The use of super-intensive orchards is a growing trend in fruit production. The present study aims to improve management of these cropping systems by focusing on how agronomic decisions impact orchard dynamics in the short to medium term and by providing a decision-support approach based on stable temporal patterns from previous seasons. A multitemporal study using remote sensing and LiDAR was conducted in a commercial almond orchard over four growing seasons (2019–2022) to determine the optimal timing of image acquisition for variable pre-harvest treatments. A model-based clustering () was applied to optimal Sentinel-2 NDVI maps and apparent soil electrical conductivity (ECa) data, interpolated to the pixel centroids of Sentinel-2 image grids, to delineate potential management zones (PMZs). The leafiness-LiDAR index (LLI), a leaf area index (LAI) estimator, was obtained as ground truth after summer pruning and before harvesting, showing a significant influence of fertigation and pruning on the LAI, with summer pruning particularly influencing orchard dynamics. The optimal time for NDVI mapping was found to be two months after summer pruning in productive years and two weeks after in unproductive years. The delineated PMZs were consistent across seasons and corresponded to significant LAI differences. This method could contribute to improving resource management and sustainability in super-intensive commercial orchards.

中文翻译：

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Leafiness-LiDAR指数和NDVI用于识别超集约杏仁园的时间模式以响应不同的管理策略


超集约化果园的使用是水果生产的增长趋势。本研究旨在通过关注农艺决策如何在中短期内影响果园动态，并提供基于前几季稳定时间模式的决策支持方法，来改善这些种植系统的管理。使用遥感和激光雷达在商业杏仁园中进行了四个生长季节（2019-2022）的多时相研究，以确定可变收获前处理的图像采集的最佳时机。基于模型的聚类 () 应用于最佳 Sentinel-2 NDVI 地图和表观土壤电导率 (ECa) 数据，插值到 Sentinel-2 图像网格的像素质心，以描绘潜在管理区 (PMZ)。叶量-LiDAR 指数 (LLI) 是一种叶面积指数 (LAI) 估计量，在夏季修剪后和收获前作为地面实况获得，显示灌溉施肥和修剪对 LAI 的显着影响，其中夏季修剪尤其影响果园动态。研究发现，进行 NDVI 制图的最佳时间是丰产年份夏季修剪后两个月，非丰产年份则为夏季修剪后两周。所描绘的 PMZ 在各个季节都是一致的，并且对应于显着的 LAI 差异。这种方法有助于改善超集约商业果园的资源管理和可持续性。