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Saturation effects limit the application of vegetation indices (VIs) in dense vegetation areas. The possibility to mitigate them by adopting a negative soil adjustment factor X is addressed. Two leaf area index (LAI) data sets are analyzed using the Google Earth Engine (GEE) for validation. The first one is derived from observations of MODerate resolution Imaging Spectroradiometer (MODIS) from 16 April 2013, to 21 October 2020, in the Apiacás area. Its corresponding VIs are calculated from a combination of Sentinel-2 and Landsat-8 surface reflectance products. The second one is a global LAI dataset with VIs calculated from Landsat-5 surface reflectance products. A linear regression model is applied to both datasets to evaluate four VIs that are commonly used to estimate LAI: normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), transformed SAVI (TSAVI), and enhanced vegetation index (EVI). The optimal soil adjustment factor of SAVI for LAI estimation is determined using an exhaustive search. The Dickey-Fuller test indicates that the time series of LAI data are stable with a confidence level of 99%. The linear regression results stress significant saturation effects in all VIs. Finally, the exhaustive searching results show that a negative soil adjustment factor of SAVI can mitigate the SAVIs' saturation in the Apiacás area (i.e., X = -0.148 for mean LAI = 5.35), and more generally in areas with large LAI values (e.g., X = -0.183 for mean LAI = 6.72). Our study further confirms that the lower boundary of the soil adjustment factor can be negative and that using a negative soil adjustment factor improves the computation of time series of LAI.

Background and Aims: To improve the management of grass communities, early plant development was compared in three species with contrasting growth forms, a caespitose (Lolium perenne), a rhizomatous (Poa pratensis) and a caespitose-stoloniferous species (Agrostis stolonifera)., Methods: Isolated seedlings were grown in a glasshouse without trophic constraints for 37 d (761 degrees Cd). The appearance of leaves and their location on tillers were recorded. Leaf appearance rate (LAR) on the tillers and site-filling were calculated. Tillering was modelled based on the assumption that tiller number increases with the number of leaves produced on the seedling main stem. Above- and below-ground parts were harvested to compare biomass., Key Results: Lolium perenne and A. stolonifera expressed similar bunch-type developments. However, root biomass was approx. 30 % lower in A. stolonifera than in L. perenne. Poa pratensis was rhizomatous. Nevertheless, the ratio of above-ground : below-ground biomass of P. pratensis was similar to that of L. perenne. LAR was approximately equal to 0.30 leaf d(-1) in L. perenne, and on the main stem and first primary tillers of A. stolonifera. LAR on the other tillers of A. stolonifera was 30 % higher than on L. perenne. For P. pratensis, LAR was 30 % lower than on L. perenne, but the interval between the appearance of two successive shoots from rhizomes was 30 % higher than the interval between two successive leaf stages on the main stem. Above-ground parts of P. pratensis first grew slower than in the other species to the benefit of the rhizomes, whose development enhanced tiller production., Conclusions: Lolium perenne had the fastest tiller production at the earliest stages of seedling development. Agrostis stolonifera and P. pratensis compensated almost completely for the delay due to higher LAR on tillers or ramets compared with L. perenne. This study provides a basis for modelling plant development.