Soil background effects on UAS and proximal remote sensing‐derived vegetation indices

Abstract

Exposed soil, due to low vegetation cover or in open canopy crops, influences scene reflectance derived from remotely sensed data. An experiment was conducted in College Station, TX, to investigate the potential of six unmanned aerial systems (UASs)‐derived and proximally sensed vegetation indices (VIs) in suppressing soil background brightness of four treatments in 2020 and 2021. The treatments were dry soil, dry soil with winter wheat (Triticum aestivum L.) crop residue, wet soil (WS), and wet soil with winter wheat crop residue (CRWS) in 2020. In 2021, WS and CRWS were replaced with dry sand and dry compost (DC). The VIs were calculated from remotely sensed data of treatment plots. Cotton (Gossypium hirsutum L.) canopy cover (%) on different dates of UAS flight was extracted using unsupervised classification. Factors such as shadows, crop residue, soil moisture, and uneven canopy growth influenced the scene reflectance. The shadow on the soil decreased the soil background reflectance to 30% in 2020. Similarly, higher NDVI was observed for DC treatment plots at an estimated mean canopy cover of <35% in 2021. The perpendicular vegetation index was least influenced by canopy cover or soil background variations. The study suggests that UAS can be used for large‐scale research without being affected by soil variability when vegetation cover is above 30%.

Plant phenotyping relevance

UAS・近接リモートセンシングによる植生指数と作物キャノピー被覆率の抽出を評価し、土壌背景の影響や手法の頑健性を検証しているため、植物表現型取得法が中心である。

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