In 2014 a catastrophic ice storm occurred in the forests of Börzsöny Mts., Hungary. In this study we analyzed the potential of Synthetic Aperture Radar (SAR) data, complemented, and compared with optical imagery, in mapping this event. Great emphasis was put on reference data: three types of field-based reference datasets were used and the damaged patches were delineated manually based on the visual interpretation of pre- and post-event orthophotos. Four classifications with different set-ups were carried out by applying the eXtreme Gradient Boosting method. Combinations of radar backscatter coefficients, polarimetric descriptors, interferometric coherence, and optical data variables were tested. All classifications were suitable for identifying uprooted trees properly (1–11% underestimation), but none of them could detect crown loss accurately (55–58% overestimation), based on the validation of the most damaged area. Proper differentiation of healthy forests with various levels of canopy closure in the reference data seems crucial for accurate canopy loss estimation. In the case of methods using only Sentinel-1 imagery, interferometric coherence together with polarimetric descriptors provided the best results (OA: 65.7%). This setup can be useful for immediate uproot damage detection for planning salvage logging if a natural disturbance happens outside the vegetation period.

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.

Accurate crop yield information is crucial for regional agricultural monitoring; however, many existing approaches rely on complex models or extensive input datasets. This study presents a low-complexity method for estimating county-level maize and sunflower yields in Fejér County, Hungary, using Harmonized Landsat–Sentinel (HLS) Normalized Difference Vegetation Index (NDVI) time series at 30 m spatial resolution. Seasonal NDVI profiles were smoothed using a double-Gaussian fitting approach. Two modelling strategies were investigated: a robust approach using all agricultural pixels and a crop-specific approach restricted to maize or sunflower pixels. The models were tested through leave-one-year-out cross-validation against official yield statistics. For maize, the crop-specific predictive model provided the most accurate estimates (R² = 0.997; mean absolute percentage error (MAPE) = 2.0%). The MAPE remained below 4% even about 30–50 days before the end of harvest. For sunflower, the highest accuracy was obtained using the robust predictive model (R² = 0.928; MAPE = 2.73%). All models showed stable performance across years, including the extreme drought year of 2022. These findings indicate that a simple NDVI-based method can provide reliable county-scale yield estimates and may serve as a practical component in regional monitoring or early-warning systems.

Organic fertilizers are widely used around the world, because they support the circular economy, sustainable agriculture, and improved soil quality, as well as carbon sequestration. State-of-the-art, process-based models can simulate the environmental impacts of organic fertilizer use and can address issues like the effect of fertilizer amount and type on crop production, soil fertility, soil organic matter accumulation, nitrate leaching, and greenhouse gas emission. However, the lack of information on the proper attribute settings for fertilizer inputs in the models hampers their application. In this study, the main goal was to support the setting of organic fertilizer attributes for process-based model applications. A comprehensive data collection was performed to gather organic fertilizer attributes that are relevant for the carbon and nitrogen cycle-related simulations. Based on the literature search, representative values are presented that can be instantly used in the models as generalized settings for several farmyard manure and slurry types. We also addressed the question of how fertilizer attribute-setting-related uncertainties propagate to the simulation outputs. We used the Biome-BGCMuSo biogeochemical model for that purpose with a maize monoculture simulation. The results indicate that manure type specific attribute setting is crucial for the nitrogen balance related model variables. For soil nitrous oxide efflux, improper composition settings can severely distort the simulation results. Sensitivity analysis suggested that dry matter content and organic nitrogen content are the two most important manure attributes that modellers must properly adjust. For slurry, the dry matter and ammonium content must be constrained for proper simulation results. The study supports crop and biogeochemical model setup with ready-to-use pragmatic information.

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.