No pixel left behind: Toward integrating Earth Observations for agriculture into the United Nations Sustainable Development Goals framework

[1]  Bingfang Wu,et al.  The GEOGLAM crop monitor for AMIS: Assessing crop conditions in the context of global markets , 2019 .

[2]  Amy McNally,et al.  Recognizing the Famine Early Warning Systems Network: Over 30 Years of Drought Early Warning Science Advances and Partnerships Promoting Global Food Security , 2019, Bulletin of the American Meteorological Society.

[3]  C. Rosenzweig,et al.  Hydrologic and Agricultural Earth Observations and Modeling for the Water-Food Nexus , 2019, Front. Environ. Sci..

[4]  Luca Brocca,et al.  Estimating irrigation water use over the contiguous United States by combining satellite and reanalysis soil moisture data , 2019, Hydrology and Earth System Sciences.

[5]  Martha C. Anderson,et al.  Earth Observations and Integrative Models in Support of Food and Water Security , 2019, Remote Sensing in Earth Systems Sciences.

[6]  Gérard Dedieu,et al.  Near real-time agriculture monitoring at national scale at parcel resolution: Performance assessment of the Sen2-Agri automated system in various cropping systems around the world , 2019, Remote Sensing of Environment.

[7]  Ian McCallum,et al.  A comparison of global agricultural monitoring systems and current gaps , 2019, Agricultural Systems.

[8]  Michele Meroni,et al.  ASAP: A new global early warning system to detect anomaly hot spots of agricultural production for food security analysis , 2019, Agricultural systems.

[9]  M. Hansen,et al.  Near doubling of Brazil’s intensive row crop area since 2000 , 2018, Proceedings of the National Academy of Sciences.

[10]  Luca Brocca,et al.  How much water is used for irrigation? A new approach exploiting coarse resolution satellite soil moisture products , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[11]  C. Justice,et al.  The Harmonized Landsat and Sentinel-2 surface reflectance data set , 2018, Remote Sensing of Environment.

[12]  W. G. M. Bastiaanssen,et al.  From Global Goals to Local Gains - A Framework for Crop Water Productivity , 2018, ISPRS Int. J. Geo Inf..

[13]  Serhiy Skakun,et al.  Prior Season Crop Type Masks for Winter Wheat Yield Forecasting: A US Case Study , 2018, Remote. Sens..

[14]  David P. Roy,et al.  Observations and Recommendations for the Calibration of Landsat 8 OLI and Sentinel 2 MSI for Improved Data Interoperability , 2018, Remote. Sens..

[15]  David P. Roy,et al.  Analysis Ready Data: Enabling Analysis of the Landsat Archive , 2018, Remote. Sens..

[16]  Heather McNairn,et al.  SAR Speckle Filtering and Agriculture Field Size: Development of SAR Data Processing Best Practices for the JECAM SAR Inter-Comparison Experiment , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[17]  Ferran Gascon,et al.  Sentinel-2 for Agricultural Monitoring , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[18]  Alex Held,et al.  Analysis-Ready Earth Observation Data and the United Nations Sustainable Development Goals , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[19]  Takeo Tadono,et al.  CEOS Analysis Ready Data for Land (CARD4L) Overview , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[20]  Christopher Justice,et al.  Geoglam: A Geo Initiative on Global Agricultural Monitoring , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[21]  M. Hill,et al.  Calibration and validation of the Australian fractional cover product for MODIS collection 6 , 2018 .

[22]  D. Lobell,et al.  Landsat-based classification in the cloud: An opportunity for a paradigm shift in land cover monitoring , 2017 .

[23]  Denisa Rodila,et al.  Building an Earth Observations Data Cube: lessons learned from the Swiss Data Cube (SDC) on generating Analysis Ready Data (ARD) , 2017 .

[24]  Christopher S. Lynnes,et al.  Archive Management of NASA Earth Observation Data to Support Cloud Analysis , 2017 .

[25]  D. Rybski,et al.  A Systematic Study of Sustainable Development Goal (SDG) Interactions , 2017 .

[26]  Christopher Conrad,et al.  Assessing gaps in irrigated agricultural productivity through satellite earth observations - A case study of the Fergana Valley, Central Asia , 2017, Int. J. Appl. Earth Obs. Geoinformation.

[27]  Amine Merzouki,et al.  Sentinel-1 high resolution soil moisture , 2017, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[28]  John P. Fulton,et al.  An overview of current and potential applications of thermal remote sensing in precision agriculture , 2017, Comput. Electron. Agric..

[29]  David M. Johnson,et al.  A multi-resolution approach to national-scale cultivated area estimation of soybean , 2017 .

[30]  Odirilwe Selomane,et al.  Essential variables help to focus sustainable development goals monitoring , 2017 .

[31]  Ben Evans,et al.  The Australian Geoscience Data Cube - foundations and lessons learned , 2017 .

[32]  Abbas Rajabifard,et al.  Sustainable development and geospatial information: a strategic framework for integrating a global policy agenda into national geospatial capabilities , 2017, Geo spatial Inf. Sci..

