Cropping Intensity in the Aral Sea Basin and Its Dependency from the Runoff Formation 2000-2012

This study is aimed at a better understanding of how upstream runoff formation affected the cropping intensity (CI: number of harvests) in the Aral Sea Basin (ASB) between 2000 and 2012. MODIS 250 m NDVI time series and knowledge-based pixel masking that included settlement layers and topography features enabled to map the irrigated cropland extent (iCE). Random forest models supported the classification of cropland vegetation phenology (CVP: winter/summer crops, double cropping, etc.). CI and the percentage of fallow cropland (PF) were derived from CVP. Spearman’s rho was selected for assessing the statistical relation of CI and PF to runoff formation in the Amu Darya and Syr Darya catchments per hydrological year. Validation in 12 reference sites using multi-annual Landsat-7 ETM+ images revealed an average overall accuracy of 0.85 for the iCE maps. MODIS maps overestimated that based on Landsat by an average factor of ~1.15 (MODIS iCE/Landsat iCE). Exceptional overestimations occurred in case of inaccurate settlement layers. The CVP and CI maps achieved overall accuracies of 0.91 and 0.96, respectively. The Amu Darya catchment disclosed significant positive (negative) relations between upstream runoff with CI (PF) and a high pressure on the river water resources in 2000–2012. Along the Syr Darya, reduced dependencies could be observed, which is potentially linked to the high number of water constructions in that catchment. Intensified double cropping after drought years occurred in Uzbekistan. However, a 10 km × 10 km grid of Spearman’s rho (CI and PF vs. upstream runoff) emphasized locations at different CI levels that are directly affected by runoff fluctuations in both river systems. The resulting maps may thus be supportive on the way to achieve long-term sustainability of crop production and to simultaneously protect the severely threatened environment in the ASB. The gained knowledge can be further used for investigating climatic impacts of irrigation in the region.

[1]  Christopher Conrad,et al.  TiSeG: A Flexible Software Tool for Time-Series Generation of MODIS Data Utilizing the Quality Assessment Science Data Set , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Changsheng Li,et al.  Mapping paddy rice agriculture in southern China using multi-temporal MODIS images , 2005 .

[3]  N. Ramankutty,et al.  Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000 , 2008 .

[4]  Mutlu Ozdogan,et al.  A new methodology to map irrigated areas using multi-temporal MODIS and ancillary data: An application example in the continental US , 2008 .

[5]  Roger A. Pielke,et al.  The impact of agricultural intensification and irrigation on land-atmosphere interactions and Indian monsoon precipitation — A mesoscale modeling perspective , 2009 .

[6]  Yann Chemin,et al.  Using remote sensing data for water depletion assessment at administrative and irrigation-system levels: case study of the Ferghana Province of Uzbekistan , 2004 .

[7]  Stefan Zerbe,et al.  Water consumption of agriculture and natural ecosystems at the Amu Darya in Lebap Province, Turkmenistan , 2014, Environmental Earth Sciences.

[8]  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 .

[9]  S. Rakhmatullaev,et al.  Transformation of water management in Central Asia: from State-centric, hydraulic mission to socio-political control , 2013, Environmental Earth Sciences.

[10]  Christine Bichsel,et al.  Liquid Challenges: Contested Water in Central Asia , 2012 .

[11]  J. Mohan Reddy,et al.  Analysis of cotton water productivity in Fergana Valley of Central Asia. , 2012 .

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

[13]  Christopher Conrad,et al.  Analysis of uncertainty in multi-temporal object-based classification , 2015 .

[14]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[15]  Prasad S. Thenkabail,et al.  Mapping seasonal rice cropland extent and area in the high cropping intensity environment of Bangladesh using MODIS 500 m data for the year 2010 , 2014 .

[16]  Pinki Mondal,et al.  Mapping cropping intensity of smallholder farms: A comparison of methods using multiple sensors , 2013 .

[17]  Kuolin Hsu,et al.  How significant is the impact of irrigation on the local hydroclimate in California’s Central Valley? Comparison of model results with ground and remote‐sensing data , 2011 .

[18]  Steffen Fritz,et al.  Mapping Priorities to Focus Cropland Mapping Activities: Fitness Assessment of Existing Global, Regional and National Cropland Maps , 2015, Remote. Sens..

[19]  Asad Sarwar Qureshi,et al.  Salt‐induced land and water degradation in the Aral Sea basin: A challenge to sustainable agriculture in Central Asia , 2009 .

[20]  Martha C. Anderson,et al.  Landsat-8: Science and Product Vision for Terrestrial Global Change Research , 2014 .

[21]  P. Atkinson,et al.  Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture , 2012 .

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

[23]  O. Bubenzer,et al.  Remote-sensing-based analysis of landscape change in the desiccated seabed of the Aral Sea—a potential tool for assessing the hazard degree of dust and salt storms , 2013, Environmental Monitoring and Assessment.

[24]  Prasad S. Thenkabail,et al.  An Automated Cropland Classification Algorithm (ACCA) for Tajikistan by Combining Landsat, MODIS, and Secondary Data , 2012, Remote. Sens..

[25]  Lijuan Wen,et al.  Modelling and analysis of the impact of irrigation on local arid climate over northwest China , 2012 .

[26]  U. Gessner,et al.  Regional land cover mapping and change detection in Central Asia using MODIS time-series , 2012 .

[27]  Christopher Conrad,et al.  Reconstructing the Spatio-Temporal Development of Irrigation Systems in Uzbekistan Using Landsat Time Series , 2012, Remote. Sens..

[28]  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.

[29]  Ulrich Michel,et al.  Decision fusion and non-parametric classifiers for land use mapping using multi-temporal RapidEye data , 2015 .

