Abstracts
With the development of geospatial data science and its application in the agricultural field, the meaningful agricultural-related geospatial data and information are inextricably linked with sustainable farming practices, internationalization of agricultural commodities, and global climate change. For better utilizing and reusing the agricultural information and knowledge, robust data centers and systems are expected to play a significant role in the management of numerous agricultural-related geospatial data, which could change the agricultural geoinformation domain by developing geospatial algorithms and workflow, creating added value information products, downstream applications and services in favor of both public and private sector stakeholders. This chapter reviews the state-of-art of the operational agriculture monitoring systems at the international, national, and regional level that provides the continued monitoring of agriculture. The user needs and the issues of current agricultural data systems are discussed. The capabilities of spatial and temporal monitoring systems are analyzed. Emphatically, the data sources and products, system functionalities, interoperability, and standardization are demonstrated.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Boryan, C., Yang, Z., Mueller, R., & Craig, M. (2011). Monitoring US agriculture: The US department of agriculture, national agricultural statistics service, cropland data layer program. Geocarto International, 26, 341–358.
Brown, S., Gerlt, S., Wilcox, L. (2011). The Value of the 2011 Crop Production Loss from the Birds Point-New Madrid Floodway Levee Breach. FAPRI-MU Report 06-11, Food and Agricultural Policy Research Institute, University of Missouri, USA, (pp. 6).
Cutter, S. L., & Emrich, C. (2005). Are natural hazards and disaster losses in the US increasing? EOS. Transactions of the American Geophysical Union, 86, 381–389.
de La Beaujardiere, J. (2006). OpenGIS® web map server implementation specification. Open Geospatial Consort Inc OGC 06–042.
Deng, M., Di, L., Han, W., et al. (2011). The development of a web-service-based on-demand global agriculture drought information system. In AGU fall meeting abstracts (p. 08).
Deng, M., Di, L., Yu, G., et al. (2012). Building an on-demand web service system for global agricultural drought monitoring and forecasting. In Geoscience and remote sensing symposium (IGARSS), 2012 IEEE international (pp. 958–961). IEEE.
Deng, M., Di, L., Han, W., et al. (2013). Web-service-based monitoring and analysis of global agricultural drought. Photogrammetric Engineering and Remote Sensing, 79, 929–943.
Di, L. (2016). Big data and its applications in agro-geoinformatics. In 2016 IEEE international geoscience and remote sensing symposium (IGARSS) (pp. 189–191).
Di, L., Yu, E. G., Kang, L., et al. (2017). RF-CLASS: A remote-sensing-based flood crop loss assessment cyber-service system for supporting crop statistics and insurance decision-making. Journal of Integrative Agriculture, 16, 408–423. https://doi.org/10.1016/S2095-3119(16)61499-5.
EDO. (2017). EDO European drought observatory – JRC European Commission. http://edo.jrc.ec.europa.eu/edov2/php/index.php?id=1000. Accessed 12 June 2017.
FSA. (2012). USDA-farm service agency common land unit (CLU) information sheet. https://www.fsa.usda.gov/. Accessed 12 June 2017.
GADMFS. (2017). Global agricultural drought monitoring and forecasting system. http://gis.csiss.gmu.edu/GADMFS/. Accessed 12 June 2017.
GDM. (2017). Global drought information system. https://www.drought.gov/gdm/. Accessed 12 June 2017.
GEOSS (2017) Geo-strategic target for agriculture. http://www.earthobservations.org/geoss.php. Accessed 11 June 2017.
Han, W., Yang, Z., Di, L., & Mueller, R. (2012). CropScape: A web service based application for exploring and disseminating US conterminous geospatial cropland data products for decision support. Computers and Electronics in Agriculture, 84, 111–123.
LANCE. (2017). LANCE: NASA near real-time data and imagery | Earthdata. https://earthdata.nasa.gov/earth-observation-data/near-real-time. Accessed 11 June 2017.
Lu, H., & Campbell, D. E. (2009). Ecological and economic dynamics of the Shunde agricultural system under China’s small city development strategy. Journal of Environmental Management, 90, 2589–2600.
Mueller, M., & Pross, B. (2015) OGC WPS 2.0 Interface Standard. Open Geospatial Consortium Specification, (pp. 14–065).
Na, A., & Priest, M. (2007) OGC Implementation Specification 06-009r6: OpenGIS Sensor Observation Service (SOS). Open Geospatial Consortium Technical Report.
NASA MODIS. (2017). Moderate resolution imaging Spectroradiometer. https://modis.gsfc.nasa.gov/data/. Accessed 12 June 2017.
Nebert, D, Whiteside, A., & Vretanos, P. (2007). OpenGIS Catalogue Services Specification. OGC Implementation Specification.
