Dynamic identification of summer cropping irrigated areas in a large basin experiencing extreme climatic variability

[1]  G. Roth,et al.  Water-use efficiency and productivity trends in Australian irrigated cotton: a review , 2013, Crop and Pasture Science.

[2]  M. Webb Seasonal climate summary southern hemisphere (summer 2011-12): a mature La Niña, strongly positive SAM and active MJO , 2013 .

[3]  Thomas Hilker,et al.  An Improved Image Fusion Approach Based on Enhanced Spatial and Temporal the Adaptive Reflectance Fusion Model , 2013, Remote. Sens..

[4]  Lei Gao,et al.  Simplified Monthly Hydrology and Irrigation Water Use Model to Explore Sustainable Water Management Options in the Murray-Darling Basin , 2013, Water Resources Management.

[5]  Tim R. McVicar,et al.  Assessing the accuracy of blending Landsat–MODIS surface reflectances in two landscapes with contrasting spatial and temporal dynamics: A framework for algorithm selection , 2013 .

[6]  Serge Rambal,et al.  Evaluation of the potential of MODIS satellite data to predict vegetation phenology in different biomes: An investigation using ground-based NDVI measurements , 2013 .

[7]  A. Huete,et al.  Evaluation of optical remote sensing to estimate actual evapotranspiration and canopy conductance , 2013 .

[8]  B. Timbal,et al.  The Millennium Drought in southeast Australia (2001–2009): Natural and human causes and implications for water resources, ecosystems, economy, and society , 2013 .

[9]  J. Vaze,et al.  Observed hydrologic non-stationarity in far south-eastern Australia: implications for modelling and prediction , 2013, Stochastic Environmental Research and Risk Assessment.

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

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

[12]  Bardan Ghimire,et al.  An Evaluation of Bagging, Boosting, and Random Forests for Land-Cover Classification in Cape Cod, Massachusetts, USA , 2012 .

[13]  J. Bruinsma,et al.  World agriculture towards 2030/2050: the 2012 revision , 2012 .

[14]  Songfeng Zheng,et al.  Applying tree-based ensemble algorithms to the classification of ecological zones using multi-temporal multi-source remote-sensing data , 2012 .

[15]  Ang Yang,et al.  A Daily River System Model for the Murray-Darling Basin: Development, Testing and Implementation , 2012 .

[16]  Edward P. Glenn,et al.  Actual evapotranspiration estimation by ground and remote sensing methods: the Australian experience , 2011 .

[17]  Marc F. P. Bierkens,et al.  Modelling global water stress of the recent past: on the relative importance of trends in water demand and climate variability , 2011 .

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

[19]  C. Ganter Seasonal climate summary southern hemisphere (winter 2010): a fast developing La Niña , 2011 .

[20]  I. V. Muralikrishna,et al.  Changes in agricultural cropland areas between a water-surplus year and a water-deficit year impacting food security, determined using MODIS 250 m time-series data and spectral matching techniques, in the Krishna River basin (India) , 2011 .

[21]  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..

[22]  B. Campbell Seasonal climate summary southern hemisphere (autumn 2010): rapid decay of El Niño, wetter than average in central, northern and eastern Australia and warmer than usual in the west and south , 2011 .

[23]  A. Dijk,et al.  A conceptual model to estimate ungauged losses in river water accounting , 2011 .

[24]  J. Pittock,et al.  Australia Demonstrates the Planet's Future: Water and Climate in the Murray–Darling Basin , 2010 .

[25]  Xiaolin Zhu,et al.  An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions , 2010 .

[26]  Md Shahriar Pervez,et al.  Mapping Irrigated Lands at 250-m Scale by Merging MODIS Data and National Agricultural Statistics , 2010, Remote. Sens..

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

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

[30]  Pierre Geurts,et al.  Supervised learning with decision tree-based methods in computational and systems biology. , 2009, Molecular bioSystems.

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

[32]  Ray Leuning,et al.  Scaling of potential evapotranspiration with MODIS data reproduces flux observations and catchment water balance observations across Australia , 2009 .

[33]  Lu Zhang,et al.  Interannual variability of catchment water balance in Australia , 2009 .

[34]  Tim R. McVicar,et al.  Climate‐related trends in Australian vegetation cover as inferred from satellite observations, 1981–2006 , 2009 .

[35]  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 .

[36]  Tim R. McVicar,et al.  Deriving consistent long-term vegetation information from AVHRR reflectance data using a cover-triangle-based framework , 2008 .

[37]  P. Thenkabail,et al.  Sub-pixel Area Calculation Methods for Estimating Irrigated Areas , 2007, Sensors.

[38]  B. Wardlow,et al.  Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains , 2007 .

[39]  Ranga B. Myneni,et al.  The impact of gridding artifacts on the local spatial properties of MODIS data : Implications for validation, compositing, and band-to-band registration across resolutions , 2006 .

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

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

[42]  Mathew R. Schwaller,et al.  On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[43]  A. Viña,et al.  Relationship between gross primary production and chlorophyll content in crops: Implications for the synoptic monitoring of vegetation productivity , 2006 .

[44]  J. Ioannidis Why Most Published Research Findings Are False , 2005 .

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

[46]  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 .

[47]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[48]  Murray C. Peel,et al.  Continental differences in the variability of annual runoff-update and reassessment , 2004 .

[49]  Tim R. McVicar,et al.  Current and potential uses of optical remote sensing in rice-based irrigation systems: a review , 2004 .

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

[51]  Tim R. McVicar,et al.  A simple method to improve field-level rice identification: toward operational monitoring with satellite remote sensing , 2003 .

[52]  C. Vörösmarty,et al.  Global water assessment and potential contributions from Earth Systems Science , 2002, Aquatic Sciences.

[53]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[54]  S. Tarantola,et al.  Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1 - Theoretical approach , 2002 .

[55]  John O. Carter,et al.  Using spatial interpolation to construct a comprehensive archive of Australian climate data , 2001, Environ. Model. Softw..

[56]  C. Woodcock,et al.  Classification and Change Detection Using Landsat TM Data: When and How to Correct Atmospheric Effects? , 2001 .

[57]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[58]  C. Brodley,et al.  Decision tree classification of land cover from remotely sensed data , 1997 .

[59]  Walid A. Abderrahman,et al.  Remote sensing application to the management of agricultural drainage water in severely arid region: A case study , 1992 .

[60]  Thomas A. McMahon,et al.  Global Runoff: Continental Comparisons of Annual Flows and Peak Discharges , 1992 .

[61]  R. Royall The Effect of Sample Size on the Meaning of Significance Tests , 1986 .

[62]  Jacob Cohen,et al.  Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit. , 1968 .