Preprints
https://doi.org/10.5194/bg-2022-186
https://doi.org/10.5194/bg-2022-186
 
30 Sep 2022
30 Sep 2022
Status: this preprint is currently under review for the journal BG.

Upscaling dryland carbon and water fluxes with artificial neural networks of optical, thermal, and microwave satellite remote sensing

Matthew P. Dannenberg1, Mallory L. Barnes2, William K. Smith3, Miriam R. Johnston1, Susan K. Meerdink1, Xian Wang2,3, Russell L. Scott4, and Joel A. Biederman4 Matthew P. Dannenberg et al.
  • 1Department of Geographical and Sustainability Sciences, University of Iowa, Iowa City IA 52245, USA
  • 2O’Neill School of Public and Environmental Affairs, Indiana University, Bloomington IN 47405, USA
  • 3School of Natural Resources and the Environment, University of Arizona, Tucson AZ 85721, USA
  • 4Southwest Watershed Research Center, Agricultural Research Service, U.S. Department of Agriculture, Tucson AZ 85719, USA

Abstract. 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 joint modeling of dryland gross primary production (GPP), net ecosystem exchange (NEE), and evapotranspiration (ET) in the western United States (U.S.) 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/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 (NIRv), 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, 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.

Matthew P. Dannenberg et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on bg-2022-186', Andrew Feldman, 19 Oct 2022
    • AC1: 'Reply on RC1', Matthew Dannenberg, 02 Dec 2022
  • RC2: 'Comment on bg-2022-186', Anonymous Referee #2, 21 Oct 2022
    • AC2: 'Reply on RC2', Matthew Dannenberg, 02 Dec 2022

Matthew P. Dannenberg et al.

Data sets

Monthly 0.05° gross primary production, net ecosystem exchange, and evapotranspiration estimates for western U.S. drylands Matthew P. Dannenberg, Mallory L. Barnes, William K. Smith, Miriam R. Johnston, Susan K. Meerdink, Xian Wang, Russell L. Scott and Joel A. Biederman https://doi.org/10.25820/data.006185

Matthew P. Dannenberg et al.

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Short summary
Earth’s drylands provide ecosystem services to many people and will likely be strongly affected by climate change, but it is quite challenging to monitor the productivity and water use of dryland plants with satellites. We developed and tested an approach for estimating dryland vegetation activity using machine learning to combine information from multiple satellite sensors. Our approach excelled at estimating photosynthesis and water use, largely due to inclusion of satellite soil moisture.
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