Nigro, J., Slayback, D., Policelli, F., & Brakenridge, G. R. (2014). NASA/DFO MODIS near real-time (NRT) global flood mapping product evaluation of flood and permanent water detection. https://floodmap.modaps.eosdis.nasa.gov//documents/NASAGlobalNRTEvaluationSummary_v4.pdf. Accessed 12 June 2017.
NOAA AVHRR. (2017). NOAA Satellite Information System (NOAASIS). http://noaasis.noaa.gov/NOAASIS/ml/avhrr.html. Accessed 12 June 2017.
Olson, K. R., & Morton, L. W. (2012). The impacts of 2011 induced levee breaches on agricultural lands of Mississippi River Valley. Journal of Soil and Water Conservation, 67, 5A–10A.
OpenLayers. (2017). OpenLayers – A high-performance, feature-packed library for all your mapping needs. http://openlayers.org/. Accessed 2 June 2017.
Peng, C., Deng, M., Di, L., & Han, W. (2015). Delivery of agricultural drought information via web services. Earth Science Informatics, 8, 527–538.
Peterson, T. C., Heim, R. R., Jr., Hirsch, R., et al. (2013). Monitoring and understanding changes in heat waves, cold waves, floods, and droughts in the United States: State of knowledge. Bulletin of the American Meteorological Society, 94, 821–834.
RF-CLASS. (2017). RF-CLASS – Remote-sensing-based flood crop loss assessment service system. http://dss.csiss.gmu.edu/RFCLASS/. Accessed 12 June 2017.
Sencha. (2017). ExtJS 4 JavaScript framework for rich apps in every browser. In Sencha. https://www.sencha.com/products/extjs/. Accessed 2 June 2017.
Shrestha, R., Di, L., Eugene, G. Y., et al. (2017). Regression model to estimate flood impact on corn yield using MODIS NDVI and USDA cropland data layer. Journal of Integrative Agriculture, 16, 398–407.
Smith, A. B., & Katz, R. W. (2013). US billion-dollar weather and climate disasters: Data sources, trends, accuracy and biases. Natural Hazards, 67, 387–410.
USDM. (2017). United States drought monitor. http://droughtmonitor.unl.edu/. Accessed 12 June 2017.
VegScape. (2017). VegScape – Vegetation condition explorer. https://nassgeodata.gmu.edu/VegScape/. Accessed 12 June 2017.
Villarini, G., & Smith, J. A. (2010). Flood peak distributions for the eastern United States.
Vretanos, P. A. (2010). OpenGIS Web Feature Service 2.0 Interface Standard. Open Geospatial Consortium Specification, (pp. 04-094).
Western Governors Association. (2004). Creating a drought early warning system for the 21st century: The National integrated drought information system.
Whiteside, A., & Evans, J. D. (2008). Web Coverage Service (WCS) Implementation Standard.
Wikipedia. (2017). 2011 Mississippi River floods. Wikipedia.
Wilhite, D. A. (1997). Responding to drought: Common threads from the past, visions for the future1. Wiley Online Library.
Wilhite, D. A. (2016). Droughts: A global assesment. Routledge.
Yagci, A. L., Di, L., Deng, M., et al. (2012). Global agricultural drought mapping: Results for the year 2011. In Geoscience and remote sensing symposium (IGARSS), 2012 IEEE international (pp. 3764–3767). IEEE.
Yang, Z., Di, L., Yu, G., & Chen, Z. (2011). Vegetation condition indices for crop vegetation condition monitoring. In Geoscience and remote sensing symposium (IGARSS), 2011 IEEE international (pp. 3534–3537). IEEE.
Yang, Z., Yu, G., Di, L., et al. (2013). Web service-based vegetation condition monitoring system-vegscape. In Geoscience and remote sensing symposium (IGARSS), 2013 IEEE international (pp. 3638–3641). IEEE.
Yang, Z., Hu, L., Yu, G., et al. (2016). Web service-based SMAP soil moisture data visualization, dissemination and analytics based on vegscape framework. In Geoscience and remote sensing symposium (IGARSS), 2016 IEEE international (pp. 3624–3627). IEEE.
Yu, G., Han, W., & Deng, M. (2010). A remote sensing-based global agricultural drought monitoring and forecasting system for supporting GEOSS. In American Geophysical Union, Fall Meeting 2010 abstracts.
Yu, G., Di, L., Zhang, B., et al. (2013). Remote-sensing-based flood damage estimation using crop condition profiles. In Agro-Geoinformatics (Agro-Geoinformatics), 2013 second international conference on (pp. 205–210). IEEE.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Hu, L., Yue, P. (2021). Spatial and Temporal Monitoring System for Agriculture. In: Di, L., Üstündağ, B. (eds) Agro-geoinformatics. Springer Remote Sensing/Photogrammetry. Springer, Cham. https://doi.org/10.1007/978-3-030-66387-2_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-66387-2_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-66386-5
Online ISBN: 978-3-030-66387-2
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)