Estimation Of Gravimetric Vegetation Moisture In The Western United States Using A Multi-Sensor Approach

LiDAR; passive microwaves; Sentinel-1; Vegetation moisture; vegetation optical depth; Computer Science Applications; Earth and Planetary Sciences (all)

Abstract :

[en] Vegetation optical depth (VOD) depends on the water, structure, and biomass of vegetation. Here, we propose a multi-sensor approach to isolate the water component from the VOD and to retrieve gravimetric vegetation moisture (mg) in the western United States. The approach estimates VOD from radar and LiDAR data and minimizes the differences between these estimates and SMAP/AMSR2 VOD observations. This minimization allows to obtain the best fitting value of mg with help of a dielectric model. Results are consistent both in space (drier vegetation in arid areas) and time (drier vegetation in drier months). The mg estimates are in the same range than in situ mg data, with some underestimation (bias ~ -0.07 kg/kg). Statistical results are reasonable (r ~ 0.45, RMSE ≤0.10 kg/kg), yet the different spatial and temporal representation of in situ and remote measurements have an impact in the direct comparisons. Our results highlight the potential for developing new vegetation moisture datasets based on VOD decomposition.

Disciplines :

Environmental sciences & ecology
Earth sciences & physical geography

Author, co-author :

Chaparro, David; Microwaves and Radar Institute, German Aerospace Center, Wessling, Germany

Jagdhuber, Thomas; Microwaves and Radar Institute, German Aerospace Center, Wessling, Germany ; University of Augsburg, Institute of Geography, Augsburg, Germany

Piles, Maria; Universitat de València, Image Processing Lab, València, Spain

Jonard, François ; Université de Liège - ULiège > Département de géographie

Vall-Llossera, Merce; Universitat Politècnica de Catalunya (UPC), CommSensLab, Department of Signal Theory and Communications, Institut d'Estudis Espacials de Catalunya, Barcelona, Spain

Camps, Adriano; Universitat Politècnica de Catalunya (UPC), CommSensLab, Department of Signal Theory and Communications, Institut d'Estudis Espacials de Catalunya, Barcelona, Spain ; Uae University, CoE, Aspire Visiting International, Al Ain, United Arab Emirates

Lopez-Martinez, Carlos; Universitat Politècnica de Catalunya (UPC), CommSensLab, Department of Signal Theory and Communications, Institut d'Estudis Espacials de Catalunya, Barcelona, Spain

Fluhrer, Anke; Microwaves and Radar Institute, German Aerospace Center, Wessling, Germany ; University of Augsburg, Institute of Geography, Augsburg, Germany

Fernandez-Moran, Roberto; Universitat de València, Image Processing Lab, València, Spain

Baur, Martin J.; University of Cambridge, Department of Geography, Cambridge, United Kingdom

Feldman, Andrew F.; Nasa Goddard Space Flight Center, Nasa Postdoctoral Program, Biospheric Sciences Laboratory, Greenbelt, United States

Entekhabi, Dara; Massachusetts Institute of Technology, Civil and Environmental Engineering, Cambridge, United States

Language :

English

Title :

Estimation Of Gravimetric Vegetation Moisture In The Western United States Using A Multi-Sensor Approach

Publication date :

16 July 2023

Event name :

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium

Event place :

Pasadena, United States

Event date :

16-07-2023 => 21-07-2023

Audience :

International

Main work title :

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings

Publisher :

Institute of Electrical and Electronics Engineers Inc.

ISBN/EAN :

9798350320107

Peer reviewed :

Peer reviewed

Additional URL :

http://xplorestaging.ieee.org/ielx7/10281394/10281399/10282157.pdf?arnumber=10282157

Funders :

The Institute of Electrical and Electronics Engineers Geoscience and Remote Sensing Society (GRSS)

Funding text :

The work of D. Chaparro was supported by the XXXIII Ramón Areces Postdoctoral Fellowship, and by “la Caixa” Foundation (ID 100010434) under Grant LCF/PR/MIT19/51840001 (MIT-Spain Seed Fund). This research was supported also by the Spanish Ministry of Science and Innovation (MCIN/AEI /10.13039/501100011 033), through the coordinated project INTERACT PID2020-114623RB-C32. M. Piles thanks the support of Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital through the project AI4CS CIPROM/2021/56.

Available on ORBi :

since 22 January 2024

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