A novel approach for estimating fractional cover of crops by correcting angular effect using radiative transfer models and UAV multi-angular spectral data.

• The Multi-FVC model was developed based on PROSAIL model and Beer-Lambert law to retrieve FVC. • Multi-FVC model was more potential in estimating FVC and suppressing the influence of soil. • Soil effect caused by the second axis in the soil spectral line could be solved by NDVI and EVI. • Multi-FVC model could realize the conversion of FVC retrieval results at any two view zenith angles. Fractional vegetation cover (FVC) plays an important role in spectral unmixing, crop growth monitoring, crop light interception calculation, and yield estimation. However, the spectral reflectance would change with view zenith angles (VZAs), and retrieved FVC is also affected by VZAs. Therefore, in this paper, the observed multi-angular (±45°, ±30°, 0°) spectral datasets with different crops (wheat and rice), along with simulated spectral dataset, were used to explore method of correcting the influence of angle effect in FVC retrieval. Firstly, a simulated dataset of multi-angular hyperspectral data and directional FVC were constructed using the PROSAIL model, and a single-angular FVC retrieval model (Sin-FVC) was established based on the Gaussian process regression (GPR) algorithm. Then, the single-angular FVC (i.e., FVC θ) was converted into vertical FVC (FVC θv) based on the Beer-Lambert law. Finally, a Multi-FVC model was developed, which summed outputs of the Sin-FVC model (FVC 0° , FVC θv) according to their respective weights (i.e., FVC correct), to correct the angle effect. Using the pixel bisection model (FVC VI) as a comparison, different FVC retrieval models (Sin-FVC, Multi-FVC, and FVC VI) were compared and evaluated in terms of FVC retrieval accuracy, ability in weakening soil background, and LNC retrieval accuracy after spectral decomposition using different retrieved FVC (FVC 0° , FVC -30°v , FVC -45°v , FVC correct). The results showed that FVC correct had the highest FVC retrieval accuracy (RRMSE = 8.5 %) compared with FVC retrieved from other models (FVC VI , FVC 0° , FVC -30°v and FVC -45°v). Meanwhile, when using the reflectance at NIR band in the black soil background as the baseline, after decoupling mixing spectra based on FVC correct , the relative offset (i.e., RO correct) of each vegetation index (NDVI, EVI, SAVI, OSAVI) was minimum (least-RO correct = 0.42 %). That is, the soil background was effectively suppressed by FVC correct spectral decomposition, along with the highest LNC retrieval accuracy, with an RRMSE of 14.2 %. [ABSTRACT FROM AUTHOR]