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Harvest-Funded and Recent Partner Research

(*Denotes Harvest-Funded)

  • Allen, S., de Brauw, A. (2018). Nutrition sensitive value chains: theory, progress, and open questions. Glob. Food Secur. 16, 22–28. doi: 10.1016/j.gfs.2017.07.
  • *Becker-Reshef, I., Barker, B., Humber, M., Puricelli, E., Sanchez, A., Sahajpal, R., McGaughey, K., Justice, C., Baruth, B., Wu, B., Prakash, A., Abdolreza, A., Jarvis, I. 2019. The GEOGLAM crop monitor for AMIS: Assessing crop conditions in the context of global markets. Global Food Security. 23, 173-181. https://doi.org/10.1016/j.gfs.2019.04.010
  • Cai, Y., Guan, K., Lobell, D., Potgieter, A., Wang, S., Peng, J., ... Peng, B. (2019). Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches. Elsevier Agricultural and Forest Meteorology, 274, 144-159. https://doi.org/10.1016/j.agrformet.2019.03.010 
  • *Franch, B., Vermote, E. F., Skakun, S., Roger, J. C., Becker-Reshef, I., Murphy, E., & Justice, C. 2019. Remote sensing based yield monitoring: Application to winter wheat in United States and Ukraine. International Journal of Applied Earth Observation and Geoinformation, 76, 112-127. https://doi.org/10.1016/j.jag.2018.11.012
  • *Fritz, S., See, L., Laso Bayas, J.C., Waldner, F., Jacques, D., Becker-Reshef, I., Whitcraft, A., Baruth, B., Bonifacio, R., Crutchfield, J., Rembold, F., Rojas, O., Schucknecht, A., Van der Velde, M., Verdin, J., Wu, B., Yan, N., You, L., Gilliams, S.,  Mücher, S., Tetrault, R., Moorthy, I., McCallum, I. 2019. A comparison of global agricultural monitoring systems and current gaps. Agricultural Systems, 168, 258-272. https://doi.org/10.1016/j.agsy.2018.05.010
  • Funk, C., Shukla, S., Thiaw, W.M., Rowland, J., Hoell, A., McNally, A., … Verdin, J. (2019). Recognizing the famine early warning systems network: over 30 years of drought early warning science advances and partnerships promoting global food security. American Meteorological Society. doi: 10.1175/BAMS-D-17-0233.1
  • Guillevic, P.C., Olioso, A., Hook, S.J., Fisher, J.B., Lagouarde, J.P. and Vermote, E.F. 2019. Impact of the Revisit of Thermal Infrared Remote Sensing Observations on Evapotranspiration Uncertainty—A Sensitivity Study Using AmeriFlux Data. Remote Sensing, 11(5), art.num.573.
  • Huang, X., Liao, C., Xing, M., Ziniti, B., Wang, J., Shang, J., … Torbick, N. (2019, October 30). A multi-temporal binary-tree classification using polarimetric RADARSAT-2 imagery. Remote Sesning of Environment, 235. https://doi.org/10.1016/j.rse.2019.111478
  • Huang, X., Ziniti, B., and Torbick, N. 2019. "Assessing Conflict Driven Food Security in Rakhine, Myanmar with Multisource Imagery," Land, MDPI, Open Access Journal, 8(6), 1-11.
  • Kumar, S. V., Dirmeyer, P. A., Peters-Lidard, C. D., Bindlish, R., & Bolten, J. (2018). Information theoretic evaluation of satellite soil moisture retrievals. Remote Sensing of Environment, 204, 392-400. https://doi.org/10.1016/j.rse.2017.10.016
  • *McNally, A.; Verdin, K.; Harrison, L.; Getirana, A.; Jacob, J.; Shukla, S.; Arsenault, K.; Peters-Lidard, C.; Verdin, J.P. 2019. Acute Water-Scarcity Monitoring for Africa. Water 11, 1968. https://www.mdpi.com/2073-4441/11/10/1968
  • *McNally, A., McCartney, S., Ruane, A., Mladenova, I., Whitcraft, A., Becker-Reshef, I., Bolten, J.D., Peters-Lidard, C., Rosenzweig, C. and Schollaert Uz, S. 2019. Hydrologic and Agricultural Earth Observations and Modeling for the Water-Food Nexus. Front. Environ. Sci. https://doi.org/10.3389/fenvs.2019.00023
  • Nakalembe, C. 2018.. Characterizing agricultural drought in the Karamoja subregion of Uganda with meteorological and satellite-based indices. Natural Hazards, 91(3), 837-862. https://doi.org/10.1007/s11069-017-3106-x
  • *Skakun, S.; Vermote, E.; Franch, B.; Roger, J.-C.; Kussul, N.; Ju, J.; Masek, J. 2019. Winter Wheat Yield Assessment from Landsat 8 and Sentinel-2 Data: Incorporating Surface Reflectance, Through Phenological Fitting, into Regression Yield Models. Remote Sens. 11, 1768. https://doi.org/10.3390/rs11151768
  • Skakun, S., Vermote, E.F., Roger, J.C., Justice, C.O. and Masek, J.G. 2019. Validation of the LaSRC cloud detection algorithm for Landsat 8 images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12(7), 2439 - 2446. 10.1109/JSTARS.2019.2894553
  • Skakun S., Justice C., Vermote E., & Roger J.-C. (2018). Transitioning from MODIS to VIIRS: an analysis of inter-consistency of NDVI data sets for agricultural monitoring. International Journal of Remote Sensing (2016 IF:1.724; 5-year IF: 1.986), vol. 39, no. 4, pp. 971-992, doi: 10.1080/01431161.2017.1395970.
