Abstract. Droughts are phenomena that affect large areas. Remote sensing data covering large territories can be used to assess the impact and extent of droughts. Drought effect on vegetation was determined using the normalized difference vegetation index (NDVI) and Vegetation Condition Index (VCI) in the eastern Baltic Sea region located between 53-60 • N and 20-30 • E. The effect of precipitation deficit on vegetation in arable land and broadleaved and coniferous forest was analysed using the Standardized Precipitation Index (SPI) calculated for 1-to 9-month timescales. Vegetation has strong seasonality in the analysed area. The beginning and the end of the vegetation season depends on the distance from the Baltic Sea, which affects temperature and precipitation patterns. The vegetation season in the southeastern part of the region is 5-6 weeks longer than in the northwestern part. The early spring air temperature, snowmelt water storage in the soil and precipitation have the largest influence on the NDVI values in the first half of the active growing season. Precipitation deficit in the first part of the vegetation season only has a significant impact on the vegetation on arable land. The vegetation in the forests is less sensitive to the moisture deficit. Correlation between VCI and the same month SPI1 is usually negative in the study area. It means that wetter conditions lead to lower VCI values, while the correlation is usually positive between the VCI and the SPI of the previous month. With a longer SPI scale the correlation gradually shifts towards the positive coefficients. The positive correlation between 3-and 6-month SPI and VCI was observed on the arable land and in both types of forests in the second half of vegetation season. The precipitation deficit is only one of the vegetation condition drivers and NDVI cannot be used universally to identify droughts, but it may be applied to better assess the effect of droughts on vegetation in the eastern Baltic Sea region.

Snow cover plays an important role in environmental, hydrological and climate systems. To monitor the inter‐annual and seasonal variation of snow in Lithuania, daily moderate resolution imaging spectroradiometer (MODIS) snow cover products MOD10A1 and MYD10A1 were employed during the period from 2002 to 2018. The main disadvantage of the MODIS sensor is that it is unable to determine the surface conditions under cloud cover. In this study, a four‐step procedure was implemented to remove clouds from the MODIS snow products and estimated the annual and monthly number of snow cover days (SCDs). The steps included a combination of MODIS data from Aqua and Terra satellites, spatial and temporal filtering of cloud covered pixels. Additional daily minimum temperature control filter helped to reduce the misclassification snow errors. The final daily cloud‐free MODIS snow maps showed an overall accuracy of 89% when compared with manual ground observations. The lowest validation scores were determined on the Baltic Sea coast, where snow cover is ephemeral, and the highest scores were in the eastern part of the country where the climate is continental. The MODIS‐based SCD had larger relative errors in autumn, smaller errors in spring and very good agreement with the in situ observations in the middle of winter. Although the generated cloud‐free MODIS snow cover product tended to overestimate the annual SCD on average by 8.5 days, we consider it adequate to describe the inter‐annual and monthly variation of snow cover in Lithuania.

Abstract. The droughts are the phenomena which affect large areas. Remote sensing data covering large territory can be used to assess the droughts' impact and theirs extent. Drought effect on vegetation was determined using Normalized Difference Vegetation Index (NDVI) in the east Baltic Sea region located between 53–60 N and 20–30 E. The effect of precipitation deficit on vegetation in arable land, broad–leaved and coniferous forest was analysed using the Standardized Precipitation Index (SPI) calculated for 1 to 9 month time scales. The vegetation has strong seasonality in the analysed area. The beginning and the end of vegetation season depends on the distance to the Baltic Sea which affects temperature and precipitation patterns. The vegetation season duration in the south-eastern part of the region is 5–6 weeks longer than in the north-western part. The early spring air temperature, snowmelt water storage in the soil and precipitation has the largest influence on NDVI values in the first half of the growing season. The precipitation deficit in the first part of the vegetation season has a significant impact only on the vegetation in the arable land. The vegetation in the forests is less sensitive to moisture deficit. The positive correlation between 3 and 6 month SPI and vegetation condition was observed in the arable land and both types of forests in the second half of the vegetation season. The precipitation deficit is only one of the vegetation condition drivers and NDVI cannot be used universally to identify droughts, but it may be applied to better assess the effect of droughts on vegetation in the eastern Baltic Sea region.

In this study we tested and evaluated an algorithm for generating binary snow maps using Sentinel-1 SAR data on various land cover types in midlatitude lowlands. The SAR data was acquired over the Šventoji river basin in Lithuania and included five cold seasons from 2014 to 2021. Snow classification was done by thresholding SAR backscattering ratio between the image with snow and the reference image based on the snow-free SAR observations in autumn. We used ground range dual-polarized Sentinel-1 data and calculated the weighted VV and VH polarization ratio to determine land cover type-specific snow classification thresholds. The validation of snow maps derived from SAR data was done using Sentinel-2 images. Validation results showed that accuracy of SAR based snow maps over grasslands and arable land was within a 0.85–0.92 range, while in the coniferous forests it was 0.49. Results suggest that the most important factors influencing the accuracy of snow detection using Sentinel-1 SAR data are snow depth and dense forest vegetation. The study contributes to the evolving SAR-based algorithms to determine snow cover characteristics and demonstrates the capability of the Sentinel-1 mission for snow monitoring in various landscapes.

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