The dynamics of Leaf Area Index (LAI) from space is key to identify crop types and their phenology over large areas, and to characterize spatial variations within growers’ fields. However, for years remote-sensing applications have been constrained by a trade-off between the spatial and temporal resolutions. This study resolves this limitation. Over the past decade, the number of companies and organizations developing CubeSat constellations has increased. These new satellites make it possible to acquire large image collections at high spatial and temporal resolutions at a relatively low cost. However, the images obtained from CubeSat constellations frequently suffer from inconsistency in the data calibration between the different satellites within the constellation. To overcome these inconsistencies, a new method to fuse a time series of images sourced from two different satellite constellations is proposed, combining the advantages of both (i.e., the temporal, spatial and spectral resolution). This new technique was applied to fuse PlanetScope images with Sentinel-2 images, to create spectrally-consistent daily images of wheat LAI at a 3 m resolution. The daily 3 m LAI estimations were compared with 57 in-situ wheat LAI measurements taken in Australia and Israel. This approach was demonstrated to successfully estimate Green LAI (LAI before the major on-set of leaf senescence) with an $R^2$ of 0.94 and 86% relative accuracy (RMSE of 1.37) throughout the growing season without using any ground calibration. However, both the Sentinel-2 based estimates and the fused Green LAI were underestimated at high LAI values (LAI > 3). To account for this, regression models were developed, improving the relative accuracy of the Green LAI estimations by up to a further 47% (RMSE of 0.35–0.63) in comparison with field measured LAI. The new time series fusion method is an effective tool for continuous daily monitoring of crops at high-resolution over large scales, which opens up a range of new precision agriculture applications. These high spatio-temporal resolution time-series are valuable for monitoring crop growth and health, and can improve the effectiveness of farming practices and enhance yield forecasts at the field and sub-field scales.

来自空间的叶面积指数（LAI）的动态变化对于确定大面积作物类型及其物候特征以及表征种植者田间的空间变化至关重要。然而，多年来，遥感应用一直受到空间和时间分辨率之间的权衡的限制。这项研究解决了这一限制。在过去的十年中，开发CubeSat星座的公司和组织的数量有所增加。这些新的卫星使得以相对较低的成本以高空间和时间分辨率获取大型图像集成为可能。但是，从CubeSat星座获得的图像经常遭受星座内不同卫星之间数据校准的不一致。为了克服这些矛盾，结合两者的优点（即时间，空间和频谱分辨率），提出了一种融合来自两个不同卫星星座的图像的时间序列的新方法。这项新技术被应用于将PlanetScope图像与Sentinel-2图像融合，以创建3 m分辨率的光谱一致的小麦LAI每日图像。每天3 m LAI估计值与57在澳大利亚和以色列进行的小麦原位LAI测量。事实证明，该方法可以成功估算出绿色LAI（主要在叶片衰老开始之前的LAI），并且${\mathrm{[R}}^2$整个生长季节的相对精度为0.94，相对精度为86％（RMSE为1.37），无需使用任何地面校准。但是，在高LAI值（LAI> 3）下，基于Sentinel-2的估计值和融合的Green LAI都被低估了。为了解决这个问题，开发了回归模型，与现场测量的LAI相比，Green LAI估计的相对准确性进一步提高了47％（RMSE为0.35-0.63）。新的时间序列融合方法是一种有效的工具，可以在高分辨率下进行大规模的大规模连续连续日监测，这开辟了一系列新的精确农业应用。这些高的时空分辨率时间序列对于监控作物的生长和健康非常有价值，并且可以提高耕作方法的有效性，并提高田间和子田规模的产量预测。