Mapping cropland-use intensity across Europe using MODIS NDVI time series

Global agricultural production will likely need to increase in the future due to population growth, changing diets, and the rising importance of bioenergy. Intensifying already existing cropland is often considered more sustainable than converting more natural areas. Unfortunately, our understanding of cropping patterns and intensity is weak, especially at broad geographic scales. We characterized and mapped cropping systems in Europe, a region containing diverse cropping systems, using four indicators: (a) cropping frequency (number of cropped years), (b) multi-cropping (number of harvests per year), (c) fallow cycles, and (d) crop duration ratio (actual time under crops) based on the MODIS Normalized Difference Vegetation Index (NDVI) time series from 2000 to 2012. Second, we used these cropping indicators and self-organizing maps to identify typical cropping systems. The resulting six clusters correspond well with other indicators of agricultural intensity (e.g., nitrogen input, yields) and reveal substantial differences in cropping intensity across Europe. Cropping intensity was highest in Germany, Poland, and the eastern European Black Earth regions, characterized by high cropping frequency, multi-cropping and a high crop duration ratio. Contrarily, we found lowest cropping intensity in eastern Europe outside the Black Earth region, characterized by longer fallow cycles. Our approach highlights how satellite image time series can help to characterize spatial patterns in cropping intensity—information that is rarely surveyed on the ground and commonly not included in agricultural statistics: our clustering approach also shows a way forward to reduce complexity when measuring multiple indicators. The four cropping indicators we used could become part of continental-scale agricultural monitoring in order to identify target regions for sustainable intensification, where trade-offs between intensification and the environmental should be explored.

[1]  R. DeFries,et al.  Agricultural intensification and changes in cultivated areas, 1970–2005 , 2009, Proceedings of the National Academy of Sciences.

[2]  L. Jackson,et al.  Special Issue Article: Advancing Environmental Conservation: Essays In Honor Of Navjot Sodhi Global food security, biodiversity conservation and the future of agricultural intensification , 2012 .

[3]  C. Kucharik,et al.  Closing the gap: global potential for increasing biofuel production through agricultural intensification , 2011 .

[4]  Navin Ramankutty,et al.  Mind the gap: how do climate and agricultural management explain the ‘yield gap’ of croplands around the world? , 2010 .

[5]  H. Haberl,et al.  Challenges for land system science , 2012 .

[6]  N. Ramankutty,et al.  Closing yield gaps through nutrient and water management , 2012, Nature.

[7]  Helmut Haberl,et al.  Global human appropriation of net primary production doubled in the 20th century , 2013, Proceedings of the National Academy of Sciences.

[8]  D. Tilman,et al.  Global food demand and the sustainable intensification of agriculture , 2011, Proceedings of the National Academy of Sciences.

[9]  Julian M. Allwood,et al.  Importance of food-demand management for climate mitigation , 2014 .

[10]  W. Laurance,et al.  Agricultural expansion and its impacts on tropical nature. , 2014, Trends in ecology & evolution.

[11]  J. Norman,et al.  The global distribution of cultivable lands: current patterns and sensitivity to possible climate change , 2002 .

[12]  Damien Sulla-Menashe,et al.  MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets , 2010 .

[13]  Donald W. Bouldin,et al.  A Cluster Separation Measure , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  P. Döll,et al.  Groundwater use for irrigation - a global inventory , 2010 .

[15]  A. Prishchepov,et al.  Quantifying yield gaps in wheat production in Russia , 2014 .

[16]  Pragya Agarwal,et al.  Introduction: What is a Self‐Organizing Map? , 2008 .

[17]  G. Blengini,et al.  The life cycle of rice: LCA of alternative agri-food chain management systems in Vercelli (Italy). , 2009, Journal of environmental management.

[18]  Kees Klein Goldewijk,et al.  Long-term dynamic modeling of global population and built-up area in a spatially explicit way: HYDE 3.1 , 2010 .

[19]  J. Mustard,et al.  Green leaf phenology at Landsat resolution: Scaling from the field to the satellite , 2006 .

[20]  Toshihiro Sakamoto,et al.  Analysis of rapid expansion of inland aquaculture and triple rice-cropping areas in a coastal area of the Vietnamese Mekong Delta using MODIS time-series imagery , 2009 .

[21]  N. Ramankutty,et al.  Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000 , 2008 .

[22]  John F. Mustard,et al.  A curve fitting procedure to derive inter-annual phenologies from time series of noisy satellite NDVI data , 2007 .

[23]  C. Müller,et al.  The yield gap of global grain production: A spatial analysis , 2010 .

[24]  M. Friedl,et al.  Land Surface Phenology from MODIS: Characterization of the Collection 5 Global Land Cover Dynamics Product , 2010 .

[25]  S. Robinson,et al.  Food Security: The Challenge of Feeding 9 Billion People , 2010, Science.

[26]  I. Bateman,et al.  Sustainable Intensification in Agriculture: Premises and Policies , 2013, Science.

[27]  W. Jetz,et al.  Global patterns and determinants of vascular plant diversity , 2007, Proceedings of the National Academy of Sciences.

[28]  P. Döll,et al.  MIRCA2000—Global monthly irrigated and rainfed crop areas around the year 2000: A new high‐resolution data set for agricultural and hydrological modeling , 2010 .

[29]  Anette Reenberg,et al.  A conceptual framework for analysing and measuring land-use intensity☆ , 2013, Current opinion in environmental sustainability.

