A Data-Intensive Approach to Address Food Sustainability: Integrating Optic and Microwave Satellite Imagery for Developing Long-Term Global Cropping Intensity and Sowing Month from 2001 to 2015

It is necessary to develop a sustainable food production system to ensure future food security around the globe. Cropping intensity and sowing month are two essential parameters for analyzing the food–water–climate tradeoff as food sustainability indicators. This study presents a global-scale analysis of cropping intensity and sowing month from 2000 to 2015, divided into three groups of years. The study methodology integrates the satellite-derived normalized vegetation index (NDVI) of 16-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) and daily land-surface-water coverage (LSWC) data obtained from The Advanced Microwave Scanning Radiometer (AMSR-E/2) in 1-km aggregate pixel resolution. A fast Fourier transform was applied to normalize the MODIS NDVI time-series data. By using advanced methods with intensive optic and microwave time-series data, this study set out to anticipate potential dynamic changes in global cropland activity over 15 years representing the Millennium Development Goal period. These products are the first global datasets that provide information on crop activities in 15-year data derived from optic and microwave satellite data. The results show that in 2000–2005, the total global double-crop intensity was 7.1 million km2, which increased to 8.3 million km2 in 2006–2010, and then to approximately 8.6 million km2 in 2011–2015. In the same periods, global triple-crop agriculture showed a rapid positive growth from 0.73 to 1.12 and then 1.28 million km2, respectively. The results show that Asia dominated double- and triple-crop growth, while showcasing the expansion of single-cropping area in Africa. The finer spatial resolution, combined with a long-term global analysis, means that this methodology has the potential to be applied in several sustainability studies, from global- to local-level perspectives.

[1]  C. Müller,et al.  Modelling the role of agriculture for the 20th century global terrestrial carbon balance , 2007 .

[2]  Matti Kummu,et al.  Gridded global datasets for Gross Domestic Product and Human Development Index over 1990–2015 , 2018, Scientific Data.

[3]  M. Kummu,et al.  Two-thirds of global cropland area impacted by climate oscillations , 2018, Nature Communications.

[4]  Kenneth Grogan,et al.  A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring , 2016, Remote. Sens..

[5]  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 .

[6]  Tomoaki Miura,et al.  Phenological Classification of the United States: A Geographic Framework for Extending Multi-Sensor Time-Series Data , 2010, Remote. Sens..

[7]  Wolfram Mauser,et al.  Global Agricultural Land Resources – A High Resolution Suitability Evaluation and Its Perspectives until 2100 under Climate Change Conditions , 2014, PloS one.

[8]  Jin Chen,et al.  Global land cover mapping at 30 m resolution: A POK-based operational approach , 2015 .

[9]  B. Wylie,et al.  NDVI saturation adjustment: A new approach for improving cropland performance estimates in the Greater Platte River Basin, USA , 2013 .

[10]  L. Rieseberg,et al.  Trends in Global Agricultural Land Use: Implications for Environmental Health and Food Security. , 2018, Annual review of plant biology.

[11]  Kyle Frankel Davis,et al.  Increased food production and reduced water use through optimized crop distribution , 2017, Nature Geoscience.

[12]  Keiji Imaoka,et al.  AMSR/AMSR-E level 2 and 3 algorithm developments and data validation plans of NASDA , 2003, IEEE Trans. Geosci. Remote. Sens..

[13]  M. Huijbregts,et al.  FLO1K, global maps of mean, maximum and minimum annual streamflow at 1 km resolution from 1960 through 2015 , 2018, Scientific Data.

[14]  F. Creutzig,et al.  Future urban land expansion and implications for global croplands , 2016, Proceedings of the National Academy of Sciences.

[15]  C. Woodcock,et al.  Resolution dependent errors in remote sensing of cultivated areas , 2006 .

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

[17]  Khalid Rehman Hakeem,et al.  Sustainable Crop Production System , 2016 .

[18]  A. Huete,et al.  A feedback based modification of the NDVI to minimize canopy background and atmospheric noise , 1995 .

[19]  Christopher O. Justice,et al.  Estimating Global Cropland Extent with Multi-year MODIS Data , 2010, Remote. Sens..

[20]  A. B. Harto,et al.  Contextualizing Mangrove Forest Deforestation in Southeast Asia Using Environmental and Socio-Economic Data Products , 2019, Forests.

[21]  A. Strahler,et al.  Monitoring vegetation phenology using MODIS , 2003 .

[22]  Steffen Fritz,et al.  A Unified Cropland Layer at 250 m for Global Agriculture Monitoring , 2016, Data.

[23]  Petra Döll,et al.  Global modeling of irrigation water requirements , 2002 .

[24]  E. Stehfest,et al.  Anthropogenic land use estimates for the Holocene – HYDE 3.2 , 2016 .

[25]  Zhongbo Su,et al.  Potential of Using Remote Sensing Techniques for Global Assessment of Water Footprint of Crops , 2010, Remote. Sens..

[26]  Keiji Imaoka,et al.  The Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), NASDA's contribution to the EOS for global energy and water cycle studies , 2003, IEEE Trans. Geosci. Remote. Sens..

[27]  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 .

[28]  Min Jiang,et al.  Decreasing Rice Cropping Intensity in Southern China from 1990 to 2015 , 2018, Remote. Sens..

[29]  Mutlu Ozdogan,et al.  The spatial distribution of crop types from MODIS data: Temporal unmixing using Independent Component Analysis , 2010 .

[30]  T. Iizumi,et al.  Modeling the Global Sowing and Harvesting Windows of Major Crops Around the Year 2000 , 2019, Journal of Advances in Modeling Earth Systems.

