Cargando…

Deep learning estimation of northern hemisphere soil freeze-thaw dynamics using satellite multi-frequency microwave brightness temperature observations

Satellite microwave sensors are well suited for monitoring landscape freeze-thaw (FT) transitions owing to the strong brightness temperature (TB) or backscatter response to changes in liquid water abundance between predominantly frozen and thawed conditions. The FT retrieval is also a sensitive clim...

Descripción completa

Detalles Bibliográficos
Autores principales: Donahue, Kellen, Kimball, John S., Du, Jinyang, Bunt, Fredrick, Colliander, Andreas, Moghaddam, Mahta, Johnson, Jesse, Kim, Youngwook, Rawlins, Michael A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10690831/
https://www.ncbi.nlm.nih.gov/pubmed/38045095
http://dx.doi.org/10.3389/fdata.2023.1243559
_version_ 1785152606274846720
author Donahue, Kellen
Kimball, John S.
Du, Jinyang
Bunt, Fredrick
Colliander, Andreas
Moghaddam, Mahta
Johnson, Jesse
Kim, Youngwook
Rawlins, Michael A.
author_facet Donahue, Kellen
Kimball, John S.
Du, Jinyang
Bunt, Fredrick
Colliander, Andreas
Moghaddam, Mahta
Johnson, Jesse
Kim, Youngwook
Rawlins, Michael A.
author_sort Donahue, Kellen
collection PubMed
description Satellite microwave sensors are well suited for monitoring landscape freeze-thaw (FT) transitions owing to the strong brightness temperature (TB) or backscatter response to changes in liquid water abundance between predominantly frozen and thawed conditions. The FT retrieval is also a sensitive climate indicator with strong biophysical importance. However, retrieval algorithms can have difficulty distinguishing the FT status of soils from that of overlying features such as snow and vegetation, while variable land conditions can also degrade performance. Here, we applied a deep learning model using a multilayer convolutional neural network driven by AMSR2 and SMAP TB records, and trained on surface (~0–5 cm depth) soil temperature FT observations. Soil FT states were classified for the local morning (6 a.m.) and evening (6 p.m.) conditions corresponding to SMAP descending and ascending orbital overpasses, mapped to a 9 km polar grid spanning a five-year (2016–2020) record and Northern Hemisphere domain. Continuous variable estimates of the probability of frozen or thawed conditions were derived using a model cost function optimized against FT observational training data. Model results derived using combined multi-frequency (1.4, 18.7, 36.5 GHz) TBs produced the highest soil FT accuracy over other models derived using only single sensor or single frequency TB inputs. Moreover, SMAP L-band (1.4 GHz) TBs provided enhanced soil FT information and performance gain over model results derived using only AMSR2 TB inputs. The resulting soil FT classification showed favorable and consistent performance against soil FT observations from ERA5 reanalysis (mean percent accuracy, MPA: 92.7%) and in situ weather stations (MPA: 91.0%). The soil FT accuracy was generally consistent between morning and afternoon predictions and across different land covers and seasons. The model also showed better FT accuracy than ERA5 against regional weather station measurements (91.0% vs. 86.1% MPA). However, model confidence was lower in complex terrain where FT spatial heterogeneity was likely beneath the effective model grain size. Our results provide a high level of precision in mapping soil FT dynamics to improve understanding of complex seasonal transitions and their influence on ecological processes and climate feedbacks, with the potential to inform Earth system model predictions.
format Online
Article
Text
id pubmed-10690831
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-106908312023-12-02 Deep learning estimation of northern hemisphere soil freeze-thaw dynamics using satellite multi-frequency microwave brightness temperature observations Donahue, Kellen Kimball, John S. Du, Jinyang Bunt, Fredrick Colliander, Andreas Moghaddam, Mahta Johnson, Jesse Kim, Youngwook Rawlins, Michael A. Front Big Data Big Data Satellite microwave sensors are well suited for monitoring landscape freeze-thaw (FT) transitions owing to the strong brightness temperature (TB) or backscatter response to changes in liquid water abundance between predominantly frozen and thawed conditions. The FT retrieval is also a sensitive climate indicator with strong biophysical importance. However, retrieval algorithms can have difficulty distinguishing the FT status of soils from that of overlying features such as snow and vegetation, while variable land conditions can also degrade performance. Here, we applied a deep learning model using a multilayer convolutional neural network driven by AMSR2 and SMAP TB records, and trained on surface (~0–5 cm