Assessing the effectiveness of satellite and UAV-based remote sensing for delineating alfalfa management zones under heterogeneous rootzone soil salinity

IF 6.3 Q1 AGRICULTURAL ENGINEERING Smart agricultural technology Pub Date : 2024-09-28 DOI:10.1016/j.atech.2024.100583
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Abstract

Site-specific application of agricultural inputs is crucial for optimizing resource utilization in alfalfa (Medicago sativa L.) production and addressing challenges such as soil salinity. The main objective of this study was to assess the effectiveness of PlanetScope and UAV-based NDVI imagery for delineating alfalfa management zones under heterogeneous rootzone soil salinity. The research was conducted in the alfalfa field located in Imperial Valley, CA. The extent of rootzone soil salinity was assessed using Electromagnetic induction (EMI) technology and deep soil sampling. Reference management zones were then defined using the soil salinity (ECe) map derived from apparent electrical conductivity (ECa) data. Additionally, a time series of NDVI images from PlanetScope imagery and an NDVI image captured using an unmanned aerial vehicle were used to delineate remote sensing-based management zones. Laboratory analysis of disturbed soil samples collected at various depths provided soil physicochemical property data. Soil salinity of the samples ranged from 2.2 to 13.4 dS m−1 with a moderate level of variability (CV = 37.7 %). ECe-based management zones accounted for approximately 83 % of the field's variability and exhibited substantial differentiation among delineated zones concerning diverse soil properties, including ECa, ECe, gravimetric water content, Mg2+, boron, Ca2+, Na+, and Cl. Notably, NDVI images effectively captured field variability on par with ECe-based zoning. Moreover, NDVI images recommended the same optimal number of zones (i.e., three) to address the field's variability, aligning with the ECe-based zoning approach. Our findings highlight that heterogeneity of soil salinity in the root zone primarily impacts the variability of alfalfa NDVI early in the growing season. Consequently, this early stage emerges as the most opportune timeframe for NDVI-based zoning for rapid assessment of rootzone soil salinity concerns.
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评估卫星和无人机遥感技术在不同根区土壤盐度条件下划定紫花苜蓿管理区的有效性
在紫花苜蓿(Medicago sativa L.)生产中,针对具体地点应用农业投入对于优化资源利用和应对土壤盐碱化等挑战至关重要。本研究的主要目的是评估 PlanetScope 和基于无人机的 NDVI 图像在不同根区土壤盐度条件下划分苜蓿管理区的有效性。研究在加利福尼亚州帝王谷的紫花苜蓿田中进行。利用电磁感应(EMI)技术和深层土壤取样评估了根区土壤盐碱化的程度。然后,利用表观电导率(ECa)数据得出的土壤盐分(ECe)图确定了参考管理区。此外,还利用 PlanetScope 图像中的 NDVI 图像时间序列和无人机拍摄的 NDVI 图像来划分基于遥感的管理区。对不同深度采集的受扰动土壤样本进行的实验室分析提供了土壤理化性质数据。样本的土壤盐度从 2.2 到 13.4 dS m-1 不等,变异性中等(CV = 37.7 %)。基于 ECe 的管理区约占田间变异性的 83%,并在划定的各区之间就不同的土壤特性(包括 ECa、ECe、重力含水量、Mg2+、硼、Ca2+、Na+ 和 Cl-)表现出很大的差异。值得注意的是,NDVI 图像能有效捕捉田间变化,与基于 ECe 的分区相当。此外,NDVI 图像推荐了相同的最佳分区数量(即三个)来处理田间的变异性,这与基于 ECe 的分区方法一致。我们的研究结果突出表明,根区土壤盐分的异质性主要影响生长季早期紫花苜蓿 NDVI 的变化。因此,在这一早期阶段,基于 NDVI 的分区是快速评估根区土壤盐分问题的最佳时机。
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