Allard de Wit

A Dutch multi-date land use database: Identification of real and methodological changes

Abstract Land cover and land use are important information sources for environmental issues. One of the most important changes at the Earths surface concerns land cover and land use. Knowledge about the location and type of these changes is essential for environmental modeling and management. Remote sensing data in combination with additional spatial data are recognized as an important source of information to detect these land cover and land use changes. In this paper, we introduce the Dutch multi-date land use database (LGN). Today there are six versions of the LGN database (LGN1–LGN6) based on satellite imagery of respectively 1986, 1992/1994, 1995/1997, 1999/2000, 2003/2004 and 2007/2008. With the completion of LGN6 a time-series of land use databases covering 20 years became available. The different databases were produced according to different methodologies. The resulting inconsistencies for monitoring land use changes and possible solutions are the main themes of this paper. Emerging user requirements, increased data availability and technical improvements lead to methodological differences between the LGN5 and LGN6 Dutch land use database. Monitoring of land use changes by integrating independent spatial datasets results in a mixture of real and methodological changes. A methodology is applied to detect real land use changes for a limited number of land use monitoring classes. The detected real changes have a high probability (almost 95%) that they are real changes. Next to these real changes, differences exist between LGN5 and LGN6, i.e. so-called methodological land use changes, being the result of methodological adaptations over time.

Exploring the efficiency of bias corrections of regional climate model output for the assessment of future crop yields in Europe

Excessive summer drying and reduced growing season length are expected to reduce European crop yields in future. This may be partly compensated by adapted crop management, increased CO2 concentration and technological development. For food security, changes in regional to continental crop yield variability may be more important than changes in mean yields. The assessment of changes in regional and larger scale crop variability requires high resolution and spatially consistent future weather, matching a specific climate scenario. Such data could be derived from regional climate models (RCMs), which provide changes in weather patterns. In general, RCM output is heavily biased with respect to observations. Due to the strong nonlinear relation between meteorological input and crop yields, the application of this biased output may result in large biases in the simulated crop yield changes. The use of RCM output only makes sense after sufficient bias correction. This study explores how RCM output can be bias corrected for the assessment of changes in European and subregional scale crop yield variability due to climate change. For this, output of the RCM RACMO of the Royal Netherlands Meteorological Institute was bias corrected and applied within the crop simulation model WOrld FOod STudies to simulate potential and water limited yields of three divergent crops: winter wheat, maize and sugar beets. The bias correction appeared necessary to successfully reproduce the mean yields as simulated with observational data. It also substantially improved the year-to-year variability of seasonal precipitation and radiation within RACMO, but some bias in the interannual variability remained. This is caused by the fact that the applied correction focuses on mean and daily variability. The interannual variability of growing season length, and as a consequence the potential yields too, appeared even deteriorated. Projected decrease in mean crop yields is well in line with earlier studies. No significant change in crop yield variability was found. Yet, only one RCM is analysed in this study, and it is recommended to extend this study with more climate models and a slightly adjusted bias correction taking into account the variability of larger time scales as well.

Estimating crop-specific evapotranspiration using remote-sensing imagery at various spatial resolutions for improving crop growth modelling