depth) soil temperature FT observations. Soil FT states were classified for the local morning (6 a.m.) and evening (6 p.m.) conditions corresponding to SMAP descending and ascending orbital overpasses, mapped to a 9 km polar grid spanning a five-year (2016–2020) record and Northern Hemisphere domain. Continuous variable estimates of the probability of frozen or thawed conditions were derived using a model cost function optimized against FT observational training data. Model results derived using combined multi-frequency (1.4, 18.7, 36.5 GHz) TBs produced the highest soil FT accuracy over other models derived using only single sensor or single frequency TB inputs. Moreover, SMAP L-band (1.4 GHz) TBs provided enhanced soil FT information and performance gain over model results derived using only AMSR2 TB inputs. The resulting soil FT classification showed favorable and consistent performance against soil FT observations from ERA5 reanalysis (mean percent accuracy, MPA: 92.7%) and in situ weather stations (MPA: 91.0%). The soil FT accuracy was generally consistent between morning and afternoon predictions and across different land covers and seasons. The model also showed better FT accuracy than ERA5 against regional weather station measurements (91.0% vs. 86.1% MPA). However, model confidence was lower in complex terrain where FT spatial heterogeneity was likely beneath the effective model grain size. Our results provide a high level of precision in mapping soil FT dynamics to improve understanding of complex seasonal transitions and their influence on ecological processes and climate feedbacks, with the potential to inform Earth system model predictions. Frontiers Media S.A. 2023-11-17 /pmc/articles/PMC10690831/ /pubmed/38045095 http://dx.doi.org/10.3389/fdata.2023.1243559 Text en Copyright © 2023 Donahue, Kimball, Du, Bunt, Colliander, Moghaddam, Johnson, Kim and Rawlins. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Big Data
Donahue, Kellen
Kimball, John S.
Du, Jinyang
Bunt, Fredrick
Colliander, Andreas
Moghaddam, Mahta
Johnson, Jesse
Kim, Youngwook
Rawlins, Michael A.
Deep learning estimation of northern hemisphere soil freeze-thaw dynamics using satellite multi-frequency microwave brightness temperature observations
title Deep learning estimation of northern hemisphere soil freeze-thaw dynamics using satellite multi-frequency microwave brightness temperature observations
title_full Deep learning estimation of northern hemisphere soil freeze-thaw dynamics using satellite multi-frequency microwave brightness temperature observations
title_fullStr Deep learning estimation of northern hemisphere soil freeze-thaw dynamics using satellite multi-frequency microwave brightness temperature observations
title_full_unstemmed Deep learning estimation of northern hemisphere soil freeze-thaw dynamics using satellite multi-frequency microwave brightness temperature observations
title_short Deep learning estimation of northern hemisphere soil freeze-thaw dynamics using satellite multi-frequency microwave brightness temperature observations
title_sort deep learning estimation of northern hemisphere soil freeze-thaw dynamics using satellite multi-frequency microwave brightness temperature observations
topic Big Data
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10690831/
https://www.ncbi.nlm.nih.gov/pubmed/38045095
http://dx.doi.org/10.3389/fdata.2023.1243559
work_keys_str_mv AT donahuekellen deeplearningestimationofnorthernhemispheresoilfreezethawdynamicsusingsatellitemultifrequencymicrowavebrightnesstemperatureobservations
AT kimballjohns deeplearningestimationofnorthernhemispheresoilfreezethawdynamicsusingsatellitemultifrequencymicrowavebrightnesstemperatureobservations
AT dujinyang deeplearningestimationofnorthernhemispheresoilfreezethawdynamicsusingsatellitemultifrequencymicrowavebrightnesstemperatureobservations
AT buntfredrick deeplearningestimationofnorthernhemispheresoilfreezethawdynamicsusingsatellitemultifrequencymicrowavebrightnesstemperatureobservations
AT collianderandreas deeplearningestimationofnorthernhemispheresoilfreezethawdynamicsusingsatellitemultifrequencymicrowavebrightnesstemperatureobservations
AT moghaddammahta deeplearningestimationofnorthernhemispheresoilfreezethawdynamicsusingsatellitemultifrequencymicrowavebrightnesstemperatureobservations
AT johnsonjesse deeplearningestimationofnorthernhemispheresoilfreezethawdynamicsusingsatellitemultifrequencymicrowavebrightnesstemperatureobservations
AT kimyoungwook deeplearningestimationofnorthernhemispheresoilfreezethawdynamicsusingsatellitemultifrequencymicrowavebrightnesstemperatureobservations
AT rawlinsmichaela deeplearningestimationofnorthernhemispheresoilfreezethawdynamicsusingsatellitemultifrequencymicrowavebrightnesstemperatureobservations