Abstract Satellite sensing in the visible to near-infrared (VNIR) domain has been the backbone of land surface monitoring and characterization for more than four decades. However, a limitation of conventional single-sensor satellite missions is their limited capacity to observe land surface dynamics at the very high spatial and temporal resolutions demanded by a wide range of applications. One solution to this spatio-temporal divide is an observation strategy based on the CubeSat standard, which facilitates constellations of small, inexpensive satellites. Repeatable near-daily image capture in RGB and near-infrared (NIR) bands at 3–4 m resolution has recently become available via a constellation of >130 CubeSats operated commercially by Planet. While the observing capacity afforded by this system is unprecedented, the relatively low radiometric quality and cross-sensor inconsistencies represent key challenges in the realization of their full potential as a game changer in Earth observation. To address this issue, we developed a Cubesat Enabled Spatio-Temporal Enhancement Method (CESTEM) that uses a multi-scale machine-learning technique to correct for radiometric inconsistencies between CubeSat acquisitions. The CESTEM produces Landsat 8 consistent atmospherically corrected surface reflectances in blue, green, red, and NIR bands, but at the spatial scale and temporal frequency of the CubeSat observations. An application of CESTEM over an agricultural dryland system in Saudi Arabia demonstrated CubeSat-based reproduction of Landsat 8 consistent VNIR data with an overall relative mean absolute deviation of 1.6% or better, even when the Landsat 8 and CubeSat acquisitions were temporally displaced by >32 days. The consistently high retrieval accuracies were achieved using a multi-scale target sampling scheme that draws Landsat 8 reference data from a series of scenes by using MODIS-consistent surface reflectance time series to quantify relative changes in Landsat-scale reflectances over given Landsat-CubeSat acquisition timespans. With the observing potential of Planet's CubeSats approaching daily nadir-pointing land surface imaging of the entire Earth, CESTEM offers the capacity to produce daily Landsat 8 consistent VNIR imagery with a factor of 10 increase in spatial resolution and with the radiometric quality of actual Landsat 8 observations. Realization of this unprecedented Earth observing capacity has far reaching implications for the monitoring and characterization of terrestrial systems at the precision scale.

中文翻译：

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使用行星、陆地卫星和 MODIS 数据的 Cubesat 启用时空增强方法 (CESTEM)

摘要 四十多年来，可见光至近红外 (VNIR) 域中的卫星传感一直是地表监测和表征的支柱。然而，传统单传感器卫星任务的局限性在于它们以广泛应用所需的非常高的空间和时间分辨率观察地表动态的能力有限。解决这种时空鸿沟的一种解决方案是基于 CubeSat 标准的观测策略，它促进了小型廉价卫星的星座。最近，Planet 商业运营的超过 130 颗立方体卫星星座可以在 RGB 和近红外 (NIR) 波段以 3-4 m 的分辨率进行可重复的近日常图像捕获。虽然该系统提供的观测能力是前所未有的，相对较低的辐射质量和交叉传感器的不一致是实现其作为地球观测游戏规则改变者的全部潜力的关键挑战。为了解决这个问题，我们开发了一种启用 Cubesat 的时空增强方法 (CESTEM)，该方法使用多尺度机器学习技术来校正 CubeSat 采集之间的辐射测量不一致。CESTEM 在蓝色、绿色、红色和 NIR 波段产生 Landsat 8 一致的大气校正表面反射率，但在 CubeSat 观测的空间尺度和时间频率上。CESTEM 在沙特阿拉伯的农业旱地系统上的应用证明了基于 CubeSat 的 Landsat 8 一致 VNIR 数据的再现，总体相对平均绝对偏差为 1.6% 或更好，即使 Landsat 8 和 CubeSat 采集在时间上偏移了 >32 天。使用多尺度目标采样方案实现了始终如一的高检索精度，该方案通过使用 MODIS 一致的表面反射率时间序列来量化给定 Landsat-CubeSat 采集的 Landsat 尺度反射率的相对变化，从一系列场景中提取 Landsat 8 参考数据时间跨度。随着 Planet CubeSats 的观测潜力接近整个地球的每日天底指向地表成像，CESTEM 提供了生成每日 Landsat 8 一致 VNIR 图像的能力，空间分辨率提高了 10 倍，并且具有实际 Landsat 8 的辐射质量观察。