Human-induced vegetation shifts drive global declines in urban vegetation transpiration

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gz, zip

Original repository

23017186

DOI

10.5281/zenodo.23017186

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Last checked

Nov 5, 2026

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cc-by-4.0

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Nov 5, 2026

About this resource

Description This repository contains the complete codebase for the manuscript "Human-induced vegetation shifts drive global declines in urban vegetation transpiration." The software implements a hybrid physics–deep learning framework (CNN-PM) for estimating urban vegetation transpiration (VTu), a six-model comparison framework for systematic architecture and input ablation analysis, a multi-source geospatial data preprocessing pipeline, an XGBoost-SHAP trend attribution module, three evapotranspiration partitioning modules (uWUE, Pérez-Priego and TEA) and a two-dimensional flux footprint module (FFP). All code is written in Python and built on PyTorch. Repository structure The repository comprises twelve modular packages, organized along the analysis pipeline from raw data ingestion through model training, evaluation, and trend attribution: 1. Data preprocessing pipeline (data_preprocessing/) Transforms raw multi-source geospatial data into model-ready input arrays. Handles 7 data sources (Landsat, TerraClimate, GLASS LAI, CAMS CO₂, GGA anthropogenic heat flux, WorldPop, VIIRS nighttime lights) at native resolutions ranging from 30 m to 10 km, performing quality control (physical range checks, IQR-based outlier detection, gap filling), spatial alignment (reprojection, resampling to a common 30 m grid), and temporal aggregation (half-hourly flux observations to daily/monthly means). Includes an eddy covariance flux station processor with u* filtering, energy balance closure assessment, and turbulence-based VTu partitioning. Outputs standardized .npz arrays compatible with all six comparison models. 2. Hybrid CNN-PM model with AHF (cnn_pm_model/) — Model #5 The primary model of the study. Couples two sub-CNN networks (CNN_c for climatic conductance G_c, CNN_a for anthropogenic conductance G_a) with a differentiable Penman-Monteith equation (PM-MU). CNN_c takes 10 vegetation/climate inputs through 3 convolutional layers; CNN_a takes 7 urban/anthropogenic inputs (including AHF) through 4 convolutional layers. Both output conductance values via Softplus activation, which are then passed to the PM-MU physics layer where AHF is explicitly added to the available energy term. The model is trained end-to-end with MSE loss, producing physically interpretable intermediate variables (G_c, G_a) alongside the final VTu estimate. 3. Hybrid CNN-PM model without AHF (cnn_pm_noahf/) — Model #6 Identical to model #5 except AHF is excluded from both CNN_a inputs (6 instead of 7) and the PM energy balance (Rn − G instead of Rn − G + AHF). Direct comparison with model #5 quantifies the added value of explicit AHF coupling in the hybrid framework. 4. Pure CNN model with AHF (pure_cnn_vtu/) — Model #3 A fully data-driven multi-branch 1D CNN with Squeeze-and-Excitation (SE) attention that directly predicts VTu from 14 input variables without embedding any physical equations. Inputs are grouped into three semantically coherent branches — vegetation (6 variables), meteorological (4 variables), and urban/AHF (4 variables) — each encoded by 3 residual convolutional blocks with channel gating, then fused via SE attention and regressed through a 4-layer fully connected head. Serves as the data-driven baseline for comparison against the physics-informed hybrid model. 5. Pure CNN model without AHF (pure_cnn_noahf/) — Model #4 Identical to model #3 except the urban branch receives 3 inputs instead of 4 (AHF removed). Comparison with model #3 quantifies AHF's contribution