Relevance of NDVI, soil apparent electrical conductivity and topography for variable rate irrigation zoning in an olive grove

IF 5.4 2区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Precision Agriculture Pub Date : 2024-09-27 DOI:10.1007/s11119-024-10191-4

K. Vanderlinden, G. Martínez, M. Ramos, L. Mateos

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Abstract

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.

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NDVI、土壤表观导电率和地形与橄榄园变率灌溉分区的相关性

橄榄园通常地形复杂，土壤多变，给灌溉管理区（MZ）的划分带来了挑战。本研究通过研究表观导电率 (ECa)、海拔 (Z)、地形湿润指数 (TWI) 和哨兵-2 NDVI 图像的时间序列的相关性来解决这一问题，从而在西班牙南部一片 40 公顷的橄榄园中为变率灌溉 (VRI) 划定灌溉管理区。采用主成分分析法（PCA）来区分橄榄树和草地植被的 NDVI 模式。PC1 代表了橄榄树的生长模式，与土壤特性关系不大，而 PC2 则与草地植被的生长模式有关，被认为是储水相关土壤特性的代表，与灌溉调度有关。使用归一化差异植被指数百分位数进行的另一种分析也得出了类似的结果，但 PCA 更适合区分草地植被和橄榄树的生长模式。NDVI 和 ECa 之间的相关性随季节而变化（r > 0.60），这是由草覆盖的动态变化所驱动的。为了评估可能的非线性关系，还使用回归树来估计 NDVI 百分位数，强调 ECa、ECaratio、Z 和斜率在预测不同 NDVI 百分位数方面的重要性。使用 ECa + Z 进行模糊 K-均值分区得出了四个类别，这些类别对灌溉调度相关变量（如粘土含量、P0.95 和 PC2）的分类效果最佳。基于 ECa、ECa + Z + TWI 和 ECa + Z + TWI + NDVI 的分区产生了两个能很好地分类 P0.95 和 PC2 的分区，但不能很好地分类粘土含量。因此，在此次 VRI 应用中，基于 ECa + Z 的分区被选为最佳分区。我们的分析表明了如何将 NDVI 序列与 ECa 和海拔高度结合使用，以评估不同分区方法在制定橄榄园 VRI 方针方面的有效性。

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来源期刊

Precision Agriculture 农林科学-农业综合

CiteScore

12.30

自引率

8.10%

发文量

103

审稿时长

>24 weeks

期刊介绍： Precision Agriculture promotes the most innovative results coming from the research in the field of precision agriculture. It provides an effective forum for disseminating original and fundamental research and experience in the rapidly advancing area of precision farming. There are many topics in the field of precision agriculture; therefore, the topics that are addressed include, but are not limited to: Natural Resources Variability: Soil and landscape variability, digital elevation models, soil mapping, geostatistics, geographic information systems, microclimate, weather forecasting, remote sensing, management units, scale, etc. Managing Variability: Sampling techniques, site-specific nutrient and crop protection chemical recommendation, crop quality, tillage, seed density, seed variety, yield mapping, remote sensing, record keeping systems, data interpretation and use, crops (corn, wheat, sugar beets, potatoes, peanut, cotton, vegetables, etc.), management scale, etc. Engineering Technology: Computers, positioning systems, DGPS, machinery, tillage, planting, nutrient and crop protection implements, manure, irrigation, fertigation, yield monitor and mapping, soil physical and chemical characteristic sensors, weed/pest mapping, etc. Profitability: MEY, net returns, BMPs, optimum recommendations, crop quality, technology cost, sustainability, social impacts, marketing, cooperatives, farm scale, crop type, etc. Environment: Nutrient, crop protection chemicals, sediments, leaching, runoff, practices, field, watershed, on/off farm, artificial drainage, ground water, surface water, etc. Technology Transfer: Skill needs, education, training, outreach, methods, surveys, agri-business, producers, distance education, Internet, simulations models, decision support systems, expert systems, on-farm experimentation, partnerships, quality of rural life, etc.