By governing water transfer between vegetation and atmosphere, evapotranspiration (ET) can have a strong influence on crop yields. An estimation of ET from remote sensing is proposed by the EUMETSAT ‘Satellite Application Facility’ (SAF) on Land Surface Analysis (LSA). This ET product is obtained operationally every 30 min using a simplified SVAT scheme that uses, as input, a combination of remotely sensed data and atmospheric model outputs. The standard operational mode uses other LSA-SAF products coming from SEVIRI imagery (the albedo, the downwelling surface shortwave flux, and the downwelling surface longwave flux), meteorological data, and the ECOCLIMAP database to identify and characterize the land cover. With the overall objective of adapting this ET product to crop growth monitoring necessities, this study focused first on improving the ET product by integrating crop-specific information from high and medium spatial resolution remote-sensing data. A Landsat (30 m)-based crop type classification is used to identify areas where the target crop, winter wheat, is located and where crop-specific Moderate Resolution Imaging Spectroradiometer (MODIS) (250 m) time series of green area index (GAI) can be extracted. The SVAT model was run for 1 year (2007) over a study area covering Belgium and part of France using this supplementary information. Results were compared to those obtained using the standard operational mode. ET results were also compared with ground truth data measured in an eddy covariance station. Furthermore, transpiration and potential transpiration maps were retrieved and compared with those produced using the Crop Growth Monitoring System (CGMS), which is run operationally by the European Commissions Joint Research Centre to produce in-season forecast of major European crops. The potential of using ET obtained from remote sensing to improve crop growth modelling in such a framework is studied and discussed. Finally, the use of the ET product is also explored by integrating it in a simpler modelling approach based on light-use efficiency. The Carnegie–Ames–Stanford Approach (CASA) agroecosystem model was therefore applied to obtain net primary production, dry matter productivity, and crop yield using only LSA-SAF products. The values of yield were compared with those obtained using CGMS, and the dry matter productivity values with those produced at the Flemish Institute for Technological Research (VITO). Results showed the potential of using this simplified remote-sensing method for crop monitoring.

Application of a genetic algorithm for crop model steering using NOAA-AVHRR data

The main objective of this study was to investigate whether AVHRR data could be useful for crop model simulation steering by intrinsically taking the mixed pixel effects into account. The second objective was to determine if the application of a genetic algorithm could be an effective technique for crop model steering. The principles were tested for the Seville test site using synthetic data and AVHRR data from 1995 and 1996 because these years show a large contrast in crop development. The main conclusions are that a genetic algorithm is a very powerfull technique for crop model optimisation, but adaptations are needed to the current optimisation scheme in order to be able to steer the WOFOST crop model on the basis of NOAA-AVHRR data.The main objective of this study was to investigate whether AVHRR data could be useful for crop model simulation steering by intrinsically taking the mixed pixel effects into account. The second objective was to determine if the application of a genetic algorithm could be an effective technique for crop model steering. The principles were tested for the Seville test site using synthetic data and AVHRR data from 1995 and 1996 because these years show a large contrast in crop development. The main conclusions are that a genetic algorithm is a very powerful technique for crop model optimization, but adaptations are needed to the current optimization scheme in order to be able to steer the WOFOST crop model on the basis of NOAA-AVHRR data.

ISIMIP2a Simulation Data from Agricultural Sector

The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) provides a framework for the collation of a set of consistent, multi-sector, multi-scale climate-impact simulations, based on scientifically and politically-relevant historical and future scenarios. This framework serves as a basis for robust projections of climate impacts, as well as facilitating model evaluation and improvement, allowing for improved estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming. It also provides a unique opportunity to consider interactions between climate change impacts across sectors. ISIMIP2a is the second ISIMIP simulation round, focusing on historical simulations (1971-2010) of climate impacts on agriculture, fisheries, permafrost, biomes, regional and global water and forests. This will serve as a basis for model evaluation and improvement, allowing for improved estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming. The focus topic for ISIMIP2a is model evaluation and validation, in particular with respect to the representation of impacts of extreme weather events and climate variability. During this phase, four common global observational climate data sets were provided across all impact models and sectors. In addition, appropriate observational data sets of impacts for each sector were collected, against which the models can be benchmarked. Access to the input data for the impact models is provided through a central ISIMIP archive (see https://www.isimip.org/gettingstarted/#input-data-bias-correction). This entry refers to the ISIMIP2a simulation data from Agricultural Sector models: CGMS-WOFOST, CLM-Crop, EPIC-Boku, EPIC-IIASA, EPIC-TAMU, GEPIC, LPJ-GUESS, LPJmL, ORCHIDEE-CROP, pAPSIM, pDSSAT, PEGASUS, PEPIC, PRYSBI2.