Machine learning-based grassland aboveground biomass estimation and its response to climate variation in Southwest China

Abstract

The demand for accurate estimation of aboveground biomass (AGB) at high spatial resolution is increasing in grassland-related research and management, especially for those regions with complex topography and fragmented landscapes, where grass and shrub are interspersed. In this study, based on 519 field AGB observations, integrating Synthetic Aperture Radar (SAR; Sentinel-1) and high-resolution (Sentinel-2) remote sensing images, environmental and topographical data, we estimated the AGB of mountain grassland in Southwest China (Yunnan Province and Guizhou Province) by using remote sensing algorithms ranging from traditional regression to cutting edge machine learning (ML) and deep learning (DL) models. Four models (i.e., multiple stepwise regression (MSR), random forest (RF), support vector machine (SVM) and convolutional neural network (CNN)) were developed and compared for AGB simulation purposes. The results indicated that the RF model performed the best among the four models (testing dataset: decision co-efficient (R2) was 0.80 for shrubland and 0.75 for grassland, respectively). Among all input variables in the RF model, the vegetation indices played the most important role in grassland AGB estimation, with 6 vegetation indices (EVI, EVI2, NDVI, NIRv, MSR and DVI) in the top 10 of input variables. For shrubland, however, topographical factors (elevation, 12.7% IncMSE (increase in mean squared error)) and SAR data (VH band, 11.3% IncMSE) were the variables which contributed the most in the AGB estimation model. By comparing the input variables to the RF model, we found that integrating SAR data has the potential to improve grassland AGB estimation, especially for shrubland (26.7% improvement in the estimation of shrubland AGB). Regional grassland AGB estimation showed a lower mean AGB in Yunnan Province (443.6 g/m2) than that in Guizhou Province (687.6 g/m2) in 2021. Moreover, the correlation between five consecutive years (2018–2022) of AGB data and climatic factors calculated by partial correlation analysis showed that regional AGB was positively related with mean annual precipitation in more than 70% of the grassland and 60% of the shrubland area, respectively. Also, we found a positive relationship with mean annual temperature in 62.8% of the grassland and 55.6% of the shrubland area, respectively. This study demonstrated that integrating SAR into grassland AGB estimation led to a remote sensing estimation model that greatly improved the accuracy of modeled mountain grassland AGB in southwest China, where the grassland consists of a complex mix of grass and shrubs.

Estimation of grassland aboveground biomass and its response to climate changes based on remote sensing inversion in Three-River-Source National Park, Tibet Plateau, China

“…Additionally, the RMSE and the MAE measured the deviation between actual and simulated grassland AGB values. Smaller values for the RMSE and MAE signified a smaller difference between the actual and simulated values, thereby indicating higher accuracy in the constructed inversion model (Zhang et al, 2023).…”

“…Moreover, there is an asynchronous relation between grassland biomass and changes in precipitation. Zhang et al (2023) researched moisture conditions affecting both the aboveground and belowground biomasses of grasslands during different stages of vegetation flowering and fruiting. Wang Q. et al (2022) found that increased temperatures have a significant effect on the biomass and species diversity of degraded grasslands in their natural recovery state, although they have little effect on natural grasslands.…”

“…Ge et al (2022) constructed grassland AGB data from 2000 to 2019 in North China by comparing 4 machine learning algorithms and selecting the optimal random forest (RF) model. Liu et al (2023) also constructed a model of grassland biomass in the western part of Southwest China with the use of RF and analyzed the relation between its response to climatic factors. Although machine learning algorithms are widely used in various fields for their advantages and accuracy, they differ markedly in sample requirements, parameter adjustment, and computational efficiency Ma et al, 2023).…”

Integrating the PROSAIL and SVR Models to Facilitate the Inversion of Grassland Aboveground Biomass: A Case Study of Zoigê Plateau, China

“…They also have obvious spatial and temporal limitations, affecting their versatility and portability [28]. Therefore, these empirical methods are difficult to apply to large-scale, long time-series, and specific growth period studies [29,30].…”

“…For example, Quan et al [20] used the PROSAIL model and Landsat-8 spectral data to invert the LAI and Cm to estimate the AGB. He et al [30] estimated the grassland AGB in the Zoigê Plateau using the lookup table (LUT) method based on the PROSAIL model and MODIS products. However, due to the complexity of RTM, a large amount of computation is required to estimate grassland AGB, resulting in low efficiency.…”