Information about pigment and water contents provides comprehensive insights for evaluating photosynthetic potential and activity of agricultural crops. In this study, we present the concept of using spectral integral ratios (SIR) to retrieve three biochemical traits, namely chlorophyll a and b (C_ab), carotenoids (C_cx), and water (C_w) content, simultaneously from hyperspectral measurements in the wavelength range 460−1100 nm. The SIR concept is based on automatic separation of respective absorption features through local peak and intercept analysis between log-transformed reflectance and convex hulls. The algorithm was tested on two synthetically established databases using a physiologically constrained look-up-table (LUT) generated by (i) the leaf optical properties model PROSPECT and (ii) the canopy radiative transfer model (RTM) PROSAIL. LUT constraints were realized based on natural C_cx-C_ab relations and green peak locations identified in the leaf optical database ANGERS. Linear regression between obtained SIRs and model parameters resulted in coefficients of determination (R²) of 0.66 (i and ii) for C_cx, R² = 0.85 (i) and 0.53 (ii) for C_ab, and R² = 0.97 (i) and 0.67 (ii) for C_w, respectively. Using the model established from the PROSPECT LUT, leaf level validation was carried out based on ANGERS data with reasonable results both in terms of goodness of fit and root mean square error (RMSE) (C_cx: R² = 0.86, RMSE = 2.1 μg cm⁻²; C_ab: R² = 0.67, RMSE = 12.5 μg cm^-2; C_w: R² = 0.89, RMSE = 0.007 cm). The algorithm was applied to airborne spectrometric HyMap data acquired on 12th July 2003 in Barrax, Spain and to AVIRIS-NG data recorded on 2nd July 2018 southwest of Munich, Germany. Mapping of the SIR results as multiband images (3-segment SIR) allows for intuitive visualization of dominant absorptions with respect to the three considered biochemical variables. Barrax in situ validation using linear regression models derived from PROSAIL LUT showed satisfactory results regarding C_ab (R² = 0.84; RMSE = 9.06 μg cm^-2) and canopy water content (CWC, R² = 0.70; RMSE = 0.05 cm). Retrieved C_cx values were reasonable according to C_ab-C_cx-dependence plausibility analysis. Hence, the presented SIR algorithm allows for computationally efficient and RTM supported robust retrievals of the two most important vegetation pigments as well as of water content and is ready to be applied on satellite imaging spectroscopy data available in the near future. The algorithm is publicly available as an interface supported tool within the 'Agricultural Applications' of the EnMAP-Box 3 hyperspectral remote sensing software suite.

有关色素和水含量的信息为评估农作物的光合作用潜力和活性提供了全面的见解。在这项研究中，我们提出了使用光谱积分比（SIR）检索三个生化特征的概念，即叶绿素a和b（C _ab），类胡萝卜素（C _cx）和水（C _w）含量，同时来自460-1100 nm波长范围的高光谱测量。SIR概念基于通过峰的自动分离各个吸收特征以及对数转换反射率和凸包之间的截距分析。使用由（i）叶片光学特性模型PROSPECT和（ii）冠层辐射传递模型（RTM）PROSAIL生成的生理约束查找表（LUT），在两个综合建立的数据库上对该算法进行了测试。LUT约束是基于自然C _cx -C _ab关系和在叶片光学数据库ANGERS中标识的绿色峰位置实现的。获得的SIR与模型参数之间的线性回归得出C的确定系数（R²）为0.66（i和ii）_cx， 对于C _ab，R ² = 0.85（i）和0.53（ii）， 对于C _w，R ^2分别为0.97（i）和0.67（ii）。使用从PROSPECT LUT建立的模型，基于ANGERS数据进行叶水平验证，得出关于拟合优度和均方根误差（RMSE）的合理结果（C _cx：R ²  = 0.86，RMSE = 2.1μg cm ^-2； C _ab：R ²  = 0.67，RMSE = 12.5μgcm ^-2； C _w：R ² = 0.89，RMSE = 0.007厘米）。该算法已应用于2003年7月12日在西班牙Barrax获得的机载光谱HyMap数据以及2018年7月2日在德国慕尼黑西南部记录的AVIRIS-NG数据。将SIR结果映射为多波段图像（三段SIR）可以直观地可视化三个已考虑到的生化变量的主要吸收。使用源自PROSAIL LUT的线性回归模型进行的Barrax原位验证显示出关于C _ab（R ²  = 0.84; RMSE = 9.06μgcm ^-2）和冠层含水量（CWC，R ²  = 0.70; RMSE = 0.05 cm）的令人满意的结果。根据C _ab -C，检索到的C _cx值是合理的_cx-依赖似然性分析。因此，提出的SIR算法可对两种最重要的植被色素以及水含量进行有效的计算和RTM支持的可靠检索，并准备在不久的将来应用于卫星成像光谱数据。该算法可作为EnMAP-Box 3高光谱遥感软件套件的“农业应用”中的接口支持工具公开使用。