The potential of remote sensing of cover crops to benefit sustainable and precision fertilization.

Abstract

Cover crops and precision fertilization are two core strategies to advance sustainable agriculture. Based on a review of proven achievements in remote sensing of vegetation, a novel approach is proposed to use remote-sensing of cover crops to map soil nutrient availability and to produce prescription maps for precision basal fertilization prior to sowing the following cash crop. The first goal of this manuscript is to introduce the concept of using remote-sensing of cover crops as 'reflectors' or 'bio-indicators' of soil nutrient availability. This concept has two components: 1. mapping nitrogen availability using remote-sensing of cover crops; 2. using remotely-detected visual symptoms of cover crops' nutrient deficiencies to guide sampling schemes. The second goal was to describe two case studies that initially evaluated the feasibility of this concept in a 20 ha field. In the first case study, cover crops mixtures containing legumes and cereals were sown during two seasons in soils with different nitrogen levels. Cereals dominated the mixture when soil nitrogen levels were low, while legumes dominated when levels were high. Plant height and texture analysis derived from UAV-RGB-images were used to measure differences between the dominant species as an indicator of soil nitrogen availability. In the second case study, in an oat cover crop, three different appearances of visual symptoms (phenotypes) were observed throughout the field, and laboratory analysis showed they significantly differed in their nutrient levels. Spectral vegetation indices and plant height derived from UAV-RGB-images were analyzed by a multi-stage classification procedure to differentiate between the phenotypes. The classified product was interpreted and interpolated to generate a high-resolution map showing nutrient uptake for the whole field. The suggested concept essentially elevates the services cover crops can provide to benefit sustainable agriculture if incorporated with remote-sensing. The potentials, limitations and open questions concerning the suggested concept are discussed.

Plant phenotyping relevance

被覆作物の表現型（草丈、テクスチャ、栄養欠乏症状）をUAV画像・スペクトル指標から抽出し、分類・マッピングするリモートセンシング手法が研究の中心であるため。

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