Rodrigues Francelino A, Blasch Gerald, Defourny Pierre, Ortiz-Monasterio J Ivan, Schulthess Urs, Zarco-Tejada Pablo J, Taylor James A, Gérard Bruno
International Maize and Wheat Improvement Center-CIMMYT, Texcoco 56237, Mexico;
Food and Rural Development, School of Agriculture, Newcastle University, Newcastle NE1 7RU, UK;
Remote Sens (Basel). 2018;10(6):930. doi: 10.3390/rs10060930. Epub 2018 Jun 12.
This study evaluates the potential of high resolution hyperspectral airborne imagery to capture within-field variability of durum wheat grain yield (GY) and grain protein content (GPC) in two commercial fields in the Yaqui Valley (northwestern Mexico). Through a weekly/biweekly airborne flight campaign, we acquired 10 mosaics with a micro-hyperspectral Vis-NIR imaging sensor ranging from 400-850 nanometres (nm). Just before harvest, 114 georeferenced grain samples were obtained manually. Using spectral exploratory analysis, we calculated narrow-band physiological spectral indices-normalized difference spectral index (NDSI) and ratio spectral index (RSI)-from every single hyperspectral mosaic using complete two by two combinations of wavelengths. We applied two methods for the multi-temporal hyperspectral exploratory analysis: (a) Temporal Principal Component Analysis (tPCA) on wavelengths across all images and (b) the integration of vegetation indices over time based on area under the curve (AUC) calculations. For GY, the best R (0.32) were found using both the spectral (NDSI-, 750 to 840 nm and 720-736 nm) and the multi-temporal AUC exploratory analysis (EVI and OSAVI through AUC) methods. For GPC, all exploratory analysis methods tested revealed (a) a low to very low coefficient of determination (R 0.21), (b) a relatively low overall prediction error (RMSE: 0.45-0.49%), compared to results from other literature studies, and (c) that the spectral exploratory analysis approach is slightly better than the multi-temporal approaches, with early season NDSI of 700 with 574 nm and late season NDSI of 707 with 523 nm as the best indicators. Using residual maps from the regression analyses of NDSIs and GPC, we visualized GPC within-field variability and showed that up to 75% of the field area could be mapped with relatively good predictability (residual class: 0.25 to 0.25%), therefore showing the potential of remote sensing imagery to capture the within-field variation of GPC under conventional agricultural practices.
本研究评估了高分辨率高光谱航空图像在墨西哥西北部 Yaqui 谷地两个商业农田中捕捉硬粒小麦籽粒产量（GY）和籽粒蛋白质含量（GPC）田间变异性的潜力。通过每周/每两周一次的航空飞行活动，我们使用一个 400 - 850 纳米（nm）的微型高光谱可见 - 近红外成像传感器获取了 10 幅镶嵌图像。就在收获前，手动获取了 114 个地理参考籽粒样本。通过光谱探索性分析，我们使用波长的完全两两组合，从每一幅高光谱镶嵌图像中计算窄带生理光谱指数——归一化差异光谱指数（NDSI）和比率光谱指数（RSI）。我们应用了两种多时间高光谱探索性分析方法：（a）对所有图像的波长进行时间主成分分析（tPCA），以及（b）基于曲线下面积（AUC）计算随时间整合植被指数。对于 GY，使用光谱（NDSI - 750 至 840 nm 和 720 - 736 nm）和多时间 AUC 探索性分析（通过 AUC 的 EVI 和 OSAVI）方法都发现了最佳的 R（0.32）。对于 GPC，所有测试的探索性分析方法都显示：（a）决定系数较低至非常低（R≤0.21），（b）与其他文献研究结果相比，总体预测误差相对较低（RMSE：0.45 - 0.49%），以及（c）光谱探索性分析方法略优于多时间方法，700 与 574 nm 的早期季节 NDSI 和 707 与 523 nm 的后期季节 NDSI 为最佳指标。使用 NDSIs 和 GPC 回归分析的残差图，我们可视化了 GPC 的田间变异性，并表明高达 75%的田间区域可以以相对较好的可预测性进行制图（残差等级：0.25 至 0.25%），因此显示了遥感图像在传统农业实践下捕捉 GPC 田间变异的潜力。
