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

  • Technical University of Munich

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

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.

Original languageEnglish
Pages (from-to)2801-2830
Number of pages30
JournalPrecision Agriculture
Volume25
Issue number6
DOIs
StatePublished - Dec 2024

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 2 - Zero Hunger
    SDG 2 Zero Hunger

Keywords

  • Model validation
  • Multispectral sensor
  • Remote sensing
  • Satellite data
  • Yield potential
  • Yield zones

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