The spectral range <1,000 nm is commonly used for estimating the chlorophyll content of plants. However, the influence of the response band of leaf water content in the Short-Wave InfraRed on the chlorophyll estimate model is uncertain. We measured the Visible-Near InfraRed-Short Wave InfraRed spectral reflectance and SPAD values of rice (Oryza sativa L.) leaves at different growth stages. The vegetation indices (VIs), red edge positions (REPs), and spectral characteristic parameters (SCPs) were calculated. The correlation between the above parameters and SPAD was analyzed. The effects of chlorophyll and water content on the spectral reflectance absorption characteristics of rice leaves were analyzed by the PROSPECT model. SPAD spectral estimation models were established with different inputs using the Random Forest (RF) regression algorithm. Our results are described as follows: (a) The spectral reflectance after continuum removal of rice leaves was highly correlated with SPAD in 600–690, 720–760, 1,400–1,490, and 1,900–1,980 nm. (b) Compared with VIs and REPs, the SCPs after continuum removal including the Depth (D1) and Area (A1, A2) parameters had the highest correlation with SPAD. (c) By taking into account the influence of leaf water content (LWC), the accuracy of SPAD estimation may be enhanced. The RF SPAD estimate model with SCPs of all absorption valley as inputs performed the best. The findings reveal on the mechanism of the influence of LWC on the accuracy of chlorophyll spectra estimation. It provides technical support for estimating SPAD in rice leaves with high accuracy and for developing chlorophyll tachistoscopes.

中文翻译：

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考虑叶片含水量影响提高水稻叶片SPAD光谱估计精度

<1,000 nm 的光谱范围通常用于估算植物的叶绿素含量。然而，短波红外中叶片含水量的响应带对叶绿素估计模型的影响尚不确定。我们测量了水稻（ Oryza sativa）的可见-近红外-短波红外光谱反射率和 SPAD 值L.) 叶子处于不同的生长阶段。计算了植被指数 (VI)、红边位置 (REP) 和光谱特征参数 (SCP)。分析了上述参数与SPAD的相关性。利用PROSPECT模型分析了叶绿素和水分含量对水稻叶片光谱反射吸收特性的影响。使用随机森林 (RF) 回归算法建立具有不同输入的 SPAD 光谱估计模型。我们的结果描述如下：(a) 水稻叶片连续去除后的光谱反射率与 SPAD 在 600-690、720-760、1,400-1,490 和 1,900-1,980 nm 高度相关。(b) 与 VI 和 REP 相比，去除连续体后的 SCP，包括深度 (D1) 和面积 (A1, A2) 参数与 SPAD 的相关性最高。(c) 通过考虑叶片含水量 (LWC) 的影响，可以提高 SPAD 估计的准确性。以所有吸收谷的 SCP 作为输入的 RF SPAD 估计模型表现最好。研究结果揭示了 LWC 对叶绿素光谱估计精度的影响机制。为高精度估算水稻叶片SPAD和研制叶绿素速光仪提供了技术支持。