  • *Torbick, N., Huang, X., Ziniti, B., Johnson, D., Masek, J. and Reba, M. 2018. Fusion of Moderate Resolution Earth Observations for Operational Crop Type Mapping. Remote Sens. 10(7), 1058; https://doi.org/10.3390/rs10071058
  • *Whitcraft, Alyssa K., et al. “No Pixel Left behind: Toward Integrating Earth Observations for Agriculture into the United Nations Sustainable Development Goals Framework.” Remote Sensing of Environment, Elsevier, 31 Oct. 2019. https://doi.org/10.1016/j.rse.2019.111470

 

Partner Research Pre-Dating Harvest

(Prior to 2018)

  • Ahamed, A., & Bolten, J. D. (2017). A MODIS-based automated flood monitoring system for southeast asia. International Journal of Applied Earth Observation and Geoinformation, 61, 104-117. https://doi.org/10.1016/j.jag.2017.05.006
  • Allen, S. & Ulimwengu, J. (2015) Agricultural Productivity, Health and Public Expenditures in Sub-Saharan Africa. Eur J Dev Res 27: 425. https://doi.org/10.1057/ejdr.2015.38
  • Anderson, W, L. You, S. Wood, U. Wood-Sichra, W. Wu. 2015. An analysis of methodological and spatial differences in global cropping systems models and maps. Global Ecology and Biogeography Volume 24, Issue 2, Page 180-191
  • Bandaru, V., Daughtry, C. S., Codling, E. E., Hansen, D. J., White-Hansen, S., & Green, C. E. (2016). Evaluating leaf and canopy reflectance of stressed rice plants to monitor arsenic contamination. International journal of environmental research and public health, 13(6), 606. doi:10.3390/ijerph13060606
  • Bandaru, V., Pei, Y., Hart, Q., & Jenkins, B. M. (2017). Impact of biases in gridded weather datasets on biomass estimates of short rotation woody cropping systems. Agricultural and forest meteorology, 233, 71-79. https://doi.org/10.1016/j.agrformet.2016.11.008
  • Baraldi, A., & Humber, M. L. (2015). Quality Assessment of Preclassification Maps Generated From Spaceborne/Airborne Multispectral Images by the Satellite Image Automatic Mapper and Atmospheric/Topographic Correction-Spectral Classification Software Products: Part 1—Theory. Part 2—experimental results. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(3), 1307-1329.
  • Baraldi, A., Boschetti, L., & Humber, M. L. (2014). Probability Sampling Protocol for Thematic and Spatial Quality Assessment of Classification Maps Generated From Spaceborne/Airborne Very High Resolution Images. IEEE Transactions on Geoscience and Remote Sensing, 52(1), 701-760.
  • Becker-Reshef, I., Justice, C., Doorn, B., Reynolds, C., Anyamba, A., Tucker, C. J., & Korontzi, S. (2009). NASA’s contribution to the Group on Earth Observations (GEO) Global Agricultural Monitoring System of Systems. NASA Earth Observer, 21, 24-29.
