Two methods for estimating the yield of different crops in Hungary from satellite remote sensing data are presented. The steps of preprocessing the remote sensing data (for geometric, radiometric, atmospheric and cloud scattering correction) are described. In the first method developed for field level estimation, reference crop fields were selected by using Landsat Thematic Mapper (TM) data for classification. A new vegetation index (General Yield Unified Reference Index (GYURI)) was deduced using a fitted double-Gaussian curve to the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data during the vegetation period. The correlation between GYURI and the field level yield data for corn for three years was R ²=0.75. The county-average yield data showed higher correlation (R ²=0.93). A significant distortion from the model gave information of the possible stress of the field. The second method presented uses only NOAA AVHRR and officially reported county-level yield data. The county-level yield data and the deduced vegetation index, GYURRI, were investigated for eight different crops for eight years. The obtained correlation was high (R ²=84.6–87.2). The developed robust method proved to be stable and accurate for operational use for county-, region- and country-level yield estimation. The method is simple and inexpensive for application in developing countries, too.

Socio-hydrology is the science of human influence on hydrology and the influence of the water cycle on human social systems. This newly emerging discipline inherently involves a historic perspective, often focusing on timescales of several centuries. While data on human history is typically available for this time frame, gathering information on the hydrological situation during such a period can prove difficult: measured hydrological data for such long periods are rare, while models and secondary data sets from geomorphology, pedology or archaeology are typically not accurate enough over such a short time. In the first part of this study, the use of historic maps in hydrology is reviewed. Major breakthroughs were the acceptance of historic map content as valid data, the use of preserved features for investigating situations earlier than the map, and the onset of digital georeferencing and data integration. Historic maps can be primary quantitative sources of hydro-geomorphological information, they can provide a context for point-based measurements over larger areas, and they can deliver time series for a better understanding of change scenarios. In the second part, a case study is presented: water level fluctuations of Lake Balaton were reconstructed from maps, levelling logs and other documents. An 18th century map system of the whole 5700 km² catchment was georeferenced, integrated with two 19th century map systems, and wetlands, forests and open water digitized. Changes in wetland area were compared with lake water level changes in a 220 yr time series. Historic maps show that the water level of the lake was closer to present-day levels than expected, and that wetland loss pre-dates drainage of the lake. The present and future role of historic maps is discussed. Historic hydrological data has to be treated with caution: while it is possible to learn form the past, the assumption that future changes will be like past changes does not always hold. Nevertheless, old maps are relatively accessible data sets and the knowledge base for using them is rapidly growing, and it can be expected that long-term time series will be established by integrating georeferenced map systems over large areas. In the Appendix, a step-by-step guide to using historic maps in hydrology is given, starting from finding a map, through georeferencing and processing the map to publication of the results.