基于高光谱的水稻叶片含水量监测研究

【目的】建立快速、无损诊断水稻叶片含水量的估测模型，为水稻水分精确管理提供依据。【方法】基于2年不同土壤水分处理和水稻品种的池栽试验，于水稻主要生育时期同步测定顶部4张叶片的光谱反射率和含水量，系统分析350-2 500 nm波段范围内任意两波段组合而成的比值（RSI）、归一化差值（NDSI）及差值（DSI）光谱指数，并分析其与叶片含水量的量化关系。【结果】不同土壤水分处理和叶位间，叶片反射光谱具有显著的时空变化特征，叶片含水量的敏感光谱波段主要位于近红外及短波红外区域；RSI （R1402, R2272）及NDSI （R1402, R2272）光谱指数与叶片含水量呈现良好的线性相关，线性拟合R2均达到0.80。基于独立试验资料对所建模型进行测试检验也显示，预测值和观察值的拟合R2也均达到0.86。【结论】RSI（R1402, R2272）、NDSI（R1402, R2272）均可用于水稻叶片含水量的定量监测。

Monitoring Leaf Water Content Based on Hyperspectra in Rice

【Objective】 The objective of the experiments is to develop a key method for fast and nondestructive monitoring leaf water content(LWC) in rice(Oryza sativa L.).【Method】 Two field experiments were conducted with different soil water conditions and rice cultivars across two growing seasons,and time-course measurements were taken on leaf hyperspectral reflectance and LWC at top four leaves over main growth stages.Several kinds of hyperspectral indices at leaf scale including ratio spectral indices(RSI),normalized difference spectral indices(NDSI) and difference spectral indices(DSI) with all combinations of two wavebands between 350 and 2 500 nm were calculated,and their relationships to LWC were analyzed.【Result】 The results indicated that the leaf spectral reflectance varied distinctly with soil water treatments and different top leaves,the sensitivity bands mostly occured within near-infrared and short-infrared spectral regions.The spectral indices as RSI(R1402,R2272) and NDSI(R1402,R2272) were linear with LWC,giving the determination coefficient of linear regression(S-R2) of 0.80,and the predicted R2(P-R2) based on the testing performance with independent datasets as 0.86.【Conclusion】 It is concluded that the RSI(R1402,R2272) and NDSI(R1402,R2272) can be used to monitor leaf water content in rice.