VEGETATION INDICES FOR IRRIGATED CORN MONITORING

ABSTRACT Monitoring of large agricultural lands is often hampered by data collection logistics at field level. To solve such a problem, remote sensing techniques have been used to estimate vegetation indices, which can subsidize crop management decision-making. Therefore, this study aimed to select vegetation indices to detect variability in irrigated corn crops. Data were collected in São Desidério, Bahia State (Brazil), using an OLI sensor (Operational Land Imager) embedded to a Landsat-8 satellite platform. Five corn growing plots under central pivot irrigation were assessed. The following vegetation indices were tested: NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), SAVI (Soil Adjusted Vegetation Index), GNDVI (Green Normalized Difference Vegetation Index), SR (Simple Ratio), NDWI (Normalized Difference Water Index), and MSI (Moisture Stress Index). Among the tested indices, SR was more sensitive to high corn biomass, while GNDVI, NDVI, EVI, and SAVI were more sensitive to low values. Overall, all indices were found to be concordant with each other, with high correlations among them. Despite this, the use of a set of these indices is advisable since some respond better to certain peculiarities than others.

摘要：大型农业区域的监测工作往往受限于田间级数据采集的后勤瓶颈。为解决此类问题，遥感技术已被用于估算植被指数，以此为作物管理决策提供支撑。据此，本研究旨在筛选可用于检测灌溉玉米田空间变异特征的植被指数。本研究于巴西巴伊亚州圣德塞德里乌开展数据采集工作，采用搭载于陆地卫星8号 (Landsat-8) 平台的陆地成像仪 (Operational Land Imager) 获取数据，共评估了5个采用中心支轴式灌溉的玉米种植地块。本次测试的植被指数包括：归一化差分植被指数 (Normalized Difference Vegetation Index，NDVI)、增强型植被指数 (Enhanced Vegetation Index，EVI)、土壤调节植被指数 (Soil Adjusted Vegetation Index，SAVI)、绿色归一化差分植被指数 (Green Normalized Difference Vegetation Index，GNDVI)、简单比值指数 (Simple Ratio，SR)、归一化差分水体指数 (Normalized Difference Water Index，NDWI) 以及水分胁迫指数 (Moisture Stress Index，MSI)。在受试指数中，简单比值指数 (SR) 对玉米生物量较高的区域更为敏感，而绿色归一化差分植被指数 (GNDVI)、归一化差分植被指数 (NDVI)、增强型植被指数 (EVI) 及土壤调节植被指数 (SAVI) 则对低生物量区域响应更佳。整体而言，所有受试植被指数间均具有较好的一致性，彼此间相关性较强。尽管如此，仍建议联合使用多组植被指数，因不同指数对特定田间异质性的响应能力存在差异。