Sub-sampling large-scale, land-based macroalgal cultures to estimate stocking densities is time-consuming, labour-intensive, and inaccurate. Therefore, the development of innovative methods to monitor stocking densities are required to maximise macroalgal productivity. In this study the spectral reflectance of a range of biomass densities (0.5–12.0 g L⁻¹) of Ulva ohnoi was measured using a spectroradiometer (ASD) and a multispectral camera (REMX). A two-band normalised difference vegetation index (NDVI), which was standardised by depth of the culture system, was quantified from both sets of data, and used to establish biomass density sensing models. The models developed using the ASD and REMX data had strong piecewise linear relationships between the standardised NDVI and biomass density (R² > 0.85, residual mean standard error < 1.31, and mean standard error < 0.99), but differed in breakpoint and slope of the regression lines. Subsequently, both models were validated using data collected in 4000 L high-rate algal ponds, representing practical conditions of the commercial land-based production of macroalgae. Notably, the REMX model had a better fit with the validation data, and the mean difference in the actual versus predicted densities was ≤0.76 g L⁻¹, with a maximum difference of 1.73 g L⁻¹. Therefore, the REMX biomass density model is a useful management tool for differentiating between low (<2 g L⁻¹), medium (2–4 g L⁻¹), and high densities (>4 g L⁻¹) using the standardised NDVI. This study has demonstrated that there is a predictive relationship between spectral reflectance and the biomass density of U. ohnoi. Notably, the REMX model will inform decision making around the frequency of harvest, as well as the quantity of biomass to be harvested, to maintain an optimal stocking density and, therefore, high productivities at a commercial scale.

对大型陆基大型藻类文化进行二次抽样以估计种群密度是费时，劳动密集型且不准确的。因此，需要开发创新的方法来监测种群密度，以最大化大型藻类的生产力。在这项研究中，Ulva ohnoi的一系列生物量密度（0.5-12.0 g L ^-1）的光谱反射率使用分光辐射计（ASD）和多光谱相机（REMX）进行测量。根据两组数据对通过培养系统深度标准化的两波段归一化植被指数（NDVI）进行了量化，并用于建立生物量密度传感模型。使用ASD和REMX数据开发的模型在标准化NDVI和生物量密度（R ² > 0.85，残留平均标准误差<1.31，平均标准误差<0.99），但回归线的断点和斜率有所不同。随后，使用在4000升高速率藻类池塘中收集的数据验证了这两个模型，这些数据代表了商业陆基大型藻类生产的实际条件。值得注意的是，REMX模型更符合验证数据，实际密度与预测密度的平均差为≤0.76g L ^-1，最大差为1.73 g L ^-1。因此，REMX生物质密度模型是区分低（<2 g L ^-1），中等（2-4 g L ^-1）和高密度（> 4 g L ^-1）的有用管理工具）使用标准化的NDVI。这项研究表明，光谱反射率与大U鱼生物量密度之间存在预测关系。值得注意的是，REMX模型将为围绕收获频率以及要收获的生物量的数量提供决策依据，以保持最佳的库存密度，从而在商业规模上保持高生产率。