Forest condition assessment based only on field observations can be limited when crown deterioration is not clearly linked to recorded damage. This study integrates ICP Forests Level II terrestrial observations with multi-sensor Earth Observation (EO) data to assess forest condition at two Serbian monitoring plots, Odžaci and Fruška Gora, during the 2025 growing season. Field data included crown defoliation, defoliation classes and tree damage records, while EO indicators included Sentinel-2 vegetation, moisture and disturbance indices, Sentinel-1 radar backscatter and MODIS daytime land surface temperature.

ICP observations showed poorer crown condition at Odžaci, with higher mean and median defoliation than Fruška Gora (35.5% and 30.0% vs. 24.1% and 15.0%), fewer trees in the low-defoliation class, and more frequent recorded damage.

EO indicators showed statistically supported consistency with this terrestrial pattern. Mean Sentinel-2 greenness indices were similar between plots, including NDVI (0.858 ± 0.042 at Odžaci vs. 0.859 ± 0.036 at Fruška Gora) and NDMI (0.329 ± 0.044 vs. 0.329 ± 0.037), but seasonal trajectories were more informative. NDMI declined more strongly at Odžaci (−0.0289 vs. −0.0217 month⁻¹; p = 0.046), while MSI (+0.0332 vs. +0.0245 month⁻¹; p = 0.033) and BSI (+0.0297 vs. +0.0208 month⁻¹; p = 0.020) increased more strongly. MODIS daytime LST was also significantly higher at Odžaci (28.49 ± 4.94°C) than at Fruška Gora (23.64 ± 4.69°C; p < 0.001), and Sentinel-1 VH and VV backscatter were significantly lower at Odžaci.

The results show that satellite-derived indicators, particularly NDMI, MSI, BSI, MODIS LST and Sentinel-1 backscatter, were statistically consistent with field-assessed crown condition at Odžaci. EO data therefore complemented ICP Forests observations by identifying moisture, thermal and structural signals associated with field-observed crown deterioration. The results should be interpreted as a pilot-scale ICP–EO consistency assessment rather than a regional validation or direct causal attribution of drought.