A new method for satellite-based remote sensing analysis of plant-specific biomass yield patterns for precision farming applications

IF 5.4 2区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Precision Agriculture Pub Date : 2024-04-28 DOI:10.1007/s11119-024-10144-x
Ludwig Hagn, Johannes Schuster, Martin Mittermayer, Kurt-Jürgen Hülsbergen
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Abstract

This study describes a new method for satellite-based remote sensing analysis of plant-specific biomass yield patterns for precision farming applications. The relative biomass potential (rel. BMP) serves as an indicator for multiyear stable and homogeneous yield zones. The rel. BMP is derived from satellite data corresponding to specific growth stages and the normalized difference vegetation index (NDVI) to analyze crop-specific yield patterns. The development of this methodology is based on data from arable fields of two research farms; the validation was conducted on arable fields of commercial farms in southern Germany. Close relationships (up to r > 0.9) were found between the rel. BMP of different crop types and study years, indicating stable yield patterns in arable fields. The relative BMP showed moderate correlations (up to r = 0.64) with the yields determined by the combine harvester, strong correlations with the vegetation index red edge inflection point (REIP) (up to r = 0.88, determined by a tractor-mounted sensor system) and moderate correlations with the yield determined by biomass sampling (up to r = 0.57). The study investigated the relationship between the rel. BMP and key soil parameters. There was a consistently strong correlation between multiyear rel. BMP and soil organic carbon (SOC) and total nitrogen (TN) contents (r = 0.62 to 0.73), demonstrating that the methodology effectively reflects the impact of these key soil properties on crop yield. The approach is well suited for deriving yield zones, with extensive application potential in agriculture.

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用于精准农业应用的特定植物生物量产量模式卫星遥感分析新方法
本研究介绍了一种基于卫星遥感分析精准农业应用中特定植物生物量产量模式的新方法。相对生物量潜能值(rel. BMP)是多年稳定和均匀产量区的指标。相对生物量潜能值来自与特定生长阶段相对应的卫星数据和归一化差异植被指数(NDVI),用于分析特定作物的产量模式。该方法的开发基于两个研究农场的耕地数据;验证则在德国南部商业农场的耕地上进行。不同作物类型和研究年份的相对 BMP 之间的关系密切(r > 0.9),表明耕地的产量模式稳定。相对 BMP 与联合收割机测定的产量呈中度相关(最高 r = 0.64),与植被指数红色边缘拐点(REIP)呈强相关(最高 r = 0.88,由拖拉机安装的传感器系统测定),与生物量取样测定的产量呈中度相关(最高 r = 0.57)。研究调查了相对 BMP 与主要土壤参数之间的关系。多年相对 BMP 与土壤有机碳 (SOC) 和全氮 (TN) 含量之间始终存在较强的相关性(r = 0.62 至 0.73),表明该方法有效地反映了这些关键土壤特性对作物产量的影响。该方法非常适合推导产量区,在农业领域具有广泛的应用潜力。
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来源期刊
Precision Agriculture
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.
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