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A multi-source data fusion approach to assess spatial-temporal variability and delineate homogeneous zones: A use case in a table grape vineyard in Greece

机译:一种多源数据融合方法,用于评估时空变化并描绘出均质区域:希腊食用葡萄葡萄园中的一个使用案例

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Precision Viticulture requires very fine-scale spatial and temporal resolution to assess quite accurately variation in a vineyard. Many studies have used proximal sensing technology and spatial-temporal data analysis to characterize the local variation of plant vigour over time. The objective of this study was to present the potential of multivariate geostatistical techniques to fuse multi-temporal data from a multi-band radiometer and a geophysical sensor with different support for delineation of a vineyard into homogeneous zones, to be submitted to differential agricultural management. The study was conducted in a commercial table grape vineyard located in southern Greece during the years 2016 and 2017. Soil electrical conductivity was measured using an EM38 sensor, while Crop Circle canopy sensor, with the sensor located at 1.5 m height from the soil surface and 1.2 m horizontally from the vines, was used for scanning the side canopy area at different crop stages. The temporal multi-sensor data were analysed with the geostatistical data fusion techniques of block cokriging, to produce thematic maps, and factorial block cokriging to estimate synthetic scale-dependent regionalized factors. The factor maps at different scales are characterised by random variability with several micro-structures of different plant and soil properties, which leads to difficulties in delineating macro-areas with homogeneous features. In such conditions, high resolution VRA technology should be preferred to management by homogeneous zones for precision viticulture. The results have shown the potential of the proposed approach to deal with multi-source data in precision viticulture. However, further statistical research on data fusion of the outcomes from different sensors is still needed. (C) 2019 Elsevier B.V. All rights reserved.
机译:精确的葡萄栽培需要非常精细的时空分辨率,以准确评估葡萄园中的变化。许多研究已使用近端传感技术和时空数据分析来表征植物活力随时间的局部变化。这项研究的目的是展示多变量地统计学技术在融合多波段辐射计和地球物理传感器的多时相数据方面的潜力,并为将葡萄园划定为同质区域提供不同的支持,并提交差异农业管理。这项研究是在2016年至2017年期间在希腊南部的一家商用食用葡萄葡萄园中进行的。土壤电导率是使用EM38传感器,作物圆冠层传感器(距土壤表面1.5 m高)测量的。距葡萄树水平1.2 m,用于扫描不同作物生长阶段的侧冠层区域。时态多传感器数据通过块状克里格法的地统计数据融合技术进行分析,以生成主题图,并通过阶乘块状克里金法来估计依赖于比例尺的合成区域因子。不同尺度的因子图的特征是具有不同植物和土壤特性的几种微观结构的随机变异性,这导致难以描绘出具有均一特征的宏观区域。在这种情况下,高分辨率VRA技术应优于均质区管理的精密葡萄栽培技术。结果表明了该方法在精密葡萄栽培中处理多源数据的潜力。但是,仍然需要对来自不同传感器的结果进行数据融合的进一步统计研究。 (C)2019 Elsevier B.V.保留所有权利。

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