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To verify large-scale vegetation parameter measurements, the average value of sampling points from small-scale data is typically used. However, this method undermines the validity of the data due to the difference in scale or an inappropriate number of sampling points. A robust universal error assessment method for measuring ground vegetation parameters is, therefore, needed. Herein, we simulated vegetation scenarios and measurements by employing a normal distribution function and the Lindbergh-Levi theorem to deduce the characteristics of the error distribution. We found that the small- and large-scale error variations were similar among the theoretically deduced leaf area index (LAI) measurements. In addition, LAI was consistently normally distributed regardless of which a systematic error or an accidental error was applied. The difference between observed and theoretical errors was highest in the low-density scenario (7.6% at <; 3% interval) and was lowest in the high-density scenario (5.5% at <; 3% interval), while the average ratio between deviation and theoretical error of each scenario was 2.64% (low density), 2.07% (medium density), and 2.29% (high density). Furthermore, the relative difference between the theoretical and empirical errors was highest in the high-density scenario (20.0% at <; 1% interval) and lowest in the low-density scenario (14.9% at <; 1% interval), respectively. These data show the strength of a universal error assessment method, and we recommend that existing large-scale data of the study region are used to build a theoretical error distribution. Such prior work in conjunction with the models outlined in this article could reduce measurement costs and improve the efficiency of conducting ground measurements.

Modeling Error Evaluation of Ground Observed Vegetation Parameters