[30]  Rick Mueller,et al.  Mapping global cropland and field size , 2015, Global change biology.

[31]  David M. Johnson.  A 2010 map estimate of annually tilled cropland within the conterminous , 2013 .

[32]  Christopher J. Kucharik,et al.  Data and monitoring needs for a more ecological agriculture , 2011 .

[33]  Helmut Haberl,et al.  Natural and socioeconomic determinants of the embodied human appropriation of net primary production and its relation to other resource use indicators , 2012, Ecological indicators.

[34]  Ujjwal Maulik,et al.  Performance Evaluation of Some Clustering Algorithms and Validity Indices , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[35]  P. Hostert,et al.  Mapping farmland abandonment and recultivation across Europe using MODIS NDVI time series. , 2015 .

[36]  Darcy Boellstorff,et al.  Impacts of set-aside policy on the risk of soil erosion in central Spain , 2005 .

[37]  Petra Döll,et al.  Global Patterns of Cropland Use Intensity , 2010, Remote. Sens..

[38]  Steffen Fritz,et al.  Highlighting continued uncertainty in global land cover maps for the user community , 2011 .

[39]  J. Lamarque,et al.  Global Biodiversity: Indicators of Recent Declines , 2010, Science.

[40]  K. D. de Beurs,et al.  Use of Landsat and MODIS data to remotely estimate Russia’s sown area , 2014 .

[41]  J.W.A. Langeveld,et al.  Analyzing the effect of biofuel expansion on land use in major producing countries: evidence of increased multiple cropping , 2014 .

[42]  Patrick Hostert,et al.  Challenges and opportunities in mapping land use intensity globally , 2013, Current opinion in environmental sustainability.

[43]  B. Koch,et al.  Agricultural land abandonment and natural forest re-growth in the Swiss mountains: A spatially explicit economic analysis , 2007 .

[44]  A. Gibon,et al.  Agricultural abandonment in mountain areas of Europe: Environmental consequences and policy response , 2000 .

[45]  Deepak K. Ray,et al.  Increasing global crop harvest frequency: recent trends and future directions , 2013 .

[46]  S. Carpenter,et al.  Solutions for a cultivated planet , 2011, Nature.

[47]  Per Jönsson,et al.  TIMESAT - a program for analyzing time-series of satellite sensor data , 2004, Comput. Geosci..

[48]  S. Polasky,et al.  Agricultural sustainability and intensive production practices , 2002, Nature.

[49]  Kathleen Neumann,et al.  Challenges in using land use and land cover data for global change studies , 2011 .

[50]  Steffen Fritz,et al.  The Need for Improved Maps of Global Cropland , 2013 .

[51]  W. Parton,et al.  Agricultural intensification and ecosystem properties. , 1997, Science.

[52]  Xiangming Xiao,et al.  Quantifying the area and spatial distribution of double- and triple-cropping croplands in India with multi-temporal MODIS imagery in 2005 , 2011 .

[53]  J. Rogan,et al.  Remote sensing technology for mapping and monitoring land-cover and land-use change , 2004 .

[54]  N. Ramankutty,et al.  Characterizing the Spatial Patterns of Global Fertilizer Application and Manure Production , 2010 .

[55]  J. Mustard,et al.  Wavelet analysis of MODIS time series to detect expansion and intensification of row-crop agriculture in Brazil , 2008 .

[56]  P. Verburg,et al.  Mapping and modelling of changes in agricultural intensity in Europe , 2011 .

[57]  Marcos Adami,et al.  Recent cropping frequency, expansion, and abandonment in Mato Grosso, Brazil had selective land characteristics , 2014 .

[58]  B. Wardlow,et al.  Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the U.S. Central Great Plains , 2008 .

[59]  Mark A. Friedl,et al.  Mapping Crop Cycles in China Using MODIS-EVI Time Series , 2014, Remote. Sens..

[60]  Mark A. Friedl,et al.  Global rain-fed, irrigated, and paddy croplands: A new high resolution map derived from remote sensing, crop inventories and climate data , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[61]  P. Leitão,et al.  Comparing the determinants of cropland abandonment in Albania and Romania using boosted regression trees , 2013 .

[62]  C. Woodcock,et al.  Post-Soviet farmland abandonment, forest recovery, and carbon sequestration in western Ukraine , 2011 .

[63]  Carsten F. Dormann,et al.  Assessing the intensity of temperate European agriculture at the landscape scale , 2006 .

[64]  N. Ramankutty,et al.  Characterizing patterns of global land use: An analysis of global croplands data , 1998 .

[65]  Obi Reddy P. Gangalakunta,et al.  Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium , 2009 .

[66]  Thomas Giesecke,et al.  Projecting the future distribution of European potential natural vegetation zones with a generalized, tree species-based dynamic vegetation model , 2012 .

[67]  N. Mueller,et al.  Leverage points for improving global food security and the environment , 2014, Science.

[68]  A. Strahler,et al.  Climate controls on vegetation phenological patterns in northern mid‐ and high latitudes inferred from MODIS data , 2004 .

[69]  T. Beringer,et al.  Bioenergy production potential of global biomass plantations under environmental and agricultural constraints , 2011 .

[70]  J. Nicolau,et al.  Abandonment of agricultural land: an overview of drivers and consequences , 2007 .

[71]  V. Radeloff,et al.  Effects of institutional changes on land use: agricultural land abandonment during the transition from state-command to market-driven economies in post-Soviet Eastern Europe , 2012 .