Quality Control of Ground-Measured Spectroradiometer Data Using the Clearness Index for Seasonal Monitoring of Crop

Ground-based optical sensors are valuable tools for continuous monitoring of crop spectral characteristics throughout their growth stages.However, spectral data collected under various environmental conditions often contain substantial noise.This study aims to develop a method for the quality control of spectral reflectance data collected by a tower-mounted spectroradiometer and to extract the seasonal characteristics of vegetation indices.The spectroradiometer continuously measured reflectance in the visible to the near-infrared wavelength range at 1-2 minute intervals over rice paddies.This procedure considered solar zenith angle, radiance variability, and device conditions to eliminate unreliable observations.Further refinement was achieved by applying user-defined temporal filters and removing outliers in daily reflectance values, which collectively enhanced the temporal consistency of reflectance in the red and nearinfrared wavelengths.In addition, a clearness index based on solar radiation modeling was applied to filter data obtained under cloudy conditions, enabling a clearer identification of both physiological and biochemical variations in crops.As a result, the biochemical vegetation index exhibited stable seasonal variations corresponding to the crop growth stages, and physiological vegetation indices showed distinct seasonal patterns after applying the clearness index threshold.The proposed quality control procedure and clearness index-based filtering approach enhance the reliability of ground-based spectral observations.These methods are expected to be applicable to quality assessment and crop monitoring using satelliteand drone-based remote sensing data.