# Estimation of Time-Series Forest Leaf Area Index (LAI) Based on Sentinel-2 and MODIS
## 1 Introduction
Distinct types of land use are usually mixed together, and the appearance of mixed pixels can have an effect on the correctness of the land use type. Therefore, it is necessary to study how to knit the advantages of the MODIS data and high spatial resolution satellite data together [38; 39; 40; 41]. The existing spatio-temporal reflectance data fusion research mostly stems from Landsat and MODIS [45; 46; 47]; the spatio-temporal data combining of the series of Sentinel data is less involved. Sentinel-2 has higher spatial resolution and a wider spectral response range than Landsat and MODIS [48; 49; 50]. After spot and Landsat satellites, the European Space Agency (ESA) launched the Sentinel-2 multispectral satellite in 2015 to monitor the land surface. Sentinel-2 has 13 multispectral bands, including 10 m, 20 m and 60 m bands. As a consequence, it provides surface reflectance data of many different wavelengths. A few bands of Sentinel-2 provide a higher resolution than Landsat 8 [51]. Due to the existence of long wavelength red-edge bands, it is quite serviceable vegetation change monitoring [52; 53; 54; 55]. At present, studies have been conducted to retrieve the LAI using Landsat and Sentinel-2 data, and the accuracy of the vegetation growth monitoring is good [56; 57; 58; 59]. Sentinel-2 has a high spatial resolution; nevertheless, the temporal resolution commonly requires more than 5 days, and it is hard to obtain sufficient cloudless images in areas with less sunny days. In some areas, the image is incomplete, making the revisit cycle \begin{table}
\begin{tabular}{c c c c c c} \hline
**LAI** & **Sensor** & **Spatial** & **Temporal** & **Span** & **References** \\
**Product** & **Sensor** & **Resolution** & **Resolution** & **Sensor** & **Reservation** \\ \hline MOD15 & TERRA-AQUA & 1 km & 8 days & Since 2000 & [PERSON] et al. [42] \\ GIMMS3g & AVHRR & 8 km & 15 days & 1981–2011 & [PERSON] et al. [43] \\ GEOV1 & SPOT & 1 km & 10 days & Since 1998 & [PERSON] et al. [44] \\ GLASS & MODIS & 0.5 km & 4 days & Since 1981 & [PERSON] et al. [29] \\ \hline \end{tabular}
\end{table} 
Table 1: Characteristics of Global LAI Products. twice or more. These factors make it more difficult to obtain clear high spatio-temporal resolution time-series images. [PERSON] et al. [60] concluded that Sentinel-2 images are affected by cloud cover, hence limiting its applicability in cloud-prone areas. Due to the technique and cost input, the current satellite sensors cannot fit the requirements of high spatial resolution and high temporal resolution at the same time [61; 62]. Therefore, many spatio-temporal data combining methods have emerged in recent years. The spatio-temporal adaptive reflection fusion model (STARFM) [63] is the first widely used mean of settlement. This method proposes that the reflectivity change trend between high and low spatial resolution data is consistent when the pixels are pure. However, this assumption cannot be satisfied when several land use types within one pixel are mixed together. As a consequence, STARFM has great uncertainty in small areas. At present, spatio-temporal data combining has already been applied to estimate the reflectivity and vegetation indices (VIs) with high spatio-temporal resolution [64; 65; 66]. These studies show the potential application of spatio-temporal data fusion in vegetation growth monitoring. In this study, the vegetation index information was extracted from Sentinel-2 images and LAI data were obtained from ground measurements. By establishing linear and nonlinear models between different vegetation indices and the LAI and by comparing the estimation results, the most suitable statistical model for forest land LAI estimation was selected. This study extracted the normalized growth curve of the MODIS LAI, studied its consistency with the measured LAI growth curve and explored the estimation method of the time-series LAI based on the Sentinel-2 LAI statistical estimation model and MODIS LAI products.