Dengia Alemayehu, Dechassa Nigussae, Wogi Lemma, Amsalu Berhanu
Ehiopian Sugar Industry Group, Research and Training, P. O. Box,15, Wonji, Adama, Ethiopia.
Haramaya University, College of Agriculture and Environmental Sciences, Africa Center of Excellence for Climate Smart Agriculture and Biodiversity Conservation, P. O. Box 138, Dire Dawa, Ethiopia.
Heliyon. 2023 Aug 5;9(8):e18982. doi: 10.1016/j.heliyon.2023.e18982. eCollection 2023 Aug.
Drastic and continuous decline in cane yields has become a major threat to sustainable sugarcane production in Ethiopia. Among the causes for the decline are the inefficient and ineffective system of monitoring sugarcane plantations. Adopting satellite-based crop monitoring through the Landviewer platform may circumvent this problem. However, the reliability of vegetation indexes calculated by the platform is unknown and thus requires evaluation. Accordingly, we tested the accuracy of selected Landviewer Calculated Vegetation Indexes (LCVIs) on three major sugarcane varieties and two cropping types. The goodness-of-fit of the sigmoid curve to the LCVIs profile of sugarcane was evaluated. The correlations between LCVIs and yield components, LCVIs and fractional green canopy cover (FGCC), as well as the time-serious Normalized Difference Vegetation Index (NDVI) and yields, were also analysed. We found that the goodness-of-fit of the sigmoid curve was significant ( < 0.001), with 84%-95% accuracy in all the indexes. The majority of LCVIs showed significant ( < 0.05) relationships with yield components and FGCC. The time-series NDVI also demonstrated a significant relationship with cane yield (R = 0.73-0.85) at the age of 10 months and above. The accuracy level of LCVIs varies with varieties and crop types, but the Normalized Difference Phenology Index (NDPI), Soil Adjusted Vegetation Index (SAVI), and NDVI were identified as the most consistent and effective LCVIs for sugarcane monitoring. Therefore, the accuracy of LCVIs was dependable and can be used effectively in monitoring sugarcane plantations to tackle the problem of continuous decline in the yield of the crop.
甘蔗产量急剧持续下降已成为埃塞俄比亚甘蔗可持续生产的重大威胁。产量下降的原因包括甘蔗种植园监测系统效率低下且效果不佳。通过Landviewer平台采用基于卫星的作物监测可能会规避这一问题。然而，该平台计算的植被指数的可靠性未知，因此需要进行评估。据此，我们在三个主要甘蔗品种和两种种植类型上测试了选定的Landviewer计算植被指数（LCVIs）的准确性。评估了S形曲线与甘蔗LCVIs曲线的拟合优度。还分析了LCVIs与产量构成要素、LCVIs与绿色冠层覆盖分数（FGCC）以及时间序列归一化差异植被指数（NDVI）与产量之间的相关性。我们发现S形曲线的拟合优度显著（<0.001），所有指数的准确率在84%-95%之间。大多数LCVIs与产量构成要素和FGCC呈显著（<0.05）关系。在10个月及以上的年龄，时间序列NDVI也与甘蔗产量呈显著关系（R=0.73-0.85）。LCVIs的准确水平因品种和作物类型而异，但归一化差异物候指数（NDPI）、土壤调整植被指数（SAVI）和NDVI被确定为用于甘蔗监测的最一致且有效的LCVIs。因此，LCVIs的准确性是可靠的，可有效用于监测甘蔗种植园，以解决作物产量持续下降的问题。
