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：针对大型农用地的监测工作，常受限于田间级数据采集的后勤瓶颈。为解决此类痛点，遥感技术已被广泛用于估算植被指数，为作物管理决策提供支撑。据此，本研究旨在筛选可识别灌溉玉米田空间变异性的植被指数。本研究的数据采集于巴西巴伊亚州圣德塞德里乌地区，采用搭载于Landsat-8卫星平台的OLI传感器（陆地成像仪，Operational Land Imager）完成，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：NDVI（归一化差异植被指数，Normalized Difference Vegetation Index）、EVI（增强型植被指数，Enhanced Vegetation Index）、SAVI（土壤调节植被指数，Soil Adjusted Vegetation Index）、GNDVI（绿色归一化差异植被指数，Green Normalized Difference Vegetation Index）、SR（简单比值指数，Simple Ratio）、NDWI（归一化差异水指数，Normalized Difference Water Index）以及MSI（水分胁迫指数，Moisture Stress Index）。测试结果表明，SR对玉米高生物量表现出更高的敏感性，而GNDVI、NDVI、EVI及SAVI则对低数值区间响应更优。整体来看，所有被测植被指数间均具有良好的一致性，彼此间相关性较强。尽管如此，仍建议联合使用多组该类植被指数，因不同指数对特定农田特征的响应效果存在差异。