Mapping Irrigated Areas of Northeast China in Comparison to Natural Vegetation

Accurate information about the location and extent of irrigation is fundamental to many aspects of food security and water resource management. This study develops a new method for identifying irrigation in northeastern China by comparing canopy moisture between the cropland and adjacent natural ecosystems (i.e., forests). This method is based on two basic assumptions, which we validated using field survey data. First, the canopy moisture of irrigated cropland, indicated by a satellite-based land surface water index (LSWI), is higher than that of the adjacent forest. Second, the difference in LSWI between irrigation cropland and forest is larger in arid regions than in humid regions. Based on the field survey and statistical dataset, our method performed well in indicating spatial variations of irrigated areas. Results from this study suggest that our method is a promising tool for mapping irrigated areas, as it is a general and repeatable method that does not rely on training samples and can be applied to other regions.

[1]  W. Dong,et al.  Opportunistic Market‐Driven Regional Shifts of Cropping Practices Reduce Food Production Capacity of China , 2018 .

[2]  Jiefang Dong,et al.  A sub-pixel method for estimating planting fraction of paddy rice in Northeast China , 2018 .

[3]  Damien Arvor,et al.  Remote Sensing and Cropping Practices: A Review , 2018, Remote. Sens..

[4]  Cunjun Li,et al.  Spring green-up phenology products derived from MODIS NDVI and EVI: Intercomparison, interpretation and validation using National Phenology Network and AmeriFlux observations , 2017 .

[5]  Stéphane Dupuy,et al.  A Combined Random Forest and OBIA Classification Scheme for Mapping Smallholder Agriculture at Different Nomenclature Levels Using Multisource Data (Simulated Sentinel-2 Time Series, VHRS and DEM) , 2017, Remote. Sens..

[6]  Rui Sun,et al.  Mapping Irrigated and Rainfed Wheat Areas Using Multi-Temporal Satellite Data , 2016, Remote. Sens..

[7]  Luciano Vieira Dutra,et al.  Mapping Fractional Cropland Distribution in Mato Grosso, Brazil Using Time Series MODIS Enhanced Vegetation Index and Landsat Thematic Mapper Data , 2015, Remote. Sens..

[8]  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.

[9]  Ainong Li,et al.  An Improved Physics-Based Model for Topographic Correction of Landsat TM Images , 2015, Remote. Sens..

[10]  Jiyuan Liu,et al.  Tracking the dynamics of paddy rice planting area in 1986–2010 through time series Landsat images and phenology-based algorithms , 2015 .

[11]  M. Abuzar,et al.  Mapping Irrigated Farmlands Using Vegetation and Thermal Thresholds Derived from Landsat and ASTER Data in an Irrigation District of Australia , 2015 .

[12]  Zhuoqi Chen,et al.  Validation of China-wide interpolated daily climate variables from 1960 to 2011 , 2015, Theoretical and Applied Climatology.

[13]  Maggi Kelly,et al.  Twentieth-century shifts in forest structure in California: Denser forests, smaller trees, and increased dominance of oaks , 2015, Proceedings of the National Academy of Sciences.

[14]  Jesslyn F. Brown,et al.  Merging remote sensing data and national agricultural statistics to model change in irrigated agriculture , 2014 .

[15]  Jianping Huang,et al.  Multiyear precipitation reduction strongly decreases carbon uptake over northern China , 2014 .

[16]  M. Boschetti,et al.  Comparative Analysis of Normalised Difference Spectral Indices Derived from MODIS for Detecting Surface Water in Flooded Rice Cropping Systems , 2014, PloS one.

[17]  Wenquan Zhu,et al.  Mapping Irrigated Areas in China From Remote Sensing and Statistical Data , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[18]  Jude H. Kastens,et al.  Classifying multiyear agricultural land use data from Mato Grosso using time-series MODIS vegetation index data , 2013 .

[19]  C. Hunsaker,et al.  Response to “Comment on ‘Soil Moisture Response to Snowmelt and Rainfall in a Sierra Nevada Mixed‐Conifer Forest’” , 2012 .

