Globally Scalable Approach to Estimate Net Ecosystem Exchange Based on Remote Sensing, Meteorological Data, and Direct Measurements of Eddy Covariance Sites
Despite a rapid development of Nature-Based Solutions (NBS) for carbon removal 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 spatiotemporal variation of net...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-11-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/21/5529 |
| _version_ | 1797466602273243136 |
|---|---|
| author | Ruslan Zhuravlev Andrey Dara André Luís Diniz dos Santos Oleg Demidov George Burba |
| author_facet | Ruslan Zhuravlev Andrey Dara André Luís Diniz dos Santos Oleg Demidov George Burba |
| author_sort | Ruslan Zhuravlev |
| collection | DOAJ |
| description | Despite a rapid development of Nature-Based Solutions (NBS) for carbon removal 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 spatiotemporal variation of net ecosystem exchange from eddy-covariance stations. A Lagrangian particle dispersion model was used for upscaling 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 thanks to the use of Landsat imagery; and (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. Stratification that uses separate Fluxnet stations for each fold of validation ensures that the models are accurate across the area covered by the Fluxnet sites. Overall, we present here a 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. |
| first_indexed | 2024-03-09T18:41:06Z |
| format | Article |
| id | doaj.art-7602e4a695ba43aeb316516e73ce4a0e |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T18:41:06Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-7602e4a695ba43aeb316516e73ce4a0e2023-11-24T06:40:26ZengMDPI AGRemote Sensing2072-42922022-11-011421552910.3390/rs14215529Globally Scalable Approach to Estimate Net Ecosystem Exchange Based on Remote Sensing, Meteorological Data, and Direct Measurements of Eddy Covariance SitesRuslan Zhuravlev0Andrey Dara1André Luís Diniz dos Santos2Oleg Demidov3George Burba4Carbonspace Ltd., D04H1F3 Dublin, IrelandCarbonspace Ltd., D04H1F3 Dublin, IrelandCarbonspace Ltd., D04H1F3 Dublin, IrelandCarbonspace Ltd., D04H1F3 Dublin, IrelandLI-COR Biosciences, Daugherty Water for Food Global Institute, University of Nebraska-Lincoln, Lincoln, NE 68588, USADespite a rapid development of Nature-Based Solutions (NBS) for carbon removal 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 spatiotemporal variation of net ecosystem exchange from eddy-covariance stations. A Lagrangian particle dispersion model was used for upscaling 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 thanks to the use of Landsat imagery; and (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. Stratification that uses separate Fluxnet stations for each fold of validation ensures that the models are accurate across the area covered by the Fluxnet sites. Overall, we present here a 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.https://www.mdpi.com/2072-4292/14/21/5529net ecosystem exchangeeddy-covarianceregressionupscalingdata augmentationfeature selection |
| spellingShingle | Ruslan Zhuravlev Andrey Dara André Luís Diniz dos Santos Oleg Demidov George Burba Globally Scalable Approach to Estimate Net Ecosystem Exchange Based on Remote Sensing, Meteorological Data, and Direct Measurements of Eddy Covariance Sites Remote Sensing net ecosystem exchange eddy-covariance regression upscaling data augmentation feature selection |
| title | Globally Scalable Approach to Estimate Net Ecosystem Exchange Based on Remote Sensing, Meteorological Data, and Direct Measurements of Eddy Covariance Sites |
| title_full | Globally Scalable Approach to Estimate Net Ecosystem Exchange Based on Remote Sensing, Meteorological Data, and Direct Measurements of Eddy Covariance Sites |
| title_fullStr | Globally Scalable Approach to Estimate Net Ecosystem Exchange Based on Remote Sensing, Meteorological Data, and Direct Measurements of Eddy Covariance Sites |
| title_full_unstemmed | Globally Scalable Approach to Estimate Net Ecosystem Exchange Based on Remote Sensing, Meteorological Data, and Direct Measurements of Eddy Covariance Sites |
| title_short | Globally Scalable Approach to Estimate Net Ecosystem Exchange Based on Remote Sensing, Meteorological Data, and Direct Measurements of Eddy Covariance Sites |
| title_sort | globally scalable approach to estimate net ecosystem exchange based on remote sensing meteorological data and direct measurements of eddy covariance sites |
| topic | net ecosystem exchange eddy-covariance regression upscaling data augmentation feature selection |
| url | https://www.mdpi.com/2072-4292/14/21/5529 |
| work_keys_str_mv | AT ruslanzhuravlev globallyscalableapproachtoestimatenetecosystemexchangebasedonremotesensingmeteorologicaldataanddirectmeasurementsofeddycovariancesites AT andreydara globallyscalableapproachtoestimatenetecosystemexchangebasedonremotesensingmeteorologicaldataanddirectmeasurementsofeddycovariancesites AT andreluisdinizdossantos globallyscalableapproachtoestimatenetecosystemexchangebasedonremotesensingmeteorologicaldataanddirectmeasurementsofeddycovariancesites AT olegdemidov globallyscalableapproachtoestimatenetecosystemexchangebasedonremotesensingmeteorologicaldataanddirectmeasurementsofeddycovariancesites AT georgeburba globallyscalableapproachtoestimatenetecosystemexchangebasedonremotesensingmeteorologicaldataanddirectmeasurementsofeddycovariancesites |