Using High-Resolution Satellite Data to Estimate Canopy Cover and Leaf Area Index in Plot Experiments

Understanding and monitoring crop growth is crucial for addressing global food security challenges and promoting sustainable agricultural practices. Traditional methods of observing crop traits in plot experiments are labor-intensive, limiting their spatial and temporal resolution. While conventional satellite platforms like Sentinel-2 and Landsat have proven valuable for large-scale agricultural monitoring, their spatial resolutions and temporal gaps are insufficient for time series of small experimental plots. Recent advancements, such as PlanetLabs’ SuperDove constellation, provide an alternative by offering daily imagery at a 3 m resolution, making them suitable for small-scale plot-level analysis. Despite their high spatial detail, these images face challenges related to radiometric stability, spatial co-registration accuracy, and quality masks, which must be resolved for effective small-scale monitoring. Addressing these limitations, this research investigates the use of PlanetScope data to estimate canopy cover (CC) and leaf area index (LAI) in plot experiments. High-resolution Unmanned Aerial System (UAS) RGB imagery was used as a reference to estimate early-stage CC. By applying a machine learning-based segmentation technique, we distinguished foliage from background pixels. This segmentation enabled us to integrate UAS-derived CC estimates with 8-band multispectral imagery from PlanetLabs’ SuperDove constellation. After improving the radiometric stability and spatial accuracy of the satellite imagery, we used the multispectral data along with UAS-derived canopy cover estimates as inputs to identify the most sensitive satellite-derived vegetation indices (VIs) for estimating CC during the early growth stages. In conjunction with LAI, we generated model-based time-series growth curves covering all phenological stages. The method was validated on experimental plots in northern Switzerland, with varying soil compaction and fertilization treatments. The study demonstrates successful segmentation of high-resolution UAS-based RGB imagery, providing a robust baseline for validating satellite-derived data and training novel retrieval methods for canopy cover. Comparative analyses identify vegetation indices from PlanetScope imagery that correlate with early crop growth. This research highlights the potential of high-resolution satellite data for generating time-series growth curves, offering a valuable tool for improving crop management and optimizing resource use across diverse farming systems.