Examining Urban Agglomeration Heat Island with Explainable Ai: An Enhanced Consideration of Anthropogenic Heat Emissions DOI

Tianyu Sheng,

Zhixin Zhang,

Zhen Qian

и другие.

Опубликована: Янв. 1, 2024

Язык: Английский

Two-stage meta-ensembling machine learning model for enhanced water quality forecasting DOI

Sepideh Heydari,

Mohammad Reza Nikoo,

Ali Mohammadi

и другие.

Journal of Hydrology, Год журнала: 2024, Номер 641, С. 131767 - 131767

Опубликована: Авг. 3, 2024

Язык: Английский

Процитировано

5
Block-scale modeling of residential land prices: Incorporating multilevel determinants and explainable artificial intelligence DOI
Peng Zhang, Shengfu Yang, Juan Huang

и другие.

Habitat International, Год журнала: 2025, Номер 156, С. 103283 - 103283

Опубликована: Янв. 2, 2025

Язык: Английский

Процитировано

0
A robust framework for accurate land surface temperature retrieval: Integrating split-window into knowledge-guided machine learning approach DOI

Yuanliang Cheng,

Hua Wu, Zhao‐Liang Li

и другие.

Remote Sensing of Environment, Год журнала: 2025, Номер 318, С. 114609 - 114609

Опубликована: Янв. 22, 2025

Язык: Английский

Процитировано

0
LFSR: Low-resolution Filling then Super-resolution Reconstruction framework for gapless all-weather MODIS-like land surface temperature generation DOI
Chan Li, Penghai Wu, Si‐Bo Duan

и другие.

Remote Sensing of Environment, Год журнала: 2025, Номер 319, С. 114637 - 114637

Опубликована: Фев. 8, 2025

Язык: Английский

Процитировано

0
Generation of global 1 km all-weather instantaneous and daily mean land surface temperatures from MODIS data DOI Creative Commons
Bing Li, Shunlin Liang, Han Ma

и другие.

Earth system science data, Год журнала: 2024, Номер 16(8), С. 3795 - 3819

Опубликована: Авг. 27, 2024

Abstract. Land surface temperature (LST) serves as a crucial variable in characterizing climatological, agricultural, ecological, and hydrological processes. Thermal infrared (TIR) remote sensing provides high temporal spatial resolutions for obtaining LST information. Nevertheless, TIR-based satellite products frequently exhibit missing values due to cloud interference. Prior research on estimating all-weather instantaneous has predominantly concentrated regional or continental scales. This study involved generating global daily mean product spanning from 2000 2020 using XGBoost. Multisource data, including Moderate-Resolution Imaging Spectroradiometer (MODIS) top-of-atmosphere (TOA) observations, radiation products, reanalysis were employed. Validation an independent dataset of 77 individual stations demonstrated the accuracy our yielding root squared errors (RMSEs) 2.787 K (instantaneous) 2.175 (daily). The RMSE clear-sky conditions was 2.614 product, which is slightly lower than cloudy-sky 2.931 K. Our higher compared MODIS official (instantaneous = 3.583 K; 3.105 K) land component fifth generation European ReAnalysis (ERA5-Land) 4.048 2.988 K). Significant improvements are observed notably at latitudes, product. monthly scale, first day 2010 can be freely downloaded https://doi.org/10.5281/zenodo.4292068 (Li et al., 2024), complete will available https://glass-product.bnu.edu.cn/ (last access: 22 August 2024).

Язык: Английский

Процитировано

1
Examining urban agglomeration heat island with explainable AI: An enhanced consideration of anthropogenic heat emissions DOI

Tianyu Sheng,

Zhixin Zhang,

Zhen Qian

и другие.

Urban Climate, Год журнала: 2024, Номер 59, С. 102251 - 102251

Опубликована: Дек. 20, 2024

Язык: Английский

Процитировано

1
Examining Urban Agglomeration Heat Island with Explainable Ai: An Enhanced Consideration of Anthropogenic Heat Emissions DOI

Tianyu Sheng,

Zhixin Zhang,

Zhen Qian

и другие.

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

0