推进布莱克莫尔根据哨兵 2 号图像划分管理区的方法

IF 5.4 2区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY
Arthur Lenoir, Bertrand Vandoorne, Ali Siah, Benjamin Dumont
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引用次数: 0

摘要

改善农业氮肥管理是欧洲最近推出的 "绿色协议 "的主要目标之一。当前的农业技术发展为改进氮肥施用方法提供了新的机遇。本研究的目的是将最初为产量地图开发的一种行之有效的划分方法应用于哨兵-2 数据,以促进农民进行精确的氮肥管理。研究分两步进行。首先,在年度层面上进行分析,评估不同感知时期子田块尺度上植被指数与产量之间的关系。第二步是通过划分管理区进行多年度分析,并比较产量图和 NDVI 图得出的结果。在不同的植被指数中,NDVI 被证明是一个有趣的候选指数,特别是在 5 月下半月左右感测该指数时,可用于分田块检测产量变化。在该地区,这一时期通常对应于旗叶期和打顶期之间的物候发展,也就是冬小麦开花之前。在 Blackmore 的划分方法中使用 NDVI 地图,而不是产量地图。使区域划分的准确率达到 69%。然而,由于产量和 NDVI 分布的统计分布不尽相同,我们认为必须调整用于区分高产和低产区域的划分阈值。调整 "性能阈值",改用 NDVI 中位数,使划分的准确率提高(71%)。但最重要的是,这种改进还在于基于卫星的划界更加稳健。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Advancing Blackmore’s methodology to delineate management zones from Sentinel 2 images

Advancing Blackmore’s methodology to delineate management zones from Sentinel 2 images

Improving agricultural nitrogen management is one of the key objectives of the recent Green Deal in Europe. Current technological developments in agriculture offer new opportunities to improve nitrogen fertilization practices. The aim of this study was to adapt to Sentinel-2 data a proven delineation method initially developed for yield maps, in order to facilitate precise nitrogen management by farmers. The study was conducted in two steps. Firstly, an analysis at annual level was conducted to assess the relationship between vegetation indices and yield at the subfield scale, for different sensing period. The second step consisted in performing a pluri- annual analysis through the delineation of management zones and compare the results achieved from yield maps and from NDVI maps. Among different vegetation indices, NDVI proved to be an interesting candidate for subfield detection of yield variation, specifically when the index was sensed was sensed around the second half of May. In this area, this period usually corresponds to phenological development between the flag leaf stage and heading stage, just prior the initiation of winter wheat flowering. Using NDVI maps within Blackmore’s delineation approach instead of yield maps. Allowed to reach an accuracy of 69% on zone classification. However, as yields and NDVI distribution do not respond to similar statistical distributions, we considered that the delineation threshold used to differentiate high from low yielding zones had to be adapted. The adaptation of the “performance threshold” in favor of the median NDVI, made it possible to achieve a higher accuracy (71%) of the delineation. But above all, the improvement lies also in a more robust satellite-based delineation.

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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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