Mon Dec 15, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
Alvarez, Cesar Ivan, Ulloa Vaca, Carlos Andrés, and Echeverria Llumipanta, Neptali Armando, 2025. Machine Learning for Urban Air Quality Prediction Using Google AlphaEarth Foundations Satellite Embeddings: A Case Study of Quito, Ecuador. Remote Sensing, 17(20):3472, doi:10.3390/rs17203472.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2025RemS...17.3472A,
author = {{Alvarez}, Cesar Ivan and {Ulloa Vaca}, Carlos Andr{\'e}s and {Echeverria Llumipanta}, Neptali Armando},
title = "{Machine Learning for Urban Air Quality Prediction Using Google AlphaEarth Foundations Satellite Embeddings: A Case Study of Quito, Ecuador}",
journal = {Remote Sensing},
keywords = {urban air quality, satellite embeddings, Google Earth Engine, machine learning, Quito},
year = 2025,
month = oct,
volume = {17},
number = {20},
eid = {3472},
pages = {3472},
abstract = "{What are the main findings? Machine learning using Google AlphaEarth
Foundations satellite embeddings in Google Earth Engine
accurately predicted NO$_{2}$ and SO$_{2}$ concentrations in
Quito (R$^{2}$ = 0.71), capturing fine-scale pollution patterns
at 10 m resolution. SHAP analysis revealed that only a small
subset of embedding bands drives accurate predictions,
demonstrating that compact, globally consistent features can
explain urban air quality dynamics without handcrafted indices
or auxiliary datasets. Machine learning using Google AlphaEarth
Foundations satellite embeddings in Google Earth Engine
accurately predicted NO$_{2}$ and SO$_{2}$ concentrations in
Quito (R$^{2}$ = 0.71), capturing fine-scale pollution patterns
at 10 m resolution. SHAP analysis revealed that only a small
subset of embedding bands drives accurate predictions,
demonstrating that compact, globally consistent features can
explain urban air quality dynamics without handcrafted indices
or auxiliary datasets. What is the implication of the main
finding? Embedding-based remote sensing models provide a
scalable solution for urban air quality monitoring in the Global
South, overcoming sparse ground stations and persistent cloud
cover. The approach supports policy-relevant applications such
as hotspot detection, trend analysis, and sustainable urban
planning, offering transferable methods for data-scarce cities
worldwide. Embedding-based remote sensing models provide a
scalable solution for urban air quality monitoring in the Global
South, overcoming sparse ground stations and persistent cloud
cover. The approach supports policy-relevant applications such
as hotspot detection, trend analysis, and sustainable urban
planning, offering transferable methods for data-scarce cities
worldwide. Many Global-South cities lack dense monitoring and
suffer persistent cloud cover, hampering fine-scale trend
detection. This study evaluates the potential of annual multi-
sensor satellite embeddings from the AlphaEarth Foundations
model in Google Earth Engine to predict and map major air
pollutants in Quito, Ecuador, between 2017 and 2024. The
64-dimensional embeddings integrate Sentinel-1 radar, Sentinel-2
optical imagery, Landsat surface reflectance, ERA5-Land climate
variables, GRACE terrestrial water storage, and GEDI canopy
structure into a compact representation of surface and climatic
conditions. Annual median concentrations of NO$_{2}$, SO$_{2}$,
PM$_{2.5}$, CO, and O$_{3}$ from the Red Metropolitana de
Monitoreo Atmosf{\'e}rico de Quito (REEMAQ) were paired with
collocated embeddings and modeled using five machine learning
algorithms. Support Vector Regression achieved the highest
accuracy for NO$_{2}$ and SO$_{2}$ (R$^{2}$ = 0.71 for both),
capturing fine-scale spatial patterns and multi-year changes,
including COVID-19 lockdown-related reductions. PM$_{2.5}$ and
CO were predicted with moderate accuracy, while O$_{3}$ remained
challenging due to its short-term photochemical and
meteorological drivers and the mismatch with annual aggregation.
SHAP analysis revealed that a small subset of embedding bands
dominated predictions for NO$_{2}$ and SO$_{2}$. The approach
provides a scalable and transferable framework for high-
resolution urban air quality mapping in data-scarce
environments, supporting long-term monitoring, hotspot
detection, and evidence-based policy interventions.}",
doi = {10.3390/rs17203472},
adsurl = {https://ui.adsabs.harvard.edu/abs/2025RemS...17.3472A},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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