[20]  Christopher Conrad,et al.  Per-field crop classification in irrigated agricultural regions in middle Asia using random forest and support vector machine ensemble , 2012, Remote Sensing.

[21]  Dylan Beaudette,et al.  Soil Moisture Response to Snowmelt and Rainfall in a Sierra Nevada Mixed‐Conifer Forest , 2011 .

[22]  Prasad S. Thenkabail,et al.  Mapping Irrigated Areas of Ghana Using Fusion of 30 m and 250 m Resolution Remote-Sensing Data , 2011, Remote. Sens..

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

[24]  P. S. Roy,et al.  Land Surface Water Index (LSWI) response to rainfall and NDVI using the MODIS Vegetation Index product , 2010 .

[25]  Pute Wu,et al.  Impact of climate change and irrigation technology advancement on agricultural water use in China , 2010 .

[26]  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 .

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

[28]  Yuanjie Li,et al.  Irrigated Area Maps and Statistics of India Using Remote Sensing and National Statistics , 2009, Remote. Sens..

[29]  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 .

[30]  V. Simonneaux,et al.  The use of high‐resolution image time series for crop classification and evapotranspiration estimate over an irrigated area in central Morocco , 2008 .

[31]  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 .

[32]  Christelle Vancutsem,et al.  GlobCover: ESA service for global land cover from MERIS , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[33]  P. Thenkabail,et al.  Irrigated area mapping in heterogeneous landscapes with MODIS time series, ground truth and census data, Krishna Basin, India , 2006 .

[34]  Jing Ma,et al.  Virtual versus real water transfers within China , 2006, Philosophical Transactions of the Royal Society B: Biological Sciences.

[35]  V. Wuwongse,et al.  Discrimination of irrigated and rainfed rice in a tropical agricultural system using SPOT VEGETATION NDVI and rainfall data , 2005 .

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

[37]  Prasad S. Thenkabail,et al.  Ganges and Indus river basin land use/land cover (LULC) and irrigated area mapping using continuous streams of MODIS data , 2005 .

[38]  Jin Chen,et al.  A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter , 2004 .

[39]  I. El-Magd,et al.  Improvements in land use mapping for irrigated agriculture from satellite sensor data using a multi-stage maximum likelihood classification , 2003 .

[40]  B. Fu,et al.  Soil moisture variation in relation to topography and land use in a hillslope catchment of the Loess Plateau, China , 2001 .

[41]  Jozsef Szilagyi,et al.  Can a vegetation index derived from remote sensing be indicative of areal transpiration , 2000 .

[42]  I. Shiklomanov.  Appraisal and Assessment of World Water Resources , 2000 .

[43]  Lifeng Luo,et al.  Detecting irrigation extent, frequency, and timing in a heterogeneous arid agricultural region using MODIS time series, Landsat imagery, and ancillary data , 2018 .

[44]  Shengjun Wu,et al.  NDVI indicated long-term interannual changes in vegetation activities and their responses to climatic and anthropogenic factors in the Three Gorges Reservoir Region, China. , 2017, The Science of the total environment.

[45]  P. Gong,et al.  Efficient corn and soybean mapping with temporal extendability: A multi-year experiment using Landsat imagery , 2014 .

[46]  Prasad S. Thenkabail,et al.  Irrigated areas of India derived using MODIS 500 m time series for the years 2001–2003 , 2010 .

[47]  M. Kercheva,et al.  CERES model application for increasing preparedness to climate variability in agricultural planning—risk analyses , 2005 .

[48]  Cecilia Martinez Beltran,et al.  Irrigated Crop Area Estimation Using Landsat TM Imagery in La Mancha, Spain , 2001 .

[49]  Petra Döll,et al.  A digital global map of irrigated areas. , 2000 .

[50]  Richard H. Waring,et al.  Forest Ecosystems: Analysis at Multiple Scales , 1985 .