Olive groves, often characterized by complex topography and highly variable soils, present challenges for delineating irrigation management zones (MZs). This study addresses this issue by examining the relevance of apparent electrical conductivity (ECa), elevation (Z), topographic wetness index (TWI) and time-series of Sentinel-2 NDVI imagery for delimiting MZs for variable rate irrigation (VRI) in a 40-ha olive grove in southern Spain. Principal Component Analysis (PCA) was employed to disentangle olive and grass cover NDVI patterns. PC1 represented the olive tree development patten and showed little relationship with soil properties, while PC2 was associated with the grass cover growth pattern and considered a proxy for water storage-related soil properties that are relevant for irrigation scheduling. An alternative analysis using NDVI percentiles yielded similar results but favored PCA for distinguishing between grass cover and olive tree development patterns. Correlation between NDVI and ECa varied seasonally (r > 0.60), driven by the grass cover dynamics. To assess also possible non-linear relationships, regression trees were used to estimate NDVI percentiles, emphasizing the importance of ECa, ECa_ratio, Z, and slope in predicting different NDVI percentiles. Fuzzy k-means zoning using ECa + Z resulted in four classes that best classified variables that are relevant for irrigation scheduling due to their relationship with soil water storage (e.g. clay content, P_0.95 and PC2). Zonings based on ECa, ECa + Z + TWI and ECa + Z + TWI + NDVI yielded two zones that classified P_0.95 and PC2 well, but not clay content. Therefore, the zoning based on ECa + Z was chosen as optimal in the context of this VRI applications. Our analysis showed how NDVI series can be used in combination with ECa and elevation to evaluate the effectiveness of different zoning approaches for developing VRI prescriptions in olive groves.

橄榄树林通常具有复杂的地形和高度变化的土壤，这给划定灌溉管理区 (MZ) 带来了挑战。本研究通过检查表观电导率 (ECa)、海拔 (Z)、地形湿度指数 (TWI) 和 Sentinel-2 NDVI 图像时间序列的相关性来解决这个问题，以界定可变速率灌溉 (VRI) 的 MZ。西班牙南部 40 公顷的橄榄园。主成分分析 (PCA) 用于理清橄榄树和草覆盖的 NDVI 模式。 PC1 代表橄榄树发育模式，与土壤特性关系不大，而 PC2 与草覆盖生长模式相关，并被认为是与灌溉计划相关的与储水相关的土壤特性的代表。使用 NDVI 百分位数的替代分析产生了类似的结果，但有利于 PCA 来区分草覆盖和橄榄树的发育模式。 NDVI 和 ECa 之间的相关性随季节变化 ( r > 0.60)，由草覆盖动态驱动。为了评估可能的非线性关系，使用回归树来估计 NDVI 百分位数，强调 ECa、ECa_比率、Z 和斜率在预测不同 NDVI 百分位数中的重要性。使用 ECa + Z 的模糊 k 均值分区产生了四个类别，这些变量由于与土壤储水量的关系（例如粘土含量，P _0.95和 PC2）而与灌溉计划相关，因此得到了最佳分类变量。基于 ECa、ECa + Z + TWI 和 ECa + Z + TWI + NDVI 的分区产生了两个分区，它们对 P _0.95和 PC2 进行了很好的分类，但没有对粘土含量进行分类。 因此，在该 VRI 应用中，基于 ECa + Z 的分区被选为最佳选择。我们的分析显示了如何将 NDVI 系列与 ECa 和海拔结合使用，以评估不同分区方法在橄榄园中开发 VRI 处方的有效性。