Study the response mechanism of Canopy spectral reflectance (CSR) to cotton nitrogen fertilizer, propose the sensitive band and center wavelength of cotton leaf nitrogen content (LNC), and compare the response characteristics of various vegetation indexes to LNC, propose a vegetation index that responds well to LNC and construct estimating model. This experiment sets five nitrogen fertilizer levels, namely N₀ (control), N₁₂₀ (120 kg/hm²), N₂₄₀ (240 kg/hm²), N₃₆₀ (360 kg/hm²), N₄₈₀ (480 kg/hm²). Among them, referring to the conventional nitrogen fertilizer is applied by local farmers (N₃₃₀, 330 kg/hm²). The results showed the following: (1) Visible light and near-infrared (NIR) can be used as two large ranges for precise monitoring of nitrogen, especially the CSR in the NIR range differs significantly under different nitrogen fertilizers. In the early stage of cotton growth, the CSR decreased with the nitrogen application rate increase, in a suitable nitrogen environment (360 kg/hm²), and beyond N₃₆₀, vice versa. In the later growth period, the CSR increases with the increase in nitrogen fertilizer. This trend is most evident in the short-wave NIR regions;(2) the range of 690–709 nm, 717–753 nm, and 940–958, which can be remote sensed by the spectral reflectance when cotton is affected in poor or rich nitrogen. The center wavelength corresponding to the nitrogen-sensitive band, respectively, are 697 nm, 735 nm, 953 nm, the band width can maintain 5–15 nm, generally not more than 20 nm;(3) compared with the ratio vegetation index, difference vegetation index, and normalized vegetation index, the combined vegetation index of more than two bands has a better effect on cotton LNC monitoring, of which the index (R₅₆₀−R₆₇₀)/(R₅₆₀ + R₆₇₀−R₄₅₀), (R₇₀₀−1.7 × R₆₇₀ + 0.7 × R₄₅₀)/(R₇₀₀ + 2.3 × R₆₇₀−1.3 × R₄₅₀) are significantly related to LNC in this papers, and the correlation coefficients can reach, respectively, 0.935^* and 0.936^*. These findings help to estimate the model of LNC. The model is as follows: Y = 19.883 × x + 42.285, where x refers to the combined vegetation index (R₇₀₀−1.7 × R₆₇₀ + 0.7 × R₄₅₀)/(R₇₀₀ + 2.3 × R₆₇₀−1.3 × R₄₅₀), Y is LNC, but the model accuracy will be affected in the crop different phenological stage, and the model has the highest monitoring accuracy during the bud period.

研究冠层光谱反射率（CSR）对棉花氮肥的响应机理，提出棉花叶片氮含量（LNC）的敏感带和中心波长，比较各种植被指数对LNC的响应特征，提出对氮肥响应的植被指数很好地建立了LNC并建立了估计模型。该实验设定了五个氮肥水平，即N ₀（对照），N ₁₂₀（120 kg / hm ²），N ₂₄₀（240 kg / hm ²），N ₃₆₀（360 kg / hm ²），N ₄₈₀（480 kg） / hm ²）。其中，指当地农民施用的常规氮肥（N _330），330 kg / hm ²）。结果表明：（1）可见光和近红外可以作为两个大范围的精确监测氮的方法，特别是在不同氮肥条件下，近红外范围内的CSR差异显着。在棉花生长的早期阶段，在适宜的氮环境下（360 kg / hm ²）且氮含量超过_360时，CSR随施氮量的增加而降低。， 反之亦然。在生育后期，随着氮肥含量的增加，企业社会责任也随之增加。这种趋势在短波近红外区域最为明显；（2）690–709 nm，717–753 nm和940–958的范围，当棉花在贫困地区或贫困地区受到影响时，可以通过光谱反射率遥测到。富含氮。与氮敏感带相对应的中心波长分别为697 nm，735 nm，953 nm，带宽可以保持5–15 nm，一般不超过20 nm；（3）与比率植被指数相比，差异植被指数和归一化植被指数，两个以上波段的组合植被指数对棉花LNC监测效果较好，其中指数（R ₅₆₀ -R ₆₇₀）/（R ₅₆₀  + R _670）-R ₄₅₀），（R ₇₀₀ −1.7×R ₆₇₀  + 0.7×R ₄₅₀）/（R ₇₀₀  + 2.3×R ₆₇₀ −1.3×R ₄₅₀）与LNC显着相关，相关系数可以达到分别为0.935 ^*和0.936 ^*。这些发现有助于估计LNC模型。该模型如下：Y  = 19.883×x + 42.285，其中x表示组合植被指数（R ₇₀₀ −1.7×R ₆₇₀  + 0.7×R ₄₅₀）/（R ₇₀₀  + 2.3×R ₆₇₀ −1.3×R ₄₅₀），Y 是LNC，但是模型准确性会在作物的不同物候期受到影响，并且该模型在芽期具有最高的监测精度。