Upscaling dryland carbon and water fluxes with artificial neural networks of optical, thermal, and microwave satellite remote sensing
<p>Earth's drylands are home to more than two billion people, provide key ecosystem services, and exert a large influence on the trends and variability in Earth's carbon cycle. However, modeling dryland carbon and water fluxes with remote sensing suffers from unique challenges not ty...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2023-01-01
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| Series: | Biogeosciences |
| Online Access: | https://bg.copernicus.org/articles/20/383/2023/bg-20-383-2023.pdf |
| _version_ | 1797943600588259328 |
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| author | M. P. Dannenberg M. L. Barnes W. K. Smith M. R. Johnston S. K. Meerdink X. Wang X. Wang R. L. Scott J. A. Biederman |
| author_facet | M. P. Dannenberg M. L. Barnes W. K. Smith M. R. Johnston S. K. Meerdink X. Wang X. Wang R. L. Scott J. A. Biederman |
| author_sort | M. P. Dannenberg |
| collection | DOAJ |
| description | <p>Earth's drylands are home to more than two billion people, provide key ecosystem services, and exert a large influence on the trends and variability
in Earth's carbon cycle. However, modeling dryland carbon and water fluxes with remote sensing suffers from unique challenges not typically
encountered in mesic systems, particularly in capturing soil moisture stress. Here, we develop and evaluate an approach for the joint modeling of
dryland gross primary production (GPP), net ecosystem exchange (NEE), and evapotranspiration (ET) in the western United States (US) using a suite of
AmeriFlux eddy covariance sites spanning major functional types and aridity regimes. We use artificial neural networks (ANNs) to predict dryland
ecosystem fluxes by fusing optical vegetation indices, multitemporal thermal observations, and microwave soil moisture and temperature retrievals from
the Soil Moisture Active Passive (SMAP) sensor. Our new dryland ANN (DrylANNd) carbon and water flux model explains more than 70 % of monthly
variance in GPP and ET, improving upon existing MODIS GPP and ET estimates at most dryland eddy covariance sites. DrylANNd predictions of NEE were
considerably worse than its predictions of GPP and ET likely because soil and plant respiratory processes are largely invisible to satellite
sensors. Optical vegetation indices, particularly the normalized difference vegetation index (NDVI) and near-infrared reflectance of vegetation
(NIR<span class="inline-formula"><sub>v</sub></span>), were generally the most important variables contributing to model skill. However, daytime and nighttime land surface temperatures
and SMAP soil moisture and soil temperature also contributed to model skill, with SMAP especially improving model predictions of shrubland,
grassland, and savanna fluxes and land surface temperatures improving predictions in evergreen needleleaf forests. Our results show that a
combination of optical vegetation indices and thermal infrared and microwave observations can substantially improve estimates of carbon and water
fluxes in drylands, potentially providing the means to better monitor vegetation function and ecosystem services in these important regions that are
undergoing rapid hydroclimatic change.</p> |
| first_indexed | 2024-04-10T20:26:45Z |
| format | Article |
| id | doaj.art-c07b43c2423b4548bc7d25576e79db2c |
| institution | Directory Open Access Journal |
| issn | 1726-4170 1726-4189 |
| language | English |
| last_indexed | 2024-04-10T20:26:45Z |
| publishDate | 2023-01-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Biogeosciences |
| spelling | doaj.art-c07b43c2423b4548bc7d25576e79db2c2023-01-25T10:14:14ZengCopernicus PublicationsBiogeosciences1726-41701726-41892023-01-012038340410.5194/bg-20-383-2023Upscaling dryland carbon and water fluxes with artificial neural networks of optical, thermal, and microwave satellite remote sensingM. P. Dannenberg0M. L. Barnes1W. K. Smith2M. R. Johnston3S. K. Meerdink4X. Wang5X. Wang6R. L. Scott7J. A. Biederman8Department of Geographical and Sustainability Sciences, University of Iowa, Iowa City, IA 52245, USAO'Neill School of Public and Environmental Affairs, Indiana University, Bloomington, IN 47405, USASchool of Natural Resources and the