Yao Xia, Jia Wenqing, Si Haiyang, Guo Ziqing, Tian Yongchao, Liu Xiaojun, Cao Weixing, Zhu Yan
National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing, Jiangsu, P. R. China.
PLoS One. 2014 Jun 10;9(6):e96352. doi: 10.1371/journal.pone.0096352. eCollection 2014.
Leaf equivalent water thickness (LEWT) is an important indicator of crop water status. Effectively monitoring the water status of wheat under different nitrogen treatments is important for effective water management in precision agriculture. Trends in the variation of LEWT in wheat plants during plant growth were analyzed based on field experiments in which wheat plants under various water and nitrogen treatments in two consecutive growing seasons. Two-band spectral indices [normalized difference spectral indices (NDSI), ratio spectral indices (RSI), different spectral indices (DSI)], and then three-band spectral indices were established based on the best two-band spectral index within the range of 350-2500 nm to reduce the noise caused by nitrogen and saturation. Then, optimal spectral indices were selected to construct models of LEWT monitoring in wheat. The results showed that the two-band spectral index NDSI(R1204, R1318) could be used for LEWT monitoring throughout the wheat growth season, but the model performed differently before and after anthesis. Therefore, further two-band spectral indices NDSIb(R1445, R487), NDSIa(R1714, R1395), and NDSI(R1429, R416), were constructed for the two developmental phases, with NDSI(R1429, R416) considered to be the best index. Finally, a three-band index (R1429-R416-R1865)/(R1429+R416+R1865), which was superior for monitoring LEWT and reducing the noise caused by nitrogen, was formed on the best two-band spectral index NDSI(R1429, R416) by adding the 1,865 nm wavelenght as the third band. This produced more uniformity and stable performance compared with the two-band spectral indices in the LEWT model. The results are of technical significance for monitoring the water status of wheat under different nitrogen treatments in precision agriculture.
叶片等效水厚度（LEWT）是作物水分状况的重要指标。有效监测不同氮处理下小麦的水分状况对于精准农业中的有效水分管理至关重要。基于连续两个生长季对处于各种水分和氮处理下的小麦植株进行的田间试验，分析了小麦植株生长期间LEWT的变化趋势。建立了双波段光谱指数[归一化差异光谱指数（NDSI）、比值光谱指数（RSI）、差异光谱指数（DSI）]，然后基于350 - 2500 nm范围内最佳的双波段光谱指数建立了三波段光谱指数，以减少氮素和饱和度引起的噪声。然后，选择最佳光谱指数构建小麦LEWT监测模型。结果表明，双波段光谱指数NDSI（R1204，R1318）可用于整个小麦生长季的LEWT监测，但该模型在开花前后表现不同。因此，针对两个发育阶段构建了进一步的双波段光谱指数NDSIb（R1445，R487）、NDSIa（R1714，R1395）和NDSI（R1429，R416），其中NDSI（R1429，R416）被认为是最佳指数。最后，在最佳双波段光谱指数NDSI（R1429，R416）的基础上，通过添加1865 nm波长作为第三波段，形成了一个三波段指数（R1429 - R416 - R1865）/（R1429 + R416 + R1865），该指数在监测LEWT和减少氮素引起的噪声方面表现更优。与LEWT模型中的双波段光谱指数相比，其具有更高的一致性和更稳定的性能。这些结果对于精准农业中监测不同氮处理下小麦的水分状况具有技术意义。
