ABSTRACT

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.

摘要

农业土地利用与农艺管理实践相结合，应以可持续实践为基础，确保生产力和环境保护的最大化。NDVI（标准化差异植被指数）时间序列已被公认为监测作物发育及其空间分布的有用方法。然而，卫星数据的空间分辨率和时间分辨率之间总是存在权衡。因此，来自 Planet CubeSat 的高空间和时间分辨率代表了克服这种权衡的可能性。本文研究了使用来自 Planet CubeSat 图像的高空间分辨率每日 NDVI 时间序列进行作物监测的潜力。2017 年的 119 幅图像，地面采样距离为 3 m，在棉花、春玉米和冬小麦田上，被收购并转换为 NDVI。应用时间序列的谐波分析（HANTS）算法来获得平滑的云和无间隙的每日时间序列。每天 3 m 的时间序列被调整为每天 9 和 30 m 的分辨率；并以 4、8 和 16 天的间隔重新采样到时间分辨率，以评估空间和时间分辨率对 NDVI 时间序列的影响。NDVI 时间序列通过其全年的最小值、最大值、平均值和变异系数进行评估。应用主成分分析 (PCA) 和逐步程序来评估最佳特征（一年中的天数）以协助 NDVI 时间序列解释。PCA 和逐步突出了全年 NDVI 时间序列解释的最佳时间。随着空间分辨率的降低，NDVI 的范围及其场内标准偏差也减小，导致场内光谱变异性损失。在每日时间分辨率下，可以在很短的时间间隔内检测到作物发育的细微差异，但随着时间分辨率的降低，作物发育的变化以更大的速率被检测到。Planet CubeSat 图像的高时空分辨率显示出监测农业系统的巨大潜力，可以在即将进行的研究中补贴农业地区的地方和区域监测，并有助于更好地管理政府和企业在技术问题上的决策制定的战略规划. 可以在很短的时间间隔内检测到作物发育的细微差异，但随着时间分辨率的降低，作物发育的变化以更大的速率被检测到。Planet CubeSat 图像的高时空分辨率显示出监测农业系统的巨大潜力，可以在即将进行的研究中补贴农业地区的地方和区域监测，并有助于更好地管理政府和企业在技术问题上的决策制定的战略规划. 可以在很短的时间间隔内检测到作物发育的细微差异，但随着时间分辨率的降低，作物发育的变化以更大的速率被检测到。Planet CubeSat 图像的高时空分辨率显示出监测农业系统的巨大潜力，可以在即将进行的研究中补贴农业地区的地方和区域监测，并有助于更好地管理政府和企业在技术问题上的决策制定的战略规划.