 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

Cite this

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title = "A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling",
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.",
keywords = "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",
author = "W.A. Dorigo and {Zurita Milla}, R. and {de Wit}, A.J.W. and J. Brazile and R. Singh and M.E. Schaepman",
year = "2007",
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language = "English",
volume = "9",
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A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling. / Dorigo, W.A.; Zurita Milla, R.; de Wit, A.J.W.; Brazile, J.; Singh, R.; Schaepman, M.E.

In: International Journal of applied Earth Observation and Geoinformation, Vol. 9, No. 2, 2007, p. 165-193.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling

AU - Dorigo, W.A.

AU - Zurita Milla, R.

AU - de Wit, A.J.W.

AU - Brazile, J.

AU - Singh, R.

AU - Schaepman, M.E.

PY - 2007

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AB - 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.

KW - radiative-transfer models

KW - leaf-area index

KW - hyperspectral vegetation indexes

KW - hydrologic data assimilation

KW - multiple linear-regression

KW - canopy chlorophyll density

KW - ensemble kalman filter

KW - bidirectional reflectance

KW - soil-moisture

KW - crop models

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DO - 10.1016/j.jag.2006.05.003

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EP - 193

JO - International Journal of applied Earth Observation and Geoinformation

JF - International Journal of applied Earth Observation and Geoinformation

SN - 0303-2434

IS - 2

ER -