Environment, University of Arizona, Tucson, AZ 85721, USADepartment of Geographical and Sustainability Sciences, University of Iowa, Iowa City, IA 52245, USADepartment of Geographical and Sustainability Sciences, University of Iowa, Iowa City, IA 52245, USAO'Neill School of Public and Environmental Affairs, Indiana University, Bloomington, IN 47405, USASchool of Natural Resources and the Environment, University of Arizona, Tucson, AZ 85721, USASouthwest Watershed Research Center, Agricultural Research Service, U.S. Department of Agriculture, Tucson, AZ 85719, USASouthwest Watershed Research Center, Agricultural Research Service, U.S. Department of Agriculture, Tucson, AZ 85719, USA<p>Earth's drylands are home to more than two billion people, provide key ecosystem services, and exert a large influence on the trends and variability in Earth's carbon cycle. However, modeling dryland carbon and water fluxes with remote sensing suffers from unique challenges not typically encountered in mesic systems, particularly in capturing soil moisture stress. Here, we develop and evaluate an approach for the joint modeling of dryland gross primary production (GPP), net ecosystem exchange (NEE), and evapotranspiration (ET) in the western United States (US) using a suite of AmeriFlux eddy covariance sites spanning major functional types and aridity regimes. We use artificial neural networks (ANNs) to predict dryland ecosystem fluxes by fusing optical vegetation indices, multitemporal thermal observations, and microwave soil moisture and temperature retrievals from the Soil Moisture Active Passive (SMAP) sensor. Our new dryland ANN (DrylANNd) carbon and water flux model explains more than 70 % of monthly variance in GPP and ET, improving upon existing MODIS GPP and ET estimates at most dryland eddy covariance sites. DrylANNd predictions of NEE were considerably worse than its predictions of GPP and ET likely because soil and plant respiratory processes are largely invisible to satellite sensors. Optical vegetation indices, particularly the normalized difference vegetation index (NDVI) and near-infrared reflectance of vegetation (NIR<span class="inline-formula"><sub>v</sub></span>), were generally the most important variables contributing to model skill. However, daytime and nighttime land surface temperatures and SMAP soil moisture and soil temperature also contributed to model skill, with SMAP especially improving model predictions of shrubland, grassland, and savanna fluxes and land surface temperatures improving predictions in evergreen needleleaf forests. Our results show that a combination of optical vegetation indices and thermal infrared and microwave observations can substantially improve estimates of carbon and water fluxes in drylands, potentially providing the means to better monitor vegetation function and ecosystem services in these important regions that are undergoing rapid hydroclimatic change.</p>https://bg.copernicus.org/articles/20/383/2023/bg-20-383-2023.pdf |
| spellingShingle | M. P. Dannenberg M. L. Barnes W. K. Smith M. R. Johnston S. K. Meerdink X. Wang X. Wang R. L. Scott J. A. Biederman Upscaling dryland carbon and water fluxes with artificial neural networks of optical, thermal, and microwave satellite remote sensing Biogeosciences |
| title | Upscaling dryland carbon and water fluxes with artificial neural networks of optical, thermal, and microwave satellite remote sensing |
| title_full | Upscaling dryland carbon and water fluxes with artificial neural networks of optical, thermal, and microwave satellite remote sensing |
| title_fullStr | Upscaling dryland carbon and water fluxes with artificial neural networks of optical, thermal, and microwave satellite remote sensing |
| title_full_unstemmed | Upscaling dryland carbon and water fluxes with artificial neural networks of optical, thermal, and microwave satellite remote sensing |
| title_short | Upscaling dryland carbon and water fluxes with artificial neural networks of optical, thermal, and microwave satellite remote sensing |
| title_sort | upscaling dryland carbon and water fluxes with artificial neural networks of optical thermal and microwave satellite remote sensing |
| url | https://bg.copernicus.org/articles/20/383/2023/bg-20-383-2023.pdf |
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