[33]  Katherine Anderson,et al.  Earth observation in service of the 2030 Agenda for Sustainable Development , 2017, Geo spatial Inf. Sci..

[34]  B. Ryan.  Open access to data crucial for a sustainable future , 2017 .

[35]  Matthew C. Hansen,et al.  National-scale soybean mapping and area estimation in the United States using medium resolution satellite imagery and field survey , 2017 .

[36]  Sujay V. Kumar,et al.  A land data assimilation system for sub-Saharan Africa food and water security applications , 2017, Scientific Data.

[37]  Nancy D. Searby,et al.  GEOCAB Portal: A gateway for discovering and accessing capacity building resources in Earth Observation , 2017, Int. J. Appl. Earth Obs. Geoinformation.

[38]  Nobuhiro Tomiyama,et al.  Mapping rice-planted areas using time-series synthetic aperture radar data for the Asia-RiCE activity , 2016, Paddy and Water Environment.

[39]  N. Pettorelli,et al.  Framing the concept of satellite remote sensing essential biodiversity variables: challenges and future directions , 2016 .

[40]  Stephen D. Prince,et al.  Degradation of net primary production in a semiarid rangeland , 2016 .

[41]  Nancy D. Searby,et al.  A Global Capacity Building Vision for Societal Applications of Earth Observing Systems and Data: Key Questions and Recommendations , 2016 .

[42]  L. García,et al.  Earth observation for water resources management : current use and future opportunities for the water sector , 2016 .

[43]  Arief Wijaya,et al.  An integrated pan‐tropical biomass map using multiple reference datasets , 2016, Global change biology.

[44]  L. Lymburner,et al.  Water observations from space: Mapping surface water from 25 years of Landsat imagery across Australia , 2016 .

[45]  Adam Lewis,et al.  Rapid, high-resolution detection of environmental change over continental scales from satellite data – the Earth Observation Data Cube , 2016, Int. J. Digit. Earth.

[46]  A. Held,et al.  The GEOGLAM Rangelands and Pasture Productivity Activity: Recent Progress and Future Directions , 2015 .

[47]  D. Roy,et al.  Conterminous United States crop field size quantification from multi-temporal Landsat data , 2015 .

[48]  Gérard Dedieu,et al.  Building a Data Set over 12 Globally Distributed Sites to Support the Development of Agriculture Monitoring Applications with Sentinel-2 , 2015, Remote. Sens..

[49]  Nicholas C. Coops,et al.  Virtual constellations for global terrestrial monitoring , 2015 .

[50]  J. Michaelsen,et al.  A global satellite-assisted precipitation climatology , 2015 .

[51]  Gérard Dedieu,et al.  “Sentinel-2 for agriculture”: Supporting global agriculture monitoring , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[52]  Louis Kouadio,et al.  Evaluation of the integrated Canadian crop yield forecaster (ICCYF) model for in-season prediction of crop yield across the Canadian agricultural landscape , 2015 .

[53]  Stefano Nativi,et al.  Big Data challenges in building the Global Earth Observation System of Systems , 2015, Environ. Model. Softw..

[54]  Jianxi Huang,et al.  Improving the timeliness of winter wheat production forecast in the United States of America, Ukraine and China using MODIS data and NCAR Growing Degree Day information , 2015 .

[55]  A. S. Belward,et al.  Who launched what, when and why; trends in global land-cover observation capacity from civilian earth observation satellites , 2015 .

[56]  Yan Li,et al.  Evaluation of irrigation water use efficiency using remote sensing in the middle reach of the Heihe river, in the semi‐arid Northwestern China , 2015 .

[57]  David P. Roy,et al.  A contemporary decennial examination of changing agricultural field sizes using Landsat time series data , 2015, Geo : geography and environment.

[58]  Christopher Justice,et al.  Agricultural growing season calendars derived from MODIS surface reflectance , 2015, Int. J. Digit. Earth.

[59]  K. Arsenault,et al.  Calculating Crop Water Requirement Satisfaction in the West Africa Sahel with Remotely Sensed Soil Moisture , 2015 .

[60]  Christopher O. Justice,et al.  A Framework for Defining Spatially Explicit Earth Observation Requirements for a Global Agricultural Monitoring Initiative (GEOGLAM) , 2015, Remote. Sens..

[61]  Christopher O. Justice,et al.  Meeting Earth Observation Requirements for Global Agricultural Monitoring: An Evaluation of the Revisit Capabilities of Current and Planned Moderate Resolution Optical Earth Observing Missions , 2015, Remote. Sens..

[62]  C. Funk,et al.  A probabilistic approach to assess agricultural drought risk to maize in Southern Africa and millet in Western Sahel using satellite estimated rainfall , 2014 .

[63]  A. Simmons,et al.  The Concept of Essential Climate Variables in Support of Climate Research, Applications, and Policy , 2014 .

