Quantifying Uncertainty and Bridging the Scaling Gap in the Retrieval of Leaf Area Index by Coupling Sentinel-2 and UAV Observations

Abstract

Leaf area index (LAI) estimates can inform decision-making in crop management. The European Space Agency’s Sentinel-2 satellite, with observations in the red-edge spectral region, can monitor crops globally at sub-field spatial resolutions (10–20 m). However, satellite LAI estimates require calibration with ground measurements. Calibration is challenged by spatial heterogeneity and scale mismatches between field and satellite measurements. Unmanned Aerial Vehicles (UAVs), generating high-resolution (cm-scale) LAI estimates, provide intermediary observations that we use here to characterise uncertainty and reduce spatial scaling discrepancies between Sentinel-2 observations and field surveys. We use a novel UAV multispectral sensor that matches Sentinel-2 spectral bands, flown in conjunction with LAI ground measurements. UAV and field surveys were conducted on multiple dates—coinciding with different wheat growth stages—that corresponded to Sentinel-2 overpasses. We compared chlorophyll red-edge index (CIred-edge) maps, derived from the Sentinel-2 and UAV platforms. We used Gaussian processes regression machine learning to calibrate a UAV model for LAI, based on ground data. Using the UAV LAI, we evaluated a two-stage calibration approach for generating robust LAI estimates from Sentinel-2. The agreement between Sentinel-2 and UAV CIred-edge values increased with growth stage—R2 ranged from 0.32 (stem elongation) to 0.75 (milk development). The CIred-edge variance between the two platforms was more comparable later in the growing season due to a more homogeneous and closed wheat canopy. The single-stage Sentinel-2 LAI calibration (i.e., direct calibration from ground measurements) performed poorly (mean R2 = 0.29, mean NRMSE = 17%) when compared to the two-stage calibration using the UAV data (mean R2 = 0.88, mean NRMSE = 8%). The two-stage approach reduced both errors and biases by >50%. By upscaling ground measurements and providing more representative model training samples, UAV observations provide an effective and viable means of enhancing Sentinel-2 wheat LAI retrievals. We anticipate that our UAV calibration approach to resolving spatial heterogeneity would enhance the retrieval accuracy of LAI and additional biophysical variables for other arable crop types and a broader range of vegetation cover types.

Estimating Processing Tomato Water Consumption, Leaf Area Index, and Height Using Sentinel-2 and VENµS Imagery

“…LAI was measured by a SunScan Canopy Analysis System-SS1 manufactured by Delta-T Company (Cambridge, UK) during the two experiments conducted in 2019 and one experiment conducted in 2020. The SunScan is a widely used, accurate, nondestructive LAI measurement system that was successfully employed in many previous studies [31,43,44]. Plant height was measured using a measuring tape during all four experiments conducted in 2018-2020.…”

Characterizing the Error and Bias of Remotely Sensed LAI Products: An Example for Tropical and Subtropical Evergreen Forests in South China

“…More complex upscaling approaches or those utilizing very high-resolution sensors (e.g., LIDAR) to bridge the scaling gap may yield better results [102,105,106]. In particular, there is an exciting potential for unmanned aerial vehicles to provide critical upscaling information from field to satellite [21].…”

“…A key challenge for the validation process is the mismatch in the scale of satellite products (typically > 300 m) compared to that of the in situ data (<10 m). Heterogeneity of LAI at scales finer than the satellite resolution [21] means that simple comparison between measurements at different scales can be problematic [22].…”

Sentinel-2 Data for Precision Agriculture?—A UAV-Based Assessment

“…Therefore, the possibility of comparing satellite with UAV data is of growing interest [ 25 , 26 ]. Revill et al [ 27 ] introduced in 2020 a two-step procedure for the calibration of satellite VIs to crop canopy characteristics: calibrate UAV data with ground measurements and afterwards using the UAV predictions to calibrate the satellite data. Such a new methodological framework might give new insights into the possible contribution of the Sentinel-2 data with regard to crop monitoring.…”