Predicting on-farm soybean yield variability using texture measures on Sentinel-2 image

IF 5.4 2区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY
Rodrigo Greggio de Freitas, Henrique Oldoni, Lucas Fernando Joaquim, João Vítor Fiolo Pozzuto, Lucas Rios do Amaral
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Abstract

Yield forecasting and within-field yield variation is essential information that helps farmers develop sustainable agriculture. However, such information still needs to be included for most of them, and remote sensing is an alternative to provide it. Our objective was to assess Random Forest regression models composed of unique GLCM texture measures as an alternative to usual empirical models that use spectral response and auxiliary data, which is complex and reaches varied results. Eleven GLCM texture models based on eight texture measures of a single spectral layer were assessed to represent soybean field yield variation in two sites and seasons. Several models achieved satisfactory results, reaching R2 from 0.90 to 0.95 and RMSE from 0.06 to 0.26 t/ha. Models above 15-window size are recommended for the soybean yield prediction as window size is an essential attribute to GLCM performance. Models derived from the bands individually (red, red-edge, near-infrared, and short wavelength infrared) were more sensitive to the window size than those derived from vegetation indices (EVI, GNDVI, GRNDVI, NDMI, NDRE, NDVI, SFDVI). The data aggregated by texture measures improve the individual spectral responses, providing alternatives to predict soybean within-field yield variation using random forest models.

Abstract Image

利用 "哨兵-2 "图像的纹理测量方法预测农场大豆产量变化
产量预测和田间产量变化是帮助农民发展可持续农业的基本信息。然而,对于大多数农民来说,这些信息仍需包括在内,而遥感则是提供这些信息的替代方法。我们的目标是评估由独特的 GLCM 纹理度量组成的随机森林回归模型,以替代使用光谱响应和辅助数据的常规经验模型,后者非常复杂,结果也各不相同。我们评估了 11 个基于单个光谱层的 8 个纹理测量值的 GLCM 纹理模型,以表示两个地点和两个季节的大豆田间产量变化。几个模型取得了令人满意的结果,R2 从 0.90 到 0.95 不等,RMSE 从 0.06 到 0.26 吨/公顷不等。由于窗口大小是影响 GLCM 性能的一个重要因素,因此建议采用 15 个窗口以上的模型进行大豆产量预测。与植被指数(EVI、GNDVI、GRNDVI、NDMI、NDRE、NDVI、SFDVI)相比,单独从波段(红、红边、近红外和短波红外)得出的模型对窗口大小更为敏感。通过纹理度量汇总的数据改善了单个光谱响应,为使用随机森林模型预测大豆田间产量变化提供了替代方法。
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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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