Publication

Abstract : 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 R2 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.