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Globally Scalable Approach to Estimate Net Ecosystem Exchange Based on Remote Sensing, Meteorological Data, and Direct Measurements of Eddy Covariance Sites
Zhuravlev, R.; Dara, A.; Santos, A.L.D.; Demidov, O.; Burba, G. Globally Scalable Approach to Estimate Net Ecosystem Exchange Based on Remote Sensing, Meteorological Data, and Direct Measurements of Eddy Covariance Sites. Remote Sens.2022, 14, 5529.
Zhuravlev, R.; Dara, A.; Santos, A.L.D.; Demidov, O.; Burba, G. Globally Scalable Approach to Estimate Net Ecosystem Exchange Based on Remote Sensing, Meteorological Data, and Direct Measurements of Eddy Covariance Sites. Remote Sens. 2022, 14, 5529.
Zhuravlev, R.; Dara, A.; Santos, A.L.D.; Demidov, O.; Burba, G. Globally Scalable Approach to Estimate Net Ecosystem Exchange Based on Remote Sensing, Meteorological Data, and Direct Measurements of Eddy Covariance Sites. Remote Sens.2022, 14, 5529.
Zhuravlev, R.; Dara, A.; Santos, A.L.D.; Demidov, O.; Burba, G. Globally Scalable Approach to Estimate Net Ecosystem Exchange Based on Remote Sensing, Meteorological Data, and Direct Measurements of Eddy Covariance Sites. Remote Sens. 2022, 14, 5529.
Abstract
Despite a rapid rise in NBS development in recent years, the methods for evaluating NBS still have certain gaps. We propose an approach based on a combination of remote sensing data and meteorological variables to reconstruct the spatio-temporal variation of net ecosystem exchange from eddy-covariance stations. Lagrangian particle dispersion model was used for upscaling of satellite images and flux towers. We trained data-driven models based on kernel methods separately for each selected land cover class. The results suggest that the proposed approach to quantifying carbon exchange on a medium-to-large scale by blending eddy covariance flux data with moderate resolution satellite and weather data provides a set of key advantages over previously deployed methods: (1) scalability, achieved via the validation design based on a separate set of eddy covariance stations; (2) high spatial and temporal resolution due to use of the Landsat imagery; (3) robust and accurate predictions due to improved data quality control, advanced machine learning techniques, and rigorous validation. The machine learning models yielded high cross-validation results. Overall we present here globally scaled technology for the land sector based on high resolution remote sensing imagery, meteorological variables, and direct carbon flux measurements of eddy covariance flux stations.
Keywords
net ecosystem exchange; eddy-covariance; regression; upscaling; data augmentation; feature selection
Subject
Environmental and Earth Sciences, Environmental Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Received:
22 September 2022
Commenter:
E.V.S. Prakasa Rao
The commenter has declared there is no conflict of interests.
Comment:
Interesting.
Can these results be validated with field data? Can we assign an error estimate? How do we account for such problems as cloud cover for the satellite
Commenter: E.V.S. Prakasa Rao
The commenter has declared there is no conflict of interests.
Can these results be validated with field data? Can we assign an error estimate? How do we account for such problems as cloud cover for the satellite