Sweet potato is a tuberous root with versatility in food products, but also with applications in the energy industry, such as in ethanol production. Developing mechanisms to assess the performance of this crop is important, difficult, and costly, as its commercial product grows below ground. The use of remote sensing to evaluate the development of sweet potato has not yet been reported in the literature. In our study, we showed that spectral vegetation indices are good proxies to monitor the temporal dynamics of crop growth and differentiate phenological stages, regardless of the growing season. The development phases were divided into three stages according to the vegetation indices: (I) initial stage (<200 GDD), when vegetation has little influence on VIs; (II) growth stage (from 200 to 500 GDD), when vegetation has high influence on VIs due to its growth; and (III) stabilization stage (> 500 GDD), when major changes in VIs no longer occur because vegetative growth has ceased. Besides that, we found that these indices can predict crop yield before harvest. In two growing seasons, the smallest errors in yield estimates occurred during the growth stage. In the summer season with NDVI at 355 GDD with errors of 2.63 t ha⁻¹ and in the winter season when GNDVI at 440 GDD had errors of 3.06 t ha⁻¹.

甘薯是一种块根，在食品中具有多种用途，但也可用于能源工业，例如乙醇生产。由于其商业产品在地下生长，因此开发评估这种作物性能的机制非常重要、困难且成本高昂。文献中尚未报道利用遥感评价甘薯的发育情况。在我们的研究中，我们表明光谱植被指数是监测作物生长的时间动态和区分物候阶段的良好代理，无论生长季节如何。根据植被指数将发育阶段分为三个阶段：（I）初始阶段（<200 GDD），植被对VIs影响不大；(II) 成长期（GDD 200 到 500），当植被因其生长而对 VI 有很大影响时；(III) 稳定阶段（> 500 GDD），此时 VI 不再发生重大变化，因为营养生长已经停止。除此之外，我们发现这些指数可以在收获前预测作物产量。在两个生长季节中，产量估计的最小误差发生在生长阶段。在夏季，NDVI 为 355 GDD，误差为 2.63 t ha^-1和在 GNDVI 在 440 GDD 的冬季有 3.06 t ha ^{-1 的}误差。