Combining remote sensing data and ecosystem modeling to map rooting depth

S. Sánchez-Ruiz; M. Chiesi; B. Martínez; M. Campos-Taberner; F. J. García-Haro; F. Maselli; M. A. Gilabert

doi:10.1117/12.2532536

21 October 2019 Combining remote sensing data and ecosystem modeling to map rooting depth

S. Sánchez-Ruiz, M. Chiesi, B. Martínez, M. Campos-Taberner, F. J. García-Haro, F. Maselli, M. A. Gilabert

Proceedings Volume 11149, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI; 1114923 (2019) https://doi.org/10.1117/12.2532536
Event: SPIE Remote Sensing, 2019, Strasbourg, France

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.

Citation Download Citation

S. Sánchez-Ruiz, M. Chiesi, B. Martínez, M. Campos-Taberner, F. J. García-Haro, F. Maselli, and M. A. Gilabert "Combining remote sensing data and ecosystem modeling to map rooting depth", Proc. SPIE 11149, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI, 1114923 (21 October 2019); https://doi.org/10.1117/12.2532536

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Ecosystems

Data modeling

Calibration

Remote sensing

Spatial resolution

Vegetation

Databases

Show All Keywords

Keywords/Phrases

Search In:

Publication Years