Wheat is one of the most important crops in Hungary, which represents approximately 20% of the entire agricultural area of the country, and about 40% of cereals. A robust yield method has been improved for estimating and forecasting wheat yield in Hungary in the period of 2003–2015 using normalized difference vegetation index (NDVI) derived from the data of the Moderate Resolution Imaging Spectroradiometer. Estimation was made at the end of June – it is generally the beginning of harvest of winter wheat in Hungary – while the forecasts were performed 1–7 weeks earlier. General yield unified robust reference index (GYURRI) vegetation index was calculated each year using different curve-fitting methods to the NDVI time series. The correlation between GYURRI and country level yield data gave correlation coefficient (r) of 0.985 for the examined 13 years in the case of estimation. Simulating a quasi-operative yield estimation process, 10 years’ (2006–2015) yield data was estimated. The differences between the estimated and actual yield data provided by the Hungarian Central Statistical Office were less than 5%, the average difference was 2.5%. In the case of forecasting, these average differences calculated approximately 2 and 4 weeks before the beginning of harvest season were 4.5% and 6.8%, respectively. We also tested the yield estimation procedure for smaller areas, for the 19 counties (Nomenclature of Territorial Units for Statistics-3 level) of Hungary. We found that, the relationship between GYURRI and the county level yield data had r of 0.894 for the years 2003–2014, and by simulating the quasi-operative forecast for 2015, the resulting 19 county average yield values differed from the actual yield as much as 8.7% in average.

Remote sensing-based crop yield estimation methods rely on vegetation indices, which depend on the availability of the number of observations during the year, influencing the value of the derived crop yield. In the present study, a robust yield estimation method was improved for estimating the yield of corn, winter wheat, sunflower, and rapeseed in Hungary for the period 2000–2020 using 16 vegetation indices. Then, meteorological data were used to reduce the differences between the estimated and census yield data. In the case of corn, the best result was obtained using the Green Atmospherically Resistant Vegetation Index, where the correlation between estimated and census data was R² = 0.888 before and R² = 0.968 after the meteorological correction. In the case of winter wheat, the Difference Vegetation Index produced the best result with R² = 0.815 and 0.894 before and after the meteorological correction. For sunflower, these correlation values were 0.730 and 0.880, and for rapeseed, 0.765 and 0.922, respectively. Using the meteorological correction, the average percentage differences between estimated and census data decreased from 7.7% to 3.9%, from 6.7% to 3.9%, from 7.2% to 4.2%, and from 7.8% to 5.1% in the case of corn, winter wheat, sunflower, and rapeseed, respectively.

Models of determining the effects of the bidirectional reflectance distribution function (BRDF) of different surfaces and of eliminating the effect of Sun-sensor-target geometry from the remotely sensed satellite data are actual. The objective of this study is to develop a simple relation between the Sun-sensor-target geometry and the remotely sensed vegetation index. In this investigation 238 National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) images were used over Hungary during the period 1996-98. The greenness vegetation index (the difference between the reflectance values of near-infrared and visible channels) was used between days of the year 140-200, because the greenness values can be considered as constant in this period over the agricultural areas. The so-called 'hot spot effect' can be observed in the variation of reflectance values with different viewing zenith angles of the sensor. A simple quadratic relation was found between the raw AVHRR greenness values and the angle enclosed by the Sun-target and target-sensor directions over the agricultural areas, forests and grasslands. A correction method was developed to eliminate the effect of the Sun-sensor-target geometry, which it is hoped will improve the accuracy of yield forecasting and estimation procedures using NOAA AVHRR data.

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.