Spatiotemporal downscaling and nowcasting of urban land surface temperatures with deep neural networks

• URL: http://arxiv.org/abs/2605.13566v1
• Date: Wed, 13 May 2026 14:05:24 GMT
• Title: Spatiotemporal downscaling and nowcasting of urban land surface temperatures with deep neural networks
• Abstract summary: Land Surface Temperature (LST) is a key variable for various applications, such as urban climate and ecology studies.<n>We combine observations from a geostationary and a polar orbiting satellite and provide LST fields at high spatial and high temporal resolution.<n>We present an LST nowcasting model based on Conv-LSTM architecture, trained across downscaled LST fields with forecast lead times of 15 to 75 minutes.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Land Surface Temperature (LST) is a key variable for various applications, such as urban climate and ecology studies. Yet, existing satellite-derived LST products provide either high spatial or high temporal resolution, resulting in a fundamental trade-off between the two. To address this trade-off, we combine observations from a geostationary and a polar orbiting satellite and provide LST fields at high spatial and high temporal resolution (1 km at 15-min intervals). We demonstrate their application for intraday forecasting of LSTs. To estimate LST fields at high spatiotemporal resolution, a U-Net model is trained to map LST fields from SEVIRI/MSG (3 km and 15 min resolution) to LST fields from Terra/Aqua MODIS (1 km, 4 overpasses per day) that are collocated in space and time. The presented model has been trained on LSTs across large European cities with a population exceeding 1 million inhabitants, and achieves an RMSE = $1.92$°C and near-zero bias MBE = $0.01$°C on the hold-out test set. As a second step, we present an LST nowcasting model based on ConvLSTM architecture, trained across downscaled LST fields with forecast lead times of 15 to 75 minutes. The nowcasting model outperforms a persistence and a Climatological Rolling Median benchmarks, with RMSEs of $0.57$ to $1.15$°C for the considered lead times and biases ranging from $-0.1$ to $0.14$°C. An additional validation conducted against independent MODIS overpasses confirms robust performance. Our LST forecast model at high spatiotemporal resolution is directly applicable to operational satellite-based LST monitoring.