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

[32]  L. You,et al.  Global cropping intensity gaps: Increasing food production without cropland expansion , 2018, Land Use Policy.

[33]  Mark A. Friedl,et al.  Mapping Asian Cropping Intensity With MODIS , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[34]  Markus Metz,et al.  Fourier transforms for detecting multitemporal landscape fragmentation by remote sensing , 2013 .

[35]  Peter H. Verburg,et al.  Meeting global land restoration and protection targets: What would the world look like in 2050? , 2018, Global Environmental Change.

[36]  Naota Hanasaki,et al.  A seawater desalination scheme for global hydrological models , 2016 .

[37]  Patrick Hostert,et al.  Mapping cropland-use intensity across Europe using MODIS NDVI time series , 2016 .

[38]  M. Yokozawa,et al.  Modelling the impacts of weather and climate variability on crop productivity over a large area: A new process-based model development, optimization, and uncertainties analysis , 2009 .

[39]  Chen Zhongxin,et al.  Crop discrimination in Northern China with double cropping systems using Fourier analysis of time-series MODIS data , 2008 .

[40]  T. Sakamoto,et al.  A crop phenology detection method using time-series MODIS data , 2005 .

[41]  S. Kanae,et al.  Global Hydrological Cycles and World Water Resources , 2006, Science.

[42]  Patrick Hostert,et al.  Global-scale patterns and determinants of cropping frequency in irrigation dam command areas , 2018 .

[43]  Martin Kaltschmitt,et al.  Sustainable Crop Production: Definition and Methodological Approach for Assessing and Implementing Sustainability , 1999 .

[44]  N. Pettorelli,et al.  Using the satellite-derived NDVI to assess ecological responses to environmental change. , 2005, Trends in ecology & evolution.

[45]  Samuel Loewenberg Global food crisis looks set to continue , 2008, The Lancet.

[46]  Z. Tadele Raising Crop Productivity in Africa through Intensification , 2017 .

[47]  D. Deryng,et al.  Crop planting dates: an analysis of global patterns. , 2010 .

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

[49]  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 .

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

[51]  K. Seto,et al.  Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools , 2012, Proceedings of the National Academy of Sciences.

[52]  Wataru Takeuchi,et al.  Development of Global Cropland Agreement Level Analysis by Integrating Pixel Similarity of Recent Global Land Cover Datasets , 2017 .

[53]  W. Verhoef,et al.  Reconstructing cloudfree NDVI composites using Fourier analysis of time series , 2000 .

[54]  Kenji Tanaka,et al.  SACRA – a method for the estimation of global high-resolution crop calendars from a satellite-sensed NDVI , 2015 .

[55]  Mirco Boschetti,et al.  RiceAtlas, a spatial database of global rice calendars and production , 2017, Scientific Data.

[56]  J. Foley,et al.  Yield Trends Are Insufficient to Double Global Crop Production by 2050 , 2013, PloS one.

[57]  Dafang Zhuang,et al.  Advances in Multi-Sensor Data Fusion: Algorithms and Applications , 2009, Sensors.

[58]  K. Tatsumi Cropping intensity and seasonality parameters across Asia extracted by multi-temporal SPOT vegetation data , 2016 .

[59]  W. Mauser,et al.  Are urban areas endangering the availability of rainfed crop suitable land? , 2012 .

[60]  T. Iizumi,et al.  How do weather and climate influence cropping area and intensity , 2015 .

[61]  Wataru Takeuchi,et al.  Estimation of Methane Emissions from Rice Paddies in the Mekong Delta Based on Land Surface Dynamics Characterization with Remote Sensing , 2018, Remote. Sens..

[62]  Steffen Fritz,et al.  Improved global cropland data as an essential ingredient for food security , 2015 .

[63]  Wataru Takeuchi,et al.  Land Surface Water Coverage Estimation with PALSAR and AMSR-E for Large Scale Flooding Detection , 2016 .

[64]  A. Gitelson Wide Dynamic Range Vegetation Index for remote quantification of biophysical characteristics of vegetation. , 2004, Journal of plant physiology.

[65]  C. Müller,et al.  Climate‐driven simulation of global crop sowing dates , 2012 .

[66]  K. Tully,et al.  Untangling a Decline in Tropical Forest Resilience: Constraints on the Sustainability of Shifting Cultivation Across the Globe , 2010 .

[67]  Kabir,et al.  Distribution of Crops and Cropping Patterns in Bangladesh , 2018, Bangladesh Rice Journal.

[68]  Changsheng Li,et al.  Combining remote sensing and ground census data to develop new maps of the distribution of rice agriculture in China , 2002 .

[69]  Bin Chen GLOBALLY INCREASED CROP GROWTH AND CROPPING INTENSITY FROM THE LONG-TERM SATELLITE-BASED OBSERVATIONS , 2018 .

[70]  Robinson I. Negrón Juárez,et al.  FFT ANALYSIS ON NDVI ANNUAL CYCLE AND CLIMATIC REGIONALITY IN NORTHEAST BRAZIL , 2001 .

[71]  Rolf Weingartner,et al.  Global monthly water stress: 2. Water demand and severity of water stress , 2011 .

[72]  J. Porter,et al.  Crop responses to climatic variation , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[73]  Krishna Prasad Vadrevu,et al.  Mapping Double and Single Crop Paddy Rice With Sentinel-1A at Varying Spatial Scales and Polarizations in Hanoi, Vietnam , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[74]  Hao Jiang,et al.  Assessing Consistency of Five Global Land Cover Data Sets in China , 2014, Remote. Sens..