Mapping biophysical variables in relation to vegetation activity: A focus on agricultural landscapes with spatial heterogeneities

A GRSL special stream – 2013
· Marie Weiss, INRA – UMR EMMAH, France (Guest editor)
· Frédéric Jacob, IRD – UMR LISAH, France (GRSL associate editor)

Agricultural landscapes, including crops, rangelands and managed forests, are typical instances for studying global changes. Their evolution is influenced by farmer practices, regional managements and governmental incentives, while these anthropogenic forcing should be adapted in accordance to expected climate change. Prior to the design of simulation tools that support decision-making, it is necessary strengthening biophysical models devoted to vegetation functioning through parameterization, calibration and validation. This requires collecting Earth observation data in relation to the involved processes.

Remote sensing is one of the most efficient means to capture spatial variabilities of land surface characteristics at a given spatial resolution and over a given extent. A large panel of literature materials has been proposed on this topic over the last three decades. Due to technical and methodological limitations, most investigations were conducted under quite homogeneous and flat conditions, using decametric to kilometric spatial resolution sensors. Recent advances, either cognitive (e.g., observing and understanding in difficult conditions), technological (e.g., existing or forthcoming sensors), or methodological (e.g., improvements in modeling and calibration, computation capabilities and stochastic calculus), now permit to address complex canopies and terrains, in relation to processes and scales.

The IEEE Geoscience and Remote Sensing Letters (IEEE GRSL) opened a call for submission of research papers, through a Special Stream devoted to solar and thermal infrared remote sensing of biophysical variables in agricultural landscapes characterized by spatial heterogeneity. Among 23 submissions, eight papers were accepted for publication. A closing paper provides a synthesis of the results with regards to the state of the art. Table 1 summarizes the content of the papers in relation to the studied type of spatial heterogeneity, the used methodology, the spectral domain and the scientific question to be addressed. Table 2 finally provides a listing of the papers, including for each paper the authors, the title and the DOI based web link. The closing paper is in open access.




Paper Spatial Heterogeneity Scale Agrosystem Type of Scene Radiative Transfer Modeling Spectral Domain Variable of Interest Scientific Question
1 Horizontal Field Vineyards 2D Linear aggregation TIR LST Directional effect on mixed pixel
2 Horizontal Landscape Sparse Vegetation 2D Linear aggregation TIR LST Directional effect on mixed pixel
3 Horizontal Field Orchard 2D Linear aggregation Hybrid model Solar Fluorescence Fluorescence of mixed pixel
4 Horizontal Field Orchard 2D Linear aggregation Solar fAPAR Spatial information from high resolution data
5 Horizontal & Vertical Landscape Spruce 3D Ray tracing Solar fAPAR Multiple scattering & spatial resolution
6 Horizontal & Vertical Field Corn, Forest, Orchard 3D Ray tracing LIDAR LIDAR Waveform Multiple scattering & spatial resolution
7 Vertical Field & Landscape Forest 2D Linear aggregation Solar Reflectance Understory impact on signal
8 Horizontal Landscape Grassland Forest 2D Solar GPP Ground sampling strategy




1 J. P. Lagouarde, S. Dayau, P. Moreau, and D. Guyon, “Directional anisotropy of brightness surface temperature over vineyards: Case study over the Medoc region (SW France),” IEEE Geosci. Remote Sens. Lett., vol. 11, no. 2, pp. 574–578.
2 P. C. Guillevic, A. Bork-Unkelbach, F. M. Gottsche, G. Hulley, J.-P. Gastellu-Etchegorry, F. S. Olesen, and J. L. Privette, “Directional viewing effects on satellite land surface temperature products over sparse vegetation canopies—A multisensor analysis,” IEEE Geosci. Remote Sens. Lett., vol. 10, no. 6, pp. 1464–1468.
3 P. J. Zarco-Tejada, L. Suarez, and V. Gonzalez-Dugo, “Spatial resolution effects on chlorophyll fluorescence retrieval in a heterogeneous canopy using hyperspectral imagery and radiative transfer simulation,” IEEE Geosci. Remote Sens. Lett., vol. 10, no. 4, pp. 937–941.
4 M. L. Guillen-Climent, P. J. Zarco-Tejada, and F. J. Villalobos, “Estimating radiation interception in heterogeneous orchards using high spatial resolution airborne imagery,” IEEE Geosci. Remote Sens. Lett., vol. 11, no. 2, pp. 579–583.
5 H. Kobayashi, R. Suzuki, S. Nagai, T. Nakai, and Y. Kim, “Spatial scale and landscape heterogeneity effects on FAPAR in an open-canopy black spruce forest in interior Alaska,” IEEE Geosci. Remote Sens. Lett., vol. 11, no. 2, pp. 564–568.
6 T. Ristorcelli, D. Hamoir, and X. Briottet, “Simulating space lidar wave- forms from smaller-footprint airborne laser scanner data for vegetation observation,” IEEE Geosci. Remote Sens. Lett., vol. 11, no. 2, pp. 534– 538
7 M. Rautiainen and J. Heiskanen, “Seasonal contribution of understory vegetation to the reflectance of a boreal landscape at different spatial scales,” IEEE Geosci. Remote Sens. Lett., vol. 10, no. 4, pp. 923–927.
8 J. Wang, Y. Ge, G. B. M. Heuvelink, and C. Zhou, “Spatial sampling design for estimating regional GPP with spatial heterogeneities,” IEEE Geosci. Remote Sens. Lett., vol. 11, no. 2, pp. 539–543.
Closing paper (open access) F. Jacob, M. Weiss, “Mapping biophysical variables from solar and thermal infrared remote sensing: focus on agricultural landscapes with spatial heterogeneity,” IEEE Geosci. Remote Sens. Lett, vol. 11, no. 10, pp. 1844-1848.


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