Share Email Print
cover

Proceedings Paper • new

Combining remote sensing data and ecosystem modeling to map rooting depth
Author(s): S. Sánchez-Ruiz; M. Chiesi; B. Martínez; M. Campos-Taberner; F. J. García-Haro; F. Maselli; M. A. Gilabert
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Biogeochemical ecosystem models describe the energy and mass exchange processes between natural systems and their environment. They normally require a large amount of inputs that present important spatial variations and require a parameterization. Other simpler ecosystem models focused on a single process only need a reduced amount of inputs usually derived from direct measurements and can be combined with the former models to calibrate their parameters. This study combines the biogeochemical model Biome-BGC and a production efficiency model (PEM) optimized for the study area to calibrate a key parameter for the simulation of the ecosystem water balance by Biome-BGC, the rooting depth. Daily gross primary production (GPP) time series for the 2005-2012 period are simulated by both models. First, the optimized PEM is validated against GPP derived from four eddy covariance (EC) towers located at different ecosystems representative of the study area. Next, GPP time series simulated by both models are combined to optimize rooting depth at the four sites: different values of rooting depth are tested and the one that results in the lowest root mean square error (RMSE) between the two GPP series is selected. Explained variance and relative RMSE between Biome- BGC and EC GPP series are respectively augmented between 3 and 14 percentage points (pp) and reduced between 1 and 33pp. Finally the methodology is extrapolated for the whole study area and an original rooting depth map for peninsular Spain, which is coherent with the spatial distribution of vegetation type and GPP in the study area, is obtained at 1-km spatial resolution.

Paper Details

Date Published: 21 October 2019
PDF: 7 pages
Proc. SPIE 11149, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI, 1114923 (21 October 2019); doi: 10.1117/12.2532536
Show Author Affiliations
S. Sánchez-Ruiz, Univ. de València (Spain)
M. Chiesi, Istituto di Biometeorologia, CNR (Italy)
B. Martínez, Univ. de València (Spain)
M. Campos-Taberner, Univ. de València (Spain)
F. J. García-Haro, Univ. de València (Spain)
F. Maselli, Istituto di Biometeorologia, CNR (Italy)
M. A. Gilabert, Univ. de València (Spain)


Published in SPIE Proceedings Vol. 11149:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI
Christopher M. U. Neale; Antonino Maltese, Editor(s)

© SPIE. Terms of Use
Back to Top