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.

This study examined the influence of climate variability on vegetation dynamics in Mongolia from 2000 to 2024, using ERA5-Land reanalysis data together with the Normalized Difference Vegetation Index (NDVI) and Gross Primary Productivity (GPP) indicators. The results show a statistically significant mean annual air temperature increase of 0.94 °C, with the most pronounced warming occurring in March (>1.5 °C/10 years). Annual precipitation increased by 32 mm (~13%), mainly in the northern and eastern regions. At the same time, the maximum NDVI increased at a rate of 0.025 units/10 years, particularly in the north and east, while no change or slight decline was observed in the central steppes during May–June. During the study period, the average annual GPP increased by 38%, from 0.25 to 0.35 kgCm⁻², with the highest gains observed in northern forests and eastern steppes. Correlation analysis revealed that NDVI is most sensitive to temperature in early spring (r = 0.31) and to precipitation in summer (r = 0.45–0.50). GPP primarily is driven by temperature in spring (r = 0.68) and by precipitation during summer (r = 0.30). The results of this study indicate that vegetation productivity in Mongolia is sensitive to seasonal climate variability, with temperature being the primary factor influencing spring growth and precipitation controlling summer growth.

Straipsnyje vertinami palydovinės informacijos apie teritorijos padengtumą sniegu trūkių užpildymo metodai. Buvo naudojami dviejų NASA palydovų Terra ir Aqua bei Lietuvos meteorologijos stočių tinklui priklausančių stočių sniego dangos duomenys 2013–2016 m. spalio–balandžio mėnesiais. Naudojant du skirtingus būdus (pirmyn ir atgal) buvo užpildyti palydovinių sniego dangos duomenų sekų trūkiai, atsirandantys dėl ištisinio debesuotumo. Gauti dydžiai palyginti su Lietuvos meteorologijos stotyse nustatytu dienų su sniego danga skaičiumi. Analizuojamu 2013–2016 m. laikotarpiu šaltojo sezono mėnesiais palydovinių duomenų trūkiai sudarė 60 % nuo bendro dienų skaičiaus. 2013–2016 m. žiemomis vidutinis dienų su sniego danga skaičius Lietuvos meteorologijos stotyse buvo 47. Remiantis Terra palydovo duomenis, šis dydis buvo 44 dienos, o pagal Aqua perduotą informaciją – 55 dienos. Kiek didesnis dienų su sniego danga skaičius nustatytas trūkius užpildžius pirmyn metodu, tačiau skirtumas yra nedidelis ir abiejų palydovų duomenys neviršija vienos dienos. Vidutiniškai sezoninis dienų su sniego danga skaičius, nustatytas remiantis palydovų duomenimis, nuo išmatuoto atskirose meteorologijos stotyse skiriasi 9 dienomis pagal Terra ir 13 dienų pagal Aqua palydovo duomenis. Dažni atodrėkiai, sniego dangos fragmentacija ir didelis debesuotumas lėmė nemažus skirtumus tarp palydovinės bei meteorologijos stočių informacijos.