  • Becker-Reshef I., Justice C., Sullivan M., Tucker CJ., Anyamba A.,  Small J.,  Pak E., Hansen M.,  Pittman K., Schmaltz J.,  Masouka E., Williams D., Reynolds C., and Doorn B. 2010. Monitoring Global Croplands with Coarse Resolution Earth Observations: The Global Agriculture Monitoring (GLAM) Project.  Remote Sensing, 2(6), 1589-1609. doi:10.3390/rs2061589
  • Becker-Reshef, I., Vermote, E., Lindeman, M., & Justice, C. (2010). A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data. Remote Sensing of Environment, 114(6), 1312-1323. https://doi.org/10.1016/j.rse.2010.01.010
  • Brown, M. E., Carr, E. R., Grace, K. L., Wiebe, K., Funk, C. C., Attavanich, W., ... & Buja, L. (2017). Do markets and trade help or hurt the global food system adapt to climate change? Food Policy, 68, 154-159. ISSN 0306-9192, doi:10.1016/j.foodpol.2017.02.004
  • Brown, M. E., Tondel, F., Essam, T., Thorne, J. A., Mann, B. F., Leonard, K., & Eilerts, G. (2012). Country and regional staple food price indices for improved identification of food insecurity. Global Environmental Change, 22(3), 784-794. https://doi.org/10.1016/j.gloenvcha.2012.03.005
  • Chambers, E., Erickson, A., Fekete, S. P., Lenchner, J., Sember, J., Srinivasan, V., ... & Whitesides, S. (2017). Connectivity Graphs of Uncertainty Regions. Algorithmica, 78(3), 990-1019.
  • Chang, A., Jung, J., Maeda, M. M., & Landivar, J. (2017). Crop height monitoring with digital imagery from Unmanned Aerial System (UAS). Computers and Electronics in Agriculture, 141, 232-237. https://doi.org/10.1016/j.compag.2017.07.008.
  • Davies, D. K., Brown, M. E., Murphy, K. J., Michael, K. A., Zavodsky, B. T., Stavros, E. N., & Caroll, M. L. (2017). Workshop on Using NASA Data for Time-Sensitive Applications [Space Agencies]. IEEE Geoscience and Remote Sensing Magazine, 5(3), 52-58. doi: 10.1109/MGRS.2017.2729278
  • Dempewolf, J., Adusei, B., Becker-Reshef, I., Hansen, M., Potapov, P., Khan, A., & Barker, B. (2014). Wheat yield forecasting for Punjab Province from vegetation index time series and historic crop statistics. Remote Sensing, 6(10), 9653-9675. doi:10.3390/rs6109653
  • Devare, M., Zandstra, M., Clobridge, A., Fotsy, M., Abreu, D., Arnaud, E., Baraka, P., Bonaiuti, E., Chukka, S., Dieng, I., Dreher, K., Erlita, S., Juarez, H., Kim, S., Koo, J., Muchlish, U., Müller, M., Mwanzia, L., Poole, J and Siddiqui, S. (2017) Open Access and Open Data at CGIAR: Challenges and Solutions. Knowledge Management for Development Journal, 13 (2). pp. 6-21. ISSN 1947-4199
  • Enenkel, M., L. See, R. Bonifacio, V. Boken, N. Chaney, P. Vinck, L.You, E. Dutra, M. Anderson. 2015. Drought and food security - Improving decision-support via new technologies and innovative collaboration. Global Food Security Volume 4, page 51-54
  • Farmaha, B. S., Lobell, D. B., Boone, K. E., Cassman, K. G., Yang, H. S., & Grassini, P. (2016). Contribution of persistent factors to yield gaps in high-yield irrigated maize. Field crops research, 186, 124-132. https://doi.org/10.1016/j.fcr.2015.10.020
  • Fayne, J. V., Bolten, J. D., Doyle, C. S., Fuhrmann, S., Rice, M. T., Houser, P. R., & Lakshmi, V. (2017). Flood mapping in the lower Mekong River Basin using daily MODIS observations. International journal of remote sensing, 38(6), 1737-1757. https://doi.org/10.1080/01431161.2017.1285503
  • Flanagan, S. A., Hurtt, G. C., Fisk, J. P., Sahajpal, R., Hansen, M. C., Dolan, K. A., ... & Zhao, M. (2016). Potential vegetation and carbon redistribution in Northern North America from climate change. Climate, 4(1), 2. doi:10.3390/cli4010002
  • Franch, B., Vermote, E. F., Becker-Reshef, I., Claverie, M., Huang, J., Zhang, J., ... & Sobrino, J. A. (2015). 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. Remote Sensing of Environment, 161, 131-148. https://doi.org/10.1016/j.rse.2015.02.014 
  • Franch, B., Vermote, E., Roger, J.C., Murphy, E., Becker-Reshef, I., Justice, C., Claverie, M., Nagol, J., Csiszar, I., Meyer, D., Baret, F., Masuoka, E., Wolfe, R. and Devadiga, S. (2017). A 30+ year AVHRR Land Surface Reflectance Climate Data Record and its application to wheat yield monitoring, Remote Sensing, 9, 296
  • Fritz S, Fonte CC, & See L (2017). The Role of Citizen Science in Earth Observation. Remote Sensing 9 (4): p. 357. doi:10.3390/rs9040357.
