Selecting the appropriate vegetation index for accurate biomass estimation is a prerequisite before and during the ecosystem management project. This study, aims to compare Vegetation Indices (VIs) that are combining both Visible and Near Infrared OLI bands (VNIR-VIs), Visible and Short Wave Infrared OLI bands and also NIR and Short Wave Infrared OLI bands (SWIR-VIs) in order to accurately model the Aboveground Biomass (AGB) of three widely-located study sites over the arid steppe lands in Algeria. The Simple Linear Model (SLM) and Support Vector Machine (SVM) were utilised as statistical learning techniques on data; firstly, from each study site separately, and secondly, from all study sites (pooled data). In all study sites, SVM improves R² with a mean of 4.5% and decreases the Root Mean Squared Error (RMSE) and Percentage of Error (PE), respectively, with 15.50 (kg DM ha⁻¹) and 1.33% on average. In all cases, the SWIR-VIs outperforms the VNIR-VIs with an improvement rate of 40% of R² and an average reduction of 362.36 kg DM ha⁻¹ and 25% of RMSE and PE, respectively. The principal main improvement was found to involve the pooled data-based model utilising normalised difference VI form, which combines OLI₂(0.452–0.512 μm) with OLI₇(2.107–2.294 μm), (R² = 0.840, p < 0.0005).

选择合适的植被指数以进行准确的生物量估算是生态系统管理项目之前和期间的先决条件。本研究旨在比较将可见光和近红外 OLI 波段 (VNIR-VI)、可见光和短波红外 OLI 波段以及 NIR 和短波红外 OLI 波段 (SWIR-VI) 组合在一起的植被指数 (VI)准确模拟阿尔及利亚干旱草原上三个广泛分布的研究地点的地上生物量 (AGB)。简单线性模型（SLM）和支持向量机（SVM）被用作数据的统计学习技术；首先，分别来自每个研究地点，其次，来自所有研究地点（汇总数据）。在所有研究地点，SVM 提高了R² 平均值为 4.5%，并分别降低了均方根误差 (RMSE) 和误差百分比 (PE)，平均为 15.50 (kg DM ha ^-1 ) 和 1.33%。在所有情况下，SWIR-VI 均优于 VNIR-VI，R ²提高率为 40%，平均减少 362.36 kg DM ha ^-1和 RMSE 和 PE 分别为 25%。主要主要的改进被认为涉及利用归一化的差VI形式汇集基于数据的模型，它结合OLI ₂与OLI（0.452-0.512微米）₇（2.107-2.294微米），（- [R 2 = 0.840，p <0.0005） .