[64]  Amy McNally,et al.  A seasonal agricultural drought forecast system for food-insecure regions of East Africa , 2014 .

[65]  M. Reeves,et al.  A remote sensing protocol for identifying rangelands with degraded productive capacity , 2014 .

[66]  J. Braun,et al.  Drivers and triggers of international food price spikes and volatility , 2014 .

[67]  J. Staatz,et al.  Challenges in measuring the impact of Market Information Systems , 2014 .

[68]  David M. Johnson.  An assessment of pre- and within-season remotely sensed variables for forecasting corn and soybean yields in the United States , 2014 .

[69]  Jonathan. Brooks.  Policy coherence and food security: The effects of OECD countries' agricultural policies , 2014 .

[70]  C. Justice,et al.  High-Resolution Global Maps of 21st-Century Forest Cover Change , 2013, Science.

[71]  Toshio Okumura,et al.  Asia rice crop monitoring in GEO GLAM , 2013, 2013 Second International Conference on Agro-Geoinformatics (Agro-Geoinformatics).

[72]  Weicheng Wu,et al.  Using remote sensing to assess impacts of land management policies in the Ordos rangelands in China , 2013 .

[73]  J. Rockström,et al.  Policy: Sustainable development goals for people and planet , 2013, Nature.

[74]  David B. Lobell,et al.  The use of satellite data for crop yield gap analysis , 2013 .

[75]  Clement Atzberger,et al.  Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs , 2013, Remote. Sens..

[76]  N. Pettorelli,et al.  Essential Biodiversity Variables , 2013, Science.

[77]  Thomas R. Loveland,et al.  A review of large area monitoring of land cover change using Landsat data , 2012 .

[78]  Martha C. Anderson,et al.  Use of Landsat thermal imagery in monitoring evapotranspiration and managing water resources , 2012 .

[79]  S. Carpenter,et al.  Solutions for a cultivated planet , 2011, Nature.

[80]  S. Goetz,et al.  Advances in remote sensing technology and implications for measuring and monitoring forest carbon stocks and change , 2011 .

[81]  S. K. Balasundram,et al.  A Review: The Role of Remote Sensing in Precision Agriculture , 2010 .

[82]  J. Gallego,et al.  Accuracy, Objectivity and Efficiency of Remote Sensing for Agricultural Statistics , 2010 .

[83]  Christopher B. Barrett,et al.  Measuring Food Insecurity , 2010, Science.

[84]  C. Justice,et al.  The spatial and temporal distribution of crop residue burning in the contiguous United States. , 2009, The Science of the total environment.

[85]  Mohammad Alauddin,et al.  Agricultural Intensification, Irrigation and the Environment in South Asia: Issues and Policy Options , 2008 .

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

[87]  James E. McMurtrey,et al.  Remote sensing the spatial distribution of crop residues , 2005 .

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

[89]  A. Bebbington.  NGOs and uneven development: geographies of development intervention , 2004 .

[90]  M. S. Moran,et al.  Remote Sensing for Crop Management , 2003 .

[91]  J. I. Ortiz-Monasterio,et al.  Soil, climate, and management impacts on regional wheat productivity in Mexico from remote sensing , 2002 .

[92]  T. Howell.  Enhancing Water Use Efficiency in Irrigated Agriculture , 2001 .

[93]  J. Paruelo,et al.  Evapotranspiration estimates using NOAA AVHRR imagery in the Pampa region of Argentina , 2000 .

[94]  S. K. McFeeters.  The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features , 1996 .

[95]  Gérard Dedieu,et al.  Methodology for the estimation of terrestrial net primary production from remotely sensed data , 1994 .

[96]  C. Justice,et al.  Global land cover classification by remote sensing: present capabilities and future possibilities , 1991 .

[97]  Gautam D. Badhwar,et al.  Field size, length, and width distributions based on LACIE ground truth data. [large area crop inventory experiment] , 1980 .

[98]  Un Desa.  Transforming our world : The 2030 Agenda for Sustainable Development , 2016 .

[99]  Svatava Janoušková,et al.  Sustainable Development Goals: A need for relevant indicators , 2016 .

[100]  M. Kalkuhl,et al.  Can the Agricultural Market Information System (AMIS) help to reduce food price volatility , 2014 .

[101]  Un Millennium,et al.  Investing in development : a practical plan to achieve the Millennium Development Goals , 2005 .

[102]  S. Mantel,et al.  The role of GIS and remote sensing in land degradation assessment and conservation mapping: some user experiences and expectations , 2001 .

[103]  Erik Lichtenberg,et al.  Agriculture and the environment. , 2000 .

[104]  F. Luna.  La nación Argentina , 1996 .

[105]  J. Randerson,et al.  Global net primary production: Combining ecology and remote sensing , 1995 .

[106]  M. Siccardi.  Telecommunications--A Burgeoning Industry for Hispanic Entrepreneurs. , 1980 .

[107]  R. B. Erb,et al.  The Use of LANDSAT Data in a Large Area Crop Inventory Experiment (LACIE) , 1975 .