Modeling vegetation greenness and its climate sensitivity with deep‐learning technology

Abstract

Climate sensitivity of vegetation has long been explored using statistical or process‐based models. However, great uncertainties still remain due to the methodologies’ deficiency in capturing the complex interactions between climate and vegetation. Here, we developed global gridded climate–vegetation models based on long short‐term memory (LSTM) network, which is a powerful deep‐learning algorithm for long‐time series modeling, to achieve accurate vegetation monitoring and investigate the complex relationship between climate and vegetation. We selected the normalized difference vegetation index (NDVI) that represents vegetation greenness as model outputs. The climate data (monthly temperature and precipitation) were used as inputs. We trained the networks with data from 1982 to 2003, and the data from 2004 to 2015 were used to validate the models. Error analysis and sensitivity analysis were performed to assess the model errors and investigate the sensitivity of global vegetation to climate change. Results show that models based on deep learning are very effective in simulating and predicting the vegetation greenness dynamics. For models training, the root mean square error (RMSE) is <0.01. Model validation also assure the accuracy of our models. Furthermore, sensitivity analysis of models revealed a spatial pattern of global vegetation to climate, which provides us a new way to investigate the climate sensitivity of vegetation. Our study suggests that it is a good way to integrate deep‐learning method to monitor the vegetation change under global change. In the future, we can explore more complex climatic and ecological systems with deep learning and coupling with certain physical process to better understand the nature.

Urban Ecological Monitoring and Prediction Based on Deep Learning

“…For example, [20] used an autoregressive sliding average model to model the trend in air pollutant concentrations over their own time series to predict future average air pollutant concentrations. For example, [12] used polynomial regression combined with meteorological data to predict daily maximum concentrations O 3 and [13] used kernel regression combined with meteorological data to predict daily maximum concentrations. [14] used artificial neural networks and linear regression to predict future air quality in NO 2 the area to be predicted by combining meteorological and historical air quality from the area to be predicted and from the surrounding air quality monitoring stations.…”

“…Methods based on supervised learning include those based on generalised additive models and those based on Gaussian process regression. For example, [13] used generalised additive models to establish the relationship between air quality and the relevant explanatory variables. [14] used Gaussian process regression to establish relationships between characteristics such as traffic flow, population density, temperature, and air quality.…”

Updates on Model Hierarchies for Understanding and Simulating the Climate System: A Focus on Data‐Informed Methods and Climate Change Impacts

“…These processes lie in the “gray zone,” with scales O (1–100 km) which are under‐resolved in typical climate models but are largely resolved in computationally intensive sub‐kilometer scale models (e.g., atmospheric subgrid momentum fluxes: Yuval & O’Gorman, 2020; Yuval et al., 2021; Wang et al., 2022; ocean momentum forcing: Guillaumin & Zanna, 2021; Perezhogin et al., 2023; convection: Brenowitz & Bretherton, 2019; Gentine et al., 2018; clouds: Rasp et al., 2018; gravity waves: Sun et al., 2023) or large eddy simulations (e.g., eddy‐diffusivity momentum flux: Lopez‐Gomez et al., 2022; Shen et al., 2022). However, other coupled processes such as atmospheric chemistry, sea ice cover, and vegetation dynamics are not modeled explicitly at any resolution and may make use of observational data sets (e.g., ozone: Nowack et al., 2018; sea‐ice: Andersson et al., 2021; vegetation: Chen et al., 2021). Alternatively, some studies simply use ML to emulate existing parameterizations at a lower computational cost (e.g., radiation: Chevallier et al., 1998; Krasnopolsky et al., 2005; aerosol microphysics: Harder et al., 2022; cloud microphysics: Andre Perkins et al., 2023).…”

Machine learning and Global Vegetation: Random Forests for Downscaling and Gapfilling

“…In addition to disaster mitigation and management, predicting vegetation-drought dynamics is essential for numerous other applications, such as advances in fundamental ecological theory (Murray et al, 2018;Schwalm et al, 2017;Meza et al, 2020;Chen et al, 2021), studies investigating future change in land use, and climate change interventions (Jiang et al, 2017).…”

“…These studies have highlighted that ML methods can accurately predict the dynamics of vegetation (Roy, 2021;Gensheimer et al, 2022). However, studies applying ML methods to global vegetation dynamics concerning drought conditions are less prominent (Li et al, 2021b;Zhang et al, 2021b;Chen et al, 2021).…”