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Assessing the potential of using high spatial resolution daily NDVI-time-series from planet CubeSat images for crop monitoring

机译:评估使用高空间分辨率每日NDVI时序系列的潜力,从行星立方体图像进行作物监测

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The agricultural land use combined with agronomic management practices shall be structured on sustainable practices, guaranteeing both the maximization of productivity and environment preservation. NDVI (Normalized Difference Vegetation Index) time-series has been recognized as a useful methodology to monitor crop development and its spatial distribution. However, there is always a trade-off between spatial and temporal resolutions in satellite data. Hence, high spatial and temporal resolutions from Planet CubeSat represent a possibility to overcome this trade-off. This paper investigated the potential of using high spatial resolution daily NDVI-time-series from Planet CubeSat images for crop monitoring. One hundred nineteen images from 2017, at 3 m ground sampling distance, over cotton, spring corn and winter wheat fields, were acquired and converted into NDVI. The harmonic analysis of time series (HANTS) algorithm was applied to obtain a smoothed cloud and gap-free daily time-series. The 3 m daily time-series were resized to daily 9 and 30 m resolution; and resampled to temporal resolutions at 4, 8 and 16 days intervals to assess the impact of spatial and temporal resolution on NDVI time-series. NDVI time-series were evaluated by their minimum, maximum, average and coefficient of variation across the year. Principal component analysis (PCA) and the stepwise procedure were applied to assess optimum features (days across the year) to assist the NDVI-time-series interpretation. PCA and stepwise highlighted the best time across the year for NDVI-time-series interpretation. As the spatial resolution decreases, the range of NDVI and its standard deviation within field also decreases, leading to loss of within field spectral variability. At daily temporal resolution, slight differences in crop development can be detected in a very short time interval, but as the temporal resolution decreases the changes in crop development are detected at larger rates. The high temporal and spatial resolutions from Planet CubeSat images demonstrated great potential to monitor agricultural systems and can subsidize, on forthcoming research, the local and regional monitoring of agricultural areas and contribute to better management regarding strategic planning of governmental and corporate decision making over technical issues.
机译:农业用地与农艺管理实践相结合,应制定可持续惯例,保证生产力和环境保护的最大化。 NDVI(归一化差异植被指数)时间系列被认为是监测作物发展及其空间分布的有用方法。但是,卫星数据中的空间和时间分辨率之间总是存在权衡。因此,来自行星CubeSat的高空间和时间分辨率代表了克服这种权衡的可能性。本文研究了使用从行星立方体图像中使用高空间分辨率每日NDVI时序的潜力进行作物监测。 2017年从棉花,春天玉米和冬小麦田的地面采样距离为3米,以3米的地面取样距离,并转化为NDVI。应用了时间序列(Hants)算法的谐波分析来获得平滑的云和无间隙日常时间序列。将3米日的时间系列调整为每日9和30米的分辨率;在4,8和16天间隔内重新采样,以评估空间和时间分辨率对NDVI时间序列的影响。 NDVI时间系列通过其全年的最小,最大,平均和变异系数评估。主要成分分析(PCA)和逐步程序应用于评估最佳特征(一年中的天),以协助NDVI - 时序解释。 PCA和逐步突出显示NDVI时序思考的一年中的最佳时间。随着空间分辨率的降低,NDVI的范围及其在字段内的标准偏差也降低,导致在现场光谱可变性内丧失。在日常时间分辨率下,可以在非常短的时间间隔中检测作物发展的轻微差异,但随着时间分辨率降低了在更大的速率下检测到作物发展的变化。来自行星立方体图像的高时和空间分辨率表现出巨大的监测农业系统,并可以即将到来的研究,农业领域的地方和区域监测提供补贴,并有助于更好地管理关于政府和企业决策的战略规划。

著录项

  • 来源
    《International journal of remote sensing》 |2021年第18期|7114-7142|共29页
  • 作者单位

    Chinese Acad Agr Sci Minist Agr Key Lab Agr Remote Sensing Inst Agr Resources & R CAAS CIAT Joint Lab Adv Technol Sustainable Agr Beijing 100081 Peoples R China|Univ Estadual Maringa Dept Agron Maringa Parana Brazil;

    Chinese Acad Agr Sci Minist Agr Key Lab Agr Remote Sensing Inst Agr Resources & R CAAS CIAT Joint Lab Adv Technol Sustainable Agr Beijing 100081 Peoples R China;

    Chinese Acad Agr Sci Minist Agr Key Lab Agr Remote Sensing Inst Agr Resources & R CAAS CIAT Joint Lab Adv Technol Sustainable Agr Beijing 100081 Peoples R China;

    Chinese Acad Agr Sci Minist Agr Key Lab Agr Remote Sensing Inst Agr Resources & R CAAS CIAT Joint Lab Adv Technol Sustainable Agr Beijing 100081 Peoples R China;

    Chongqing Inst Meteorol Sci Chongqing Peoples R China;

    Chinese Acad Agr Sci Minist Agr Key Lab Agr Remote Sensing Inst Agr Resources & R CAAS CIAT Joint Lab Adv Technol Sustainable Agr Beijing 100081 Peoples R China;

    Chinese Acad Agr Sci Minist Agr Key Lab Agr Remote Sensing Inst Agr Resources & R CAAS CIAT Joint Lab Adv Technol Sustainable Agr Beijing 100081 Peoples R China;

    Univ Estadual Maringa Dept Agron Maringa Parana Brazil;

    Embrapa Soja Natl Soybean Res Ctr Brazilian Agr R Londrina Parana Brazil;

    Embrapa Soja Natl Soybean Res Ctr Brazilian Agr R Londrina Parana Brazil;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
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