Agriculture
Link to materials for attendees: g.co/earth/g4gindia2018agriculture
Cloud based decision support for agricultural sector
Cloud based decision support for agricultural sector
In this workshop we will explore the ways that cloud-scale geospatial processing can inform decisions made in the agricultural sector. Examples analyses include, but are not limited to:
- Crop mapping (agriculture vs. non-agriculture, crop type, active vs. fallowed)
- Estimating water consumption (evapotranspiration)
- Estimating yield
- Informing planting decisions based on medium-range weather forecasts
Who should attend for the Agriculture track? Ideal participants will have the following characteristics:
- Works with satellite remote sensing data to inform agricultural decision making.
- Is looking to scale up analyses to larger areas or time series to have increased impact.
- Is interested in multi-organization collaborations to address agricultural issues.
- Is interested in open data, open science, and open source development practices.
Want to learn more about our work with Agriculture?
Recent publications involving Earth Engine::
- Jin, Z., Azzari, G., Burke, M., Aston, S., & Lobell, D. B. (2017). Mapping Smallholder Yield Heterogeneity at Multiple Scales in Eastern Africa. Remote Sensing, 9(9), 931. Link
- Cabot, P. E., Vashisht, A., & Chávez, J. L. (2017). Using Remote Sensing Assessments to Document Consumptive Use (CU) on Alfalfa and Grass Hayfields Managed Under Reduced and Full Irrigation Regimes. Link
- Eckert, S., Kiteme, B., Njuguna, E., & Zaehringer, J. G. (2017). Agricultural Expansion and Intensification in the Foothills of Mount Kenya: A Landscape Perspective. Remote Sensing, 9(8), 784. Link
- Jain, M., Singh, B., Srivastava, A., Malik, R. K., McDonald, A., & Lobell, D. B. (2017). Using satellite data to identify the causes of and potential solutions for yield gaps in India’s Wheat Belt. Environmental Research Letters. Link
- McGwire, K. C., Weltz, M. A., Snyder, K. A., Huntington, J. L., Morton, C. G., & McEvoy, D. J. (2017). Satellite Assessment of Early-Season Forecasts for Vegetation Conditions of Grazing Allotments in Nevada, United States. Rangeland Ecology & Management. Link
- Izquierdo-Verdiguier E, Zurita-Milla R, Rolf A. On the use of guided regularized random forests to identify crops in smallholder farm fields. InAnalysis of Multitemporal Remote Sensing Images (MultiTemp), 2017 9th International Workshop on the 2017 Jun 27 (pp. 1-3). IEEE. Link
- Parente, L., Ferreira, L., Faria, A., Nogueira, S., Araújo, F., Teixeira, L., & Hagen, S. (2017). Monitoring the brazilian pasturelands: A new mapping approach based on the landsat 8 spectral and temporal domains. International Journal of Applied Earth Observation and Geoinformation, 62, 135-143. Link
- Chance, E. W., Cobourn, K. M., Thomas, V. A., Dawson, B. C., & Flores, A. N. (2017). Identifying Irrigated Areas in the Snake River Plain, Idaho: Evaluating Performance across Composting Algorithms, Spectral Indices, and Sensors. Remote Sensing, 9(6), 546. Link