Remotely Sensed Land Surface Processes

Dear Colleagues,

Land surface processes are complex processes that occur at the interface between the land surface and atmosphere. These processes include energy and mass exchange between the land surface and the atmosphere, which determines global and local climate at different scales, e.g., urban surface energy balance determines the urban climate, or glaciers/surface energy balance determines changes in glacier mass and the water balance at a regional scale. With the rapid development of remote sensing technology, different kinds of remote sensing data are available, e.g., different spatial and time resolutions and different spectral sampling and coverages. Since the early 1980s, these data have provided an opportunity to observe and model land surface processes, e.g., the parameterization for one-source surface energy balance based on remote sensing SEBAL (Bastiaanssen et al., 1998) and SEBS (Su 2002), two-source energy balance models for vegetation–soil systems, based on multi-angular remote sensing data (Norman 1995) and urban surface heat flux monitoring based on remote sensing data (Weng et al., 2014). No systematic exploration has been attempted, however, of the specific advantages and synergies of the latest generation of Earth Observation systems.

In the 1980s and 1990s, the land surface processes community organized and carried out major experiments on land surface processes, e.g., the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) centered on a 15 x 15 km test site near Manhattan, Kansas (Sellers et al., 1992), the long term field campaign experiment for soil moisture and land surface processes remote sensing (SMOSRES—Surface Monitoring Of the Soil Reservoir Experiment) since 2001, in Mauzac, near Toulouse, France (Rosnay et al., 2006), the integrated remote sensing experiment on hydrological and ecological processes in the Heihe River Basin (Li et al., 2013), and the URBANFLUXES project in Greece from 2015 (Chrysoulakis et al., 2016). Some of these experimental sites have evolved into permanent research sites and many new sites have come into operation, but a new synthesis of these efforts by a wide and growing community would be very timely and relevant.

The assimilation of remote sensing data into different numerical land surface process models shows that remote sensing can provide useful information for numerical modelling and reduce biases. New schemes on the surface energy balance, adapted to use remote sensing data, have also been developed and the applications of remote sensing in land surface processes has grown rapidly in different research fields dealing with, e.g., the cryosphere, forests, agriculture, and urban areas.

This Special Issue invites contributions describing applications of new remote sensing technology to observe and model land surface processes. In particular, but not exclusively, manuscripts are encouraged addressing the following topics:

• Land surface temperature retrieval and surface flux parameterization based on remote sensing of complex heterogeneous surfaces, e.g., urban areas and 3D vegetation canopies
• LiDAR to map land surface properties, such as aerodynamic roughness
• Remote sensing of the terrestrial carbon cycle in different biomes
• Remote sensing of glacier change and high elevation water cycles
• Assimilation of remote sensing data in numerical models of land surface process

References:

Bastiaanssen, W. G., Menenti, M., Feddes, R. A., & Holtslag, A. A. M. (1998). A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. Journal of hydrology, 212, 198-212.

Su, Z. (2002). The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes. Hydrology and earth system sciences, 6(1), 85-100.

Sellers, P. J., Hall, F. G., Asrar, G., Strebel, D. E., & Murphy, R. E. (1992). An overview of the first international satellite land surface climatology project (ISLSCP) field experiment (FIFE). Journal of Geophysical Research: Atmospheres, 97(D17), 18345-18371.

Norman, J. M., Kustas, W. P., & Humes, K. S. (1995). Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature. Agricultural and Forest Meteorology, 77(3-4), 263-293.

Weng, Q., Hu, X., Quattrocchi, D. A., & Liu, H. (2014). Assessing intra-urban surface energy fluxes using remotely sensed ASTER imagery and routine meteorological data: A case study in Indianapolis, USA. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(10), 4046-4057.

Sellers, P. J., Hall, F. G., Asrar, G., Strebel, D. E., & Murphy, R. E. (1992). An overview of the first international satellite land surface climatology project (ISLSCP) field experiment (FIFE). Journal of Geophysical Research: Atmospheres, 97(D17), 18345-18371.

Li, X., Cheng, G., Liu, S., Xiao, Q., Ma, M., Jin, R., Che, T., Liu, Q., Wang, W., Qi, Y. and Wen, J., 2013. Heihe watershed allied telemetry experimental research (HiWATER): Scientific objectives and experimental design. Bulletin of the American Meteorological Society, 94(8), pp.1145-1160.

De Rosnay, P., Calvet, J.C., Kerr, Y., Wigneron, J.P., Lemaître, F., Escorihuela, M.J., Sabater, J.M., Saleh, K., Barrié, J., Bouhours, G. and Coret, L., 2006. SMOSREX: A long term field campaign experiment for soil moisture and land surface processes remote sensing. Remote Sensing of Environment, 102(3-4), pp.377-389.

Chrysoulakis, N., Heldens, W., Gastellu-Etchegorry, J.P., Grimmond, S., Feigenwinter, C., Lindberg, F., Del Frate, F., Klostermann, J., Mitraka, Z., Esch, T. and Albitar, A., 2016, April. Anthropogenic heat flux estimation from space: first results. In EGU General Assembly Conference Abstracts (Vol. 18, p. 11900).

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