Inspecting the Food-Water Nexus in the Ogallala Aquifer Region Using Satellite Remote Sensing Time Series

[1]  B. Scanlon,et al.  Global evaluation of new GRACE mascon products for hydrologic applications , 2016 .

[2]  Zhe Zhu,et al.  Overall Methodology Design for the United States National Land Cover Database 2016 Products , 2019, Remote. Sens..

[3]  Maosheng Zhao,et al.  Improvements of the MODIS terrestrial gross and net primary production global data set , 2005 .

[4]  Mekonnen Gebremichael,et al.  Remote Sensing-Based Assessment of the Crop, Energy and Water Nexus in the Central Valley, California , 2019, Remote. Sens..

[5]  B. Scanlon,et al.  Comparison of seasonal terrestrial water storage variations from GRACE with groundwater‐level measurements from the High Plains Aquifer (USA) , 2007 .

[6]  Fulu Tao,et al.  Remote sensing of crop production in China by production efficiency models: models comparisons, estimates and uncertainties , 2005 .

[7]  F. Landerer,et al.  Emerging trends in global freshwater availability , 2018, Nature.

[8]  J. Famiglietti,et al.  Satellite-based estimates of groundwater depletion in India , 2009, Nature.

[9]  Mekonnen Gebremichael,et al.  Improving the Applicability of Hydrologic Models for Food-Energy-Water Nexus Studies Using Remote Sensing Data , 2020, Remote. Sens..

[10]  J. Famiglietti The global groundwater crisis , 2014 .

[11]  Maosheng Zhao,et al.  A Continuous Satellite-Derived Measure of Global Terrestrial Primary Production , 2004 .

[12]  Huadong Guo,et al.  An assessment of global electric power consumption using the Defense Meteorological Satellite Program-Operational Linescan System nighttime light imagery , 2019 .

[13]  Jessica A. Gephart,et al.  The Global Food‐Energy‐Water Nexus , 2018, Reviews of Geophysics.

[14]  Pierre-Emmanuel Kirstetter,et al.  +50 Years of Terrestrial Hydroclimatic Variability in Africa’s Transboundary Waters , 2019, Scientific Reports.

[15]  W. Cleveland,et al.  Regression by local fitting: Methods, properties, and computational algorithms , 1988 .

[16]  Qihao Weng,et al.  World energy consumption pattern as revealed by DMSP-OLS nighttime light imagery , 2016 .

[17]  Tomasz Jasiński,et al.  Modeling electricity consumption using nighttime light images and artificial neural networks , 2019, Energy.

[18]  M. Sophocleous Groundwater recharge and sustainability in the High Plains aquifer in Kansas, USA , 2005 .

[19]  Khaled H. Hamed,et al.  A modified Mann-Kendall trend test for autocorrelated data , 1998 .

[20]  J. Pekel,et al.  High-resolution mapping of global surface water and its long-term changes , 2016, Nature.

[21]  Maosheng Zhao,et al.  Applying Improved Estimates of MODIS Productivity to Characterize Grassland Vegetation Dynamics , 2006 .

[22]  W. Cohen,et al.  Evaluation of MODIS NPP and GPP products across multiple biomes. , 2006 .

[23]  Zhengwei Yang,et al.  Monitoring US agriculture: the US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program , 2011 .

[24]  Jonathan D. Haskett,et al.  NET PRIMARY PRODUCTION OF U.S. MIDWEST CROPLANDS FROM AGRICULTURAL HARVEST YIELD DATA , 2001 .

[25]  R. Reedy,et al.  Groundwater depletion and sustainability of irrigation in the US High Plains and Central Valley , 2012, Proceedings of the National Academy of Sciences.

[26]  Yude Pan,et al.  BIOMASS AND NPP ESTIMATION FOR THE MID-ATLANTIC REGION (USA) USING PLOT-LEVEL FOREST INVENTORY DATA , 2001 .

[27]  Marios Sophocleous,et al.  Review: groundwater management practices, challenges, and innovations in the High Plains aquifer, USA—lessons and recommended actions , 2010 .

[28]  Yang Hong,et al.  Assessment of Physical Water Scarcity in Africa Using GRACE and TRMM Satellite Data , 2019, Remote. Sens..

[29]  Jonathan M. Adams,et al.  Global pattern of NPP to GPP ratio derived from MODIS data: effects of ecosystem type, geographical location and climate , 2009 .

[30]  Sami F. Masri,et al.  The energy-water agriculture nexus: the past, present and future of holistic resource management via remote sensing technologies , 2016 .

[31]  G. Jewitt,et al.  The Development of the Water-Energy-Food Nexus as a Framework for Achieving Resource Security: A Review , 2019, Front. Environ. Sci..

[32]  V. M. Tiwari,et al.  Dwindling groundwater resources in northern India, from satellite gravity observations , 2009 .

[33]  S. Swenson,et al.  Estimated accuracies of regional water storage variations inferred from the Gravity Recovery and Climate Experiment (GRACE) , 2003 .

[34]  Bin Xu,et al.  Remote Sensing Estimates of Grassland Aboveground Biomass Based on MODIS Net Primary Productivity (NPP): A Case Study in the Xilingol Grassland of Northern China , 2014, Remote. Sens..