Related papers

• Predicting Pseudo-nitzschia harmful algal blooms along the Portuguese Coast using satellite-derived predictors [69.38367341895632]
  Pseudo-nitzschia diatoms pose recurrent risks to coastal ecosystems and harvesting along the Portuguese Atlantic coast.<n>We develop and evaluate a machine-learning framework to predict harmful algal bloom occurrence using exclusively satellite-derived predictors.
  arXiv  Detail & Related papers  (2026-07-08T18:14:24Z)
• Air Quality Downscaling with Station-Guided Pseudo-Supervision [60.31045749983062]
  Super-resolving coarse atmospheric fields to local PM$_2.5$ variations is uniquely challenged by a mismatch in spatial support.<n>We present a station-guided framework for high-resolution PM$_2.5$ downscaling over Europe.
  arXiv  Detail & Related papers  (2026-07-06T16:32:25Z)
• Tyche: One Step Flow for Efficient Probabilistic Weather Forecasting [48.263481135566316]
  We present Tyche, a one-step conditional flow model for efficient probabilistic weather forecasting.<n>Experiments on ERA5 at 1.5$circ$ and 6-hour resolution show that our Tyche, using merely a single NFE, matches or exceeds the forecast skill and calibration of state-of-the-art multi-step generative baselines.
  arXiv  Detail & Related papers  (2026-05-07T20:25:28Z)
• ASTREA: Introducing Agentic Intelligence for Orbital Thermal Autonomy [51.56484100374058]
  ASTREA is the first agentic system executed on flight-heritage hardware for autonomous spacecraft operations.<n>We integrate a resource-constrained Large Language Model (LLM) agent with a reinforcement learning controller in an asynchronous architecture tailored for space-qualified platforms.
  arXiv  Detail & Related papers  (2025-09-16T08:52:13Z)
• WGAST: Weakly-Supervised Generative Network for Daily 10 m Land Surface Temperature Estimation via Spatio-Temporal Fusion [3.344876133162209]
  We present WGAST, a Weakly-performing Genedrative Network for Daily 10 m LST resolution via SSupervise-Temporal Fusion of MODIS, Landsat 8, and Sentinel-2.<n>It adopts a conditional generative adversarial architecture with a generator composed of four stages: feature extraction, fusion, LST reconstruction, and noise suppression.<n>Experiments demonstrate that WGAST outperforms existing methods in both quantitative and qualitative evaluations.
  arXiv  Detail & Related papers  (2025-08-08T17:49:46Z)
• OKG-LLM: Aligning Ocean Knowledge Graph with Observation Data via LLMs for Global Sea Surface Temperature Prediction [70.48962924608033]
  This work presents the first systematic effort to construct an Ocean Knowledge Graph (OKG) specifically designed to represent diverse ocean knowledge for SST prediction.<n>We develop a graph embedding network to learn the comprehensive semantic and structural knowledge within the OKG, capturing both the unique characteristics of individual sea regions and the complex correlations between them. Finally, we align the learned knowledge with fine-grained numerical SST data and leverage a pre-trained LLM to model SST patterns for accurate prediction.
  arXiv  Detail & Related papers  (2025-07-31T02:06:03Z)
• FuseTen: A Generative Model for Daily 10 m Land Surface Temperature Estimation from Spatio-Temporal Satellite Observations [3.344876133162209]
  Urban heatwaves, droughts, and land heatwaves are pressing and growing challenges in the context of climate change.<n>One of the most important variables for assessing and understanding these phenomena is Land Surface Temperature (LST)<n>We propose FuseTen to produce daily LST observations at a fine 10 m spatial resolution by fusing-basedtemporal observations from Landsat 8, and Terra MODIS.
  arXiv  Detail & Related papers  (2025-07-30T23:04:16Z)
• Daily Land Surface Temperature Reconstruction in Landsat Cross-Track Areas Using Deep Ensemble Learning With Uncertainty Quantification [5.6087513714958686]
  We propose DELAG, an ensemble learning method that integrates annual temperature cycles and processes to reconstruct Landsat LST in complex urban areas.<n>DelAG successfully reconstructed LST in New York City, London and Hong Kong from three different areas as study areas.<n>Results demonstrate the successful reconstruction through DELAG and highlight the broader applications of LST reconstruction for estimating accurate air temperature.
  arXiv  Detail & Related papers  (2025-02-20T10:35:01Z)
• Deep Learning for Spatio-Temporal Fusion in Land Surface Temperature Estimation: A Comprehensive Survey, Experimental Analysis, and Future Trends [3.344876133162209]
  Land Surface Temperature (LST) plays a critical role in understanding key environmental processes.<n>Satellite sensors often face a trade-off between spatial and temporal resolutions.<n>Spatio-Temporal Fusion (STF) has emerged as a powerful method to integrate two satellite data sources.
  arXiv  Detail & Related papers  (2024-12-21T13:53:15Z)
• MambaDS: Near-Surface Meteorological Field Downscaling with Topography Constrained Selective State Space Modeling [68.69647625472464]
  Downscaling, a crucial task in meteorological forecasting, enables the reconstruction of high-resolution meteorological states for target regions. Previous downscaling methods lacked tailored designs for meteorology and encountered structural limitations. We propose a novel model called MambaDS, which enhances the utilization of multivariable correlations and topography information.
  arXiv  Detail & Related papers  (2024-08-20T13:45:49Z)
• Long-term drought prediction using deep neural networks based on geospatial weather data [75.38539438000072]
  High-quality drought forecasting up to a year in advance is critical for agriculture planning and insurance. We tackle drought data by introducing an end-to-end approach that adopts a systematic end-to-end approach. Key findings are the exceptional performance of a Transformer model, EarthFormer, in making accurate short-term (up to six months) forecasts.
  arXiv  Detail & Related papers  (2023-09-12T13:28:06Z)
• Generating gapless land surface temperature with a high spatio-temporal resolution by fusing multi-source satellite-observed and model-simulated data [7.166180462786921]
  We present an integrated temperature fusion framework for satellite-observed and LSM-simulated LST data to map gapless LST at a 60-m spatial resolution and half-hourly temporal resolution. Evaluations were implemented in an urban-dominated region (the city of Wuhan in China) and a natural-dominated region (the Heihe River Basin in China)
  arXiv  Detail & Related papers  (2021-11-29T02:28:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.