## 4 Discussion  
Our study was targeted on exploring multisource remote sensing capacities for assessing and monitoring mountainous natural grasslands in Armenia. A list of spectral indices (Table 3), which had proven to be valuable tools in describing spatial and temporal variability of biomass [35,92], was applied to compare multisource space-born remote sensing data (Planet Scope, Sentinel-2, and Landsat 8) with air-born (UAV) data using Spearman correlation analysis. The aim was to assess the capability of each space platform for analyzing the trend of short-term spatial-temporal changes of Armenian grasslands for May-July 2018-2023. It is noteworthy that, in our study, air-born (UAV) remote sensing was applied as a validation tool. This approach is known for mapping the complex vegetation of peatlands [34]. The correlations between the UAV and satellite-based indices were positive but were also distinguished by different correlation strengths. Strong positive correlations were observed with the NDVI, GLI, and EVI indices; moderate between SAVI, MSAVI, and DVI; and weak correlation between NDWI, GNDVI, and GSAVI indices. Additionally, high correlations of UAV were observed with Planet Scope, after Sentinel-2 and Landsat 8 had been registered. The correlation analysis confirms that UAV VIs are comparable to the space-born multisensory data for monitoring mountain grasslands because, in all cases, a significant positive correlation \(p<0.01\) (**) was observed. The spatial correlation discovered four main spectral indices (NDVI, GLI, EVI, and SAVI), which all showed strong significant correlations with those calculated from UAV data. Afterwards, the Mann-Kendall tests (MK, MKKH, MKKY, PW, TFPW) were applied for trend analysis. According to all five Mann-Kendall tests, among the studied platforms, only three Sentinel-2-based indices (NDVI, GLI, and SAVI) showed a decreasing trend for May-July 2018-2023. Therefore, Sentinel-2 showed a more consistent and frequent detection of significant trends across vegetation indices. It is important to note that all changes which prove a significant decreasing trend for the studied short period were observed only in May. Though we could not find studies where this approach was applied for grasslands, it worked well for peatlands and vineyards where the authors tried to compare the VIs derived from satellites, aircrafts, and UAVs [34,35,92]. The correlation results were again around r = 0.7, which were close to our best correlations between UAV (4 cm) and Planet Scope (3 m) (r = 0.69) and UAV (4 cm) and Sentinel-2 (10 m) (r = 0.66). We suggest that the reason for the existing high variability is the spatial resolution of the multisource data.  
If we consider Sen's slope as an individual estimator, applied well for the trend analysis based on short-term databases and for different studies [86, 87, 88], we can indicate that, according to Sen's slope, the estimations based on Planet Scope and Sentinel-2 show a decreasing trend, which, according to MK tests, were observed for Sentinel-2-based VIs in May only. This outcome gives us food for thought that Sens's slope could be more robust for our case than MK tests.