Yin, GF; Li, AN; Zhang, ZJ; Lei, GB (2020). Temporal Validation of Four LAI Products over Grasslands in the Northeastern Tibetan Plateau. PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING, 86(4), 225-233.

Time series of leaf area index (LAI) products are now widely used, and the temporal validation is the prerequisite for their proper application. However, a systematical comparison between different products using both direct and indirect methods is still lacking. The objective of this paper is to assess and compare the temporal performances of four LAI products: Moderate Resolution Imaging Spectromdiometer (MODIS) LAI (MOD)15A2, MOD15A2h, Geoland2 Version 1 (GEOvi), and Global Land Surface Satellite (cLAss). The study area, which is dominated by gmsslands, is located in the northeastern Tibetan Plateau (Ti), and temperature is the main stress factor affecting grass growth. Both a correlation analysis with temperature and a direct comparison with temporally continuous LAI reference maps were implemented in our temporal validation experiments. The results show that no single product can capture the rapid change and the seasonal trend in LAI simultaneously, and the compositing period used in each product determines the quality of the corresponding LAI time series. The MOD15A2 and MOD15A2h products, which have short compositing windows (eight days), are suitable for detecting rapid change. A grazinginduced biomass decrease that occurred around day of year 205 in 2014 in our study area was clearly revealed in these two products. For the GEOV1 and GLASS products, which have compositing windows of 30 days and 1 year, respectively, the grazing date was shifted (GEovr) or even invisible (0ms.9). However, products with prolonged compositing windows may be more robust to observation noise, and the resulting products may be suitable for capturing the seasonal trend. This study highlights that the concurrent use of data from various sensors onboard different satellites, and the introduction of new generations of satellites (e.g., Gaofen-6), are two promising ways to further improve existing LAI time series.