Comparison of Different Important Predictors and Models for Estimating Large-Scale Biomass of Rubber Plantations in Hainan Island, China

Abstract

Rubber (Hevea brasiliensis Muell.) plantations are among the most critical agricultural ecosystems in tropical regions, playing a vital role in regional carbon balance. Accurate large-scale biomass estimation for these plantations remains a challenging task due to the severe signal saturation problem. Recent advances in remote sensing big data, cloud platforms, and machine learning have facilitated the precise acquisition of key physiological variables, such as stand age (A) and canopy height (H), which are critical parameters for biomass estimation but have been underutilized in prior studies. Using Hainan Island—the second-largest rubber planting base in China—as a case study, we integrated extensive ground surveys, maps of stand age and canopy height, remote sensing indicators (RSIs), and geographical and climate indicators (ECIs) to ascertain the optimal method for estimating rubber plantation biomass. We compared different inputs and estimation approaches (direct and indirect) using the random forest algorithm and analyzed the spatiotemporal characteristics of rubber plantation biomass on Hainan Island. The results indicated that the traditional model (RSIs + ECIs) had low accuracy and significant estimation bias (R2 = 0.24, RMSE = 38.36 mg/ha). The addition of either stand age or canopy height considerably enhance model accuracy (R2 = 0.77, RMSE ≈ 21.12 mg/ha). Moreover, incorporating the DBH obtained through indirect inversion yielded even greater predictive accuracy (R2 = 0.97, RMSE = 7.73 mg/ha), outperforming estimates derived from an allometric equation model input with the DBH (R2 = 0.67, RMSE = 25.43 mg/ha). However, augmenting the model with stand age, canopy height, or their combination based on RSIs, ECIs, and DBH only marginally improved the accuracy. Consequently, it is not recommended in scenarios with limited data and computing resources. Employing the optimal model, we generated biomass maps of rubber plantations on Hainan Island for 2016 and 2020, revealing that the spatiotemporal distribution pattern of the biomass is closely associated with the establishment year of the rubber plantations. While average biomass in a few areas has undergone slight decreases, total biomass has exhibited significant growth, reaching 5.46 × 107 mg by the end of 2020, underscoring its considerable value as a carbon sink.

Estimating Sugarcane Aboveground Biomass and Carbon Stock Using the Combined Time Series of Sentinel Data with Machine Learning Algorithms

“…To alternatively inform the related stockholders, accurate and rapid mapping aimed at estimating sugarcane AGB and carbon stock quantification is useful. The studies of Mansaray et al [17], Wang et al [18], Wang et al [19], and Li et al [20] have shown highly accurate mapping of crops AGB (i.e., rice, sugarcane, graze, and rubber tree) using multiple remote sensing data and powerful machine learning regression models. Therefore, satellitebased Earth Observation (EO) data can offer valuable information on wide geographical areas in a timely and cost-efficient manner.…”

“…Uribeetxebarria et al [43] combined S1 and S2 data to estimate wheat biomass and yield in the Llanada Alavesa region in northern Spain, obtaining high accuracy with R 2 = 0.95 and RMSE = 0.2 t/ha. In addition, Li, Wang, Gao, Wu, Cheng, Ren, Bao, Yun, Wu, and Xie [20] also mapped the biomass of rubber plantations in Hainan island, China, using S2 and Landsat data, achieving high accuracy (R 2 = 0.97 and RMSE = 7.7 × 10 −9 t/ha). Their results proved highly accurate with excellent mapping crop biomass based on combining multi-temporal S1 and S2 satellites and the great machine learning regression methods.…”

Vegetation Classification and a Biomass Inversion Model for Wildfires in Chongli Based on Remote Sensing Data

“…The results of vegetation classification have a direct impact on forest biomass [20], which serves as an indicator of energy accumulation in the production and metabolic processes of an ecosystem within a specific area. It is a crucial indicator that reflects the structural and functional characteristics of the forest ecosystem, as well as its development potential [21]. These results hold significant practical significance for the scientific management and economic development of forested lands [22].…”

Semi-FCMNet: Semi-Supervised Learning for Forest Cover Mapping from Satellite Imagery via Ensemble Self-Training and Perturbation

“…Forests play a vital role in the land ecosystem of the Earth. They are indispensable for conserving biodiversity, protecting watersheds, capturing carbon, mitigating climate change effects [1,2], maintaining ecological balance, regulating rainfall patterns, and ensuring the stability of large-scale climate systems [3,4]. As a result, the timely and precise monitoring and mapping of forest cover has emerged as a vital aspect of sustainable forest management and the monitoring of ecosystem transformations [5].…”