Betalain (Bt) is as a class of edible natural pigments and important biochemical aparmeter can explain the physiological response and the resistance of plants caused by different environmental stress factors. Hyperspectral spectroscopy has been widely used to estimate plant pigments contents accurately and non-destructively. However, there is very little research about hyperspectral remote sensing estimation and inversion of betalain contents. The algorithm of Bt content inversion with viable universality is the key to improving the practicality of quantitative remote sensing. Therefore, in this study, based on the radioactive transfer mechanism and SVM model, the optical characteristics of Bt and other factors of Suaeda salsa are analyzed, and a coupling model (PROSAIL + SVM) of simulated canopy reflectivity and factor contents is established. The model was then applied to the remote sensing inversion. Results demonstrated that Bt content was sensitive to the spectral range between 460 and 592 nm, particularly between 530 and 550 nm where the sensitivity index reached 0.7. Optimized indices; NDSI_473nm,475nm, RSI_473nm,475nm and NDPI_473nm,475nm, calculated by simulated spectral reflectance had a significant correlation with Bt content (R= ±0.80, p < 0.001). Moreover, simulated Sentinel-2A reflectance of the Blue (B2) and Green (B3) bands showed sensitivity of 66% and 68%, respectively. Spectral indices NDPI _{b2, b3}, NDSI _{b2, b3}, RSI_{b2, b3} showed correlation of ± 0.71. The PROSAIL + SVM model developed from simulated hyperspectral reflectance, estimated Bt content with high R² (0.82), indicating that the precision of the model was higher and the universality was stronger. A model composed of optimized indices (NDSI_{473nm, 475nm}, RSI_{473nm, 475nm} and NDPI_{473nm, 475nm}) showed promising estimation ability with low RMSE (0.39 μg·cm⁻²) with R² = 0.78 and RPD = 1.99. When the PROSAIL + SVM model was extended to multispectral images (Sentinel-2A), the model estimated the Bt content at moderate to good levels (R² = 0.68). The results indicated that optimized hyperspectral and multispectral NDPI, NDSI, RSI have significant correlation with betalain content of S. salsa, suggesting these indices might be used to quantify Bt content with high accuracy, thereby alleviating the ill-posed inverse problem and improving sensitivity. Not only would this enhance the application of quantitative remote sensing technology in betalain research, but the results also pose positive application for precision agriculture in detecting Bt pigments in crops, as a surrogate for evaluating the health condition of crops.

Betalain（Bt）是一类可食用的天然色素，重要的生化参数可以解释由不同环境胁迫因素引起的植物的生理反应和抗性。高光谱光谱已被广泛用于准确无损地估算植物色素的含量。但是，关于高光谱遥感估计和β胶质含量反演的研究很少。具有通用性的Bt含量反演算法是提高定量遥感实用性的关键。因此，在本研究中，基于放射性转移机理和支持向量机模型，对盐碱地碱蓬的Bt光学特性及其他因素进行了研究。通过分析，建立了模拟冠层反射率与因子含量的耦合模型（PROSAIL + SVM）。然后将该模型应用于遥感反演。结果表明，Bt含量对460至592 nm的光谱范围敏感，特别是在530至550 nm的光谱范围内，敏感指数达到0.7。优化指标；通过模拟光谱反射率计算得出的NDSI _{473nm，475nm}，RSI _{473nm，475nm}和NDPI _{473nm，475nm}与Bt含量具有显着相关性（R =±0.80，p  <0.001）。此外，蓝色（B2）和绿色（B3）波段的模拟Sentinel-2A反射率分别显示出66％和68％的灵敏度。光谱指数NDPI _b2，b3，NDSI_b2，b3，RSI _b2，b3显示为±0.71的相关性。PROSAIL + SVM模型是通过模拟的高光谱反射率开发的，估计的Bt含量具有较高的R ²（0.82），表明该模型的精度更高，通用性更强。由优化指标（NDSI _{473nm，475nm}，RSI _{473nm，475nm}和NDPI _{473nm，475nm}）组成的模型显示出有希望的估计能力，具有低RMSE（_0.39μg ·cm ^-2），R ²  = 0.78，RPD = 1.99。当PROSAIL + SVM模型扩展到多光谱图像（Sentinel-2A）时，该模型估计Bt含量处于中等至良好水平（R ² = 0.68）。结果表明，高光谱优化和多光谱NDPI，NDSI，RSI具有的甜菜素含量显著相关碱蓬，提示这些指标可以被用来量化的Bt内容以高准确度，从而减轻不适定反演问题和提高灵敏度。这不仅会增强定量遥感技术在甜菜碱研究中的应用，而且该结果也对精密农业在检测作物中的Bt色素方面有积极的应用，作为评估作物健康状况的替代方法。