Pixel level spatial variability modeling using SHAP reveals the relative importance of factors influencing LST

Keyphrases

• Pixel Level 100%
• Spatial Variability 100%
• Land Surface Temperature 100%
• Factor Importance 100%
• Variability Modeling 100%
• Urban Agglomeration 20%
• Urban Centers 20%
• Sampling Strategy 20%
• Modified Normalized Difference Water Index (MNDWI) 20%
• Low Altitude 10%
• Geographical Location 10%
• High Temperature 10%
• Zhejiang Province 10%
• Annual Mean 10%
• Scant Attention 10%
• Spatial Heterogeneity 10%
• Nighttime 10%
• Spatial Distribution 10%
• Provincial Level 10%
• Management Authority 10%
• Agglomeration 10%
• Relative Contribution 10%
• Warm Climate 10%
• Community Health 10%
• Spatially Explicit 10%
• Mountainous Areas 10%
• Shapley Additive Explanations 10%
• Spatial Temperature Distribution 10%
• XGBoost 10%
• Lowland 10%
• SHAP Method 10%
• Healthcare Sustainability 10%
• Regional Thermal Environment 10%
• Regional Sustainability 10%
• Extreme Gradient Boosting Tree 10%
• Nighttime Light 10%
• Small Spatial Scale 10%

Earth and Planetary Sciences

• Land Surface Temperature 100%
• Spatial Distribution 20%
• Environmental Impact Assessment 10%
• Spatial Variation 10%
• Thermal Environment 10%