[30]  C. Ringler,et al.  Optimizing irrigation efficiency improvements in the Aral Sea Basin , 2016 .

[31]  Philippe Le Coustumer,et al.  Facts and Perspectives of Water Reservoirs in Central Asia: A Special Focus on Uzbekistan , 2010 .

[32]  Heiko Paeth,et al.  Dynamical downscaling of climate change in Central Asia , 2013 .

[33]  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 .

[34]  Sarah L. O'Hara,et al.  Irrigation and land degradation: implications for agriculture in Turkmenistan, central Asia , 1997 .

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

[36]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[37]  Maja Schlüter,et al.  Managing water-use trade-offs in a semi-arid river delta to sustain multiple ecosystem services: a modeling approach , 2009, Ecological Research.

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

[39]  K. Sayre,et al.  Research Prospectus: A Vision for Sustainable Land Management Research in Central Asia. , 2009 .

[40]  Olena Dubovyk,et al.  Spatio-temporal analyses of cropland degradation in the irrigated lowlands of Uzbekistan using remote-sensing and logistic regression modeling , 2012, Environmental Monitoring & Assessment.

[41]  Yang Yang,et al.  Remote Sensing of Irrigated Agriculture: Opportunities and Challenges , 2010, Remote. Sens..

[42]  David B. Lobell,et al.  Irrigation cooling effect on temperature and heat index extremes , 2008 .

[43]  Igor S. Zonn,et al.  Karakum Canal: Artificial River in a Desert , 2012 .

[44]  Kai Wegerich,et al.  Water security in the Syr Darya Basin , 2015 .

[45]  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 .

[46]  Olena Dubovyk,et al.  Derivation of temporal windows for accurate crop discrimination in heterogeneous croplands of Uzbekistan using multitemporal RapidEye images , 2014 .

[47]  James Rowland,et al.  Mapping irrigated areas in Afghanistan over the past decade using MODIS NDVI , 2014 .

[48]  G. Menz,et al.  Land Suitability Assessment for Afforestation with Elaeagnus Angustifolia L. in Degraded Agricultural Areas of the Lower Amudarya River Basin , 2016 .

[49]  P. Micklin.  The Aral Sea Disaster , 2007 .

[50]  Andrew Jarvis,et al.  Hole-filled SRTM for the globe Version 4 , 2008 .

[51]  Peter Bauer-Gottwein,et al.  Will climate change exacerbate water stress in Central Asia? , 2012, Climatic Change.

[52]  David P. Roy,et al.  MODIS land data storage, gridding, and compositing methodology: Level 2 grid , 1998, IEEE Trans. Geosci. Remote. Sens..

[53]  Christopher Conrad,et al.  Assessing irrigated cropland dynamics in central Asia between 2000 and 2011 based on MODIS time series , 2012, Remote Sensing.

[54]  P. Thenkabail,et al.  Spectral Matching Techniques to Determine Historical Land-use/Land-cover (LULC) and Irrigated Areas Using Time-series 0.1-degree AVHRR Pathfinder Datasets , 2007 .

[55]  D. Lobell,et al.  Cropland distributions from temporal unmixing of MODIS data , 2004 .

[56]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

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

[58]  John P. A. Lamers,et al.  Conservation agriculture in Central Asia—What do we know and where do we go from here? , 2012 .

[59]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[60]  K. Frenken,et al.  Irrigation in Central Asia in figures: AQUASTAT Survey-2012. , 2013 .

[61]  Prasad S. Thenkabail,et al.  Influence of Resolution in Irrigated Area Mapping and Area Estimation , 2009 .

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

[63]  Ximing Cai,et al.  Sustainability analysis for irrigation water management in the Aral Sea region , 2003 .

[64]  C. Justice,et al.  Atmospheric correction of MODIS data in the visible to middle infrared: first results , 2002 .

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

[66]  Christopher Conrad,et al.  Temporal segmentation of MODIS time series for improving crop classification in Central Asian irrigation systems , 2011 .

[67]  Wenquan Zhu,et al.  A Shape-matching Cropping Index (CI) Mapping Method to Determine Agricultural Cropland Intensities in China using MODIS Time-series Data , 2012 .

[68]  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 .

[69]  C. Conrad,et al.  Mapping abandoned agricultural land in Kyzyl-Orda, Kazakhstan using satellite remote sensing , 2015 .

[70]  Feng Gao,et al.  A simple and effective method for filling gaps in Landsat ETM+ SLC-off images , 2011 .

[71]  Daniele De Wrachien,et al.  Water in Central Asia: Past, present and future, Victor A. Dukhovny and Joop L.G. de Schutter. CRC Press/Balkema, Taylor & Francis Group, London, UK, 2011. ISBN 978‐0‐415‐45962‐4 (hardback), 410 pp. , 2011 .

[72]  Fabian Löw,et al.  Defining the Spatial Resolution Requirements for Crop Identification Using Optical Remote Sensing , 2014, Remote. Sens..

[73]  K. Moffett,et al.  Remote Sens , 2015 .

[74]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[75]  M. Schull,et al.  An irrigated area map of the world (1999) derived from remote sensing , 2006 .

[76]  G. Henebry,et al.  Climate and environmental change in arid Central Asia: impacts, vulnerability, and adaptations. , 2009 .

[77]  Christopher Conrad,et al.  Mapping and assessing water use in a Central Asian irrigation system by utilizing MODIS remote sensing products , 2007 .

[78]  P. Vlek,et al.  Tree establishment under deficit irrigation on degraded agricultural land in the lower Amu Darya River region, Aral Sea Basin , 2008 .

[79]  Christopher Conrad,et al.  Adapting to water scarcity: constraints and opportunities for improving irrigation management in Khorezm, Uzbekistan , 2013 .