Recently, the Global Map of Salt-affected Soils (GSSmap) was launched, which pursued a country-driven approach and aimed to update the global and country-level information on salt-affected soils (SAS). The aim of this paper was to present how Hungary contributed to GSSmap by preparing its own SAS maps using advanced digital soil mapping techniques. We used not just a combination of random forest and multivariate geostatistical techniques for predicting the spatial distribution of SAS indicators (i.e., pH, electrical conductivity and exchangeable sodium percentage) for the topsoil (0–30 cm) and subsoil (30–100 cm), but also a number of indices derived from Sentinel-2 satellite images as environmental covariates. The importance plots of random forests showed that in addition to climatic, geomorphometric parameters and legacy soil information, image indices were the most important covariates. The performance of spatial modelling was checked by 10-fold cross validation showing that the accuracy of the SAS maps was acceptable. By this study and by the resulting maps of it, we not just contributed to GSSmap, but also renewed the SAS mapping methodology in Hungary, where we paid special attention to modelling and quantifying the prediction uncertainty that had not been quantified or even taken into consideration earlier.

Mapping and assessing ecosystem services (ES) projects at the national level have been implemented recently in the European Union in order to comply with the targets set out in the EU’s Biodiversity Strategy for 2020 and later in the Strategy for 2030. In Hungary this work has just been accomplished in a large-scale six-year project. The Hungarian assessment was structured along the ES cascade with each level described by a set of indicators. We present the selected and quantified indicators for 12 ES. For the assessment of cascade level 4, human well-being, a set of relevant well-being dimensions were selected. The whole process was supported by several forms of involvement, interviews, consultations and workshops and in thematic working groups performing the ES quantifications, followed by building scenarios and synthesizing maps and results. Here we give an overview of the main steps and results of the assessment, discuss related conceptual issues and recommend solutions that may be of international relevance. We refine some definitions of the cascade levels and suggest theoretical extensions to the cascade model. By finding a common basis for ES assessments and especially for national ones, we can ensure better comparability of results and better adoption in decision making.

A gyepek, füves élőhelyek kiterjedésének és állapotának ismerete, valamint ezek monitorozása egyre inkább előtérbe kerülő kérdés. Magyarországon is számos szakterületnek lenne szüksége arra, hogy a különböző gyeptípusok kiterjedésével, ökológiai állapotával, a gyepek hozamával és egyéb tulajdonságaikkal kapcsolatos adatok egy egységes és átlátható rendszerben, rendszeresen frissülő információtartalommal rendelkezésre álljanak. Egy ilyen adatbázis hatékonyan megalapozná a megőrzés, a fenntartás és a hasznosítás lehetőségeinek és módjainak tervezését, monitorozását. Magyarországon egyelőre nincs olyan térbeli adatbázis, amely mindezen igények összességét országos szinten ki tudná szolgálni. Létezik ugyanakkor számos, a felszínborításról és/vagy földhasználatról információt nyújtó adat és téradat, melyekből kiindulhatunk egy országos rendszer megteremtéséhez. Cikkünk célja, hogy ismertessünk néhányat ezek közül, különös tekintettel a Lechner Tudásközpont által előállított felszínborítás térképekre. Az ismertetett adatbázisok gyepekre vonatkozó területi adatainak összevetése során kiderül, hogy annak a kérdésnek a megválaszolása sem egyszerű, hogy pontosan mennyi gyep van ma Magyarországon. Ennek oka többek között, hogy az adatbázisok eltérő módszertannal és nómenklatúrával, eltérő célok mentén készülnek. Ugyanakkor a rengeteg tapasztalat, amely összegyűlt ezeknek és egyéb adatbázisoknak a létrehozása során, fontos kiindulópont lehet a közös gondolkodáshoz, amellyel egy általános gyepkataszter, gyepregiszter vagy gyepgazdálkodási adatbázis megalapozható Magyarországon.

Grassland state assessment is essential given their vital role in food security, carbon sequestration and other ecosystem services. Harvested aboveground biomass (HAB), aboveground net primary production (ANPP) and net primary production (NPP) are among the most important grassland state indicators. However, spatially explicit production estimates are largely lacking, and grassland area estimations also remain uncertain. This study addresses these gaps for drought-prone Central European grasslands over 2017-2024. We synthesized grassland extent data, collected extensive field measurements, and used remote sensing-based biophysical proxies to build an ensemble of eight linear models for spatial extrapolation at 10 m resolution. The proxies explained 11-41% of observed biomass (BM) variability. The ensemble mean ANPP was 325.7±56.6 gBM m2 year1 (median: 316 gBM m2 year1), with modest overall interannual variability. Upscaled country-wide ANPP averaged 35.1±9.2 Mt BM year-1 annually (range: 31.9-38.3; median: 35.9 Mt BM year-1). Uncertainty from grassland area estimation was roughly twice that from model choice. Using literature and local data, NPP was estimated at 421±110 gC m2 year-1 (median value), showing low interannual variability. Results highlight grassland area uncertainty as the dominant source of error in biomass estimation, rather than the remote sensing models themselves.

Monitoring the condition of grasslands is essential given their vital role in food security, carbon sequestration and other ecosystem services. Harvested aboveground biomass (HAB) and aboveground net primary production (ANPP) are among the most important grassland state indicators. However, spatially explicit production estimates are largely lacking, and grassland area estimations also remain uncertain. This study addresses these gaps for drought-prone Central European grasslands over 2017–2024. We synthesized grassland extent data, collected extensive field measurements on biomass (BM), and used remote sensing-based biophysical proxies to build an ensemble of six linear models for spatial extrapolation at 10 m resolution. Bayesian framework was used for the linear model fitting that also considers uncertainty of the observations. The ensemble mean ANPP was 310.7 ± 19 gBM m⁻², with modest interannual variability. Upscaled country-wide mean ANPP was 34.3 ± 13.3 Mt year⁻¹. The results indicate that, within the frame of the present study, the remote sensing-based linear model selection has a larger influence on the country totals than the grassland area database selection. The results highlight that both grassland area uncertainty and model construction are major sources of uncertainty in biomass estimation that have to be addressed in future studies.