  • Fritz S, See L, Perger C, McCallum I, Schill C, Schepaschenko D, Duerauer M, Karner M, et al. (2017). A global dataset of crowdsourced land cover and land use reference data. Scientific Data 4: p.170075. doi:10.1038/sdata.2017.75
  • Fritz S., Schepaschenko D., and See L. (2016). Carbon tracking: Limit uncertainties in land emissions. Nature, 534 (7609). p. 621 doi:10.1038/534621e
  • Fritz, S., L. See, I. Mccallum, L. You, et al. (2015). Mapping global cropland and field size, Global Change Biology 21, 1980-1992, doi: 10.1111/gcb.12838
  • Fritz, S., See, L., You, L., Justice, C., BeckerReshef, I., Bydekerke, L., ... & Gilliams, S. (2013). The need for improved maps of global cropland. Eos, Transactions American Geophysical Union, 94(3), 31-32. https://doi.org/10.1002/2013EO030006
  • Funk, C., Nicholson, S. E., Landsfeld, M., Klotter, D., Peterson, P., & Harrison, L. (2015). The centennial trends greater horn of Africa precipitation dataset. Scientific data, 2, 150050. ​doi:10.1038/sdata.2015.50.
  • Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., ... & Michaelsen, J. (2015). The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Scientific data, 2, 150066. doi: 10.1038/sdata.2015.66.
  • Funk, C., Verdin, A., Michaelsen, J., Peterson, P., Pedreros, D., & Husak, G. (2015). A global satellite-assisted precipitation climatology. Earth System Science Data, 7(2), 275. doi: 10.5194/essdd-8-401-2015.
  • Goodyear, L., Barela, E., Jewiss, J., & Usinger, J. (Eds.). (2014). Qualitative inquiry in evaluation: From theory to practice (Vol. 29). John Wiley & Sons.
  • Guo, S., Lenchner, J., Connell, J., Dholakia, M., & Muta, H. (2017, March). Conversational bootstrapping and other tricks of a concierge robot. In Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction (pp. 73-81). ACM.
  • Gustafson, D., Gutman, A., Leet, W., Drewnowski, A., Fanzo, J., & Ingram, J. (2016). Seven food system metrics of sustainable nutrition security. Sustainability, 8(3), 196. doi:10.3390/su8030196
  • Hatfield, J.L., K.J. Boote, B.A. Kimball, L.H. Ziska, R.C. Izaurralde, D. Ort, A. Thomson, D.W. Wolfe. 2011. Climate impacts on agriculture: Implications for crop production. Agron. J. 103:351-370.
  • Izaurralde, R.C., W.B. McGill, J.R. Williams, C.D. Jones, R.P. Link, D.H. Manowitz, D.E. Schwab, X. Zhang, G.P. Robertson, and N. Millar. 2017. Simulating microbial denitrification with EPIC: Model description and evaluation. Ecol. Modell. 359:349-362. doi: 10.1016/j.ecolmodel.2017.06.007. 
  • Jain, M., Srivastava, A. K., Joon, R. K., McDonald, A., Royal, K., Lisaius, M. C., & Lobell, D. B. (2016). Mapping Smallholder Wheat Yields and Sowing Dates Using Micro-Satellite Data. Remote Sensing, 8(10), 860. doi:10.3390/rs8100860
  • Janetos, A., Justice, C., Jahn, M., Obersteiner, M., Glauber, J., Mulhern, W. 2017 “The Risks of Multiple Breadbasket Failures in the 21st Century: A Science Research Agenda.” March. Pardee Center Research Report, The Frederick S. Pardee Center for the Study of the Longer-Range Future, Boston University.
  • Jantz, S. M., Barker, B., Brooks, T. M., Chini, L. P., Huang, Q., Moore, R. M., ... & Hurtt, G. C. (2015). Future habitat loss and extinctions driven by landuse change in biodiversity hotspots under four scenarios of climatechange mitigation. Conservation Biology, 29(4), 1122-1131. https://doi.org/10.1111/cobi.12549
  • Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell, D. B., & Ermon, S. (2016). Combining satellite imagery and machine learning to predict poverty. Science, 353(6301), 790-794. doi: 10.1126/science.aaf7894
  • Johnson, D. M. (2016) A comprehensive assessment of the correlations between field crop yields and commonly used MODIS products. International Journal of Applied Earth Observation and Geoinformation, Volume 52, Pages 65-81, ISSN 0303-2434, https://doi.org/10.1016/j.jag.2016.05.010.
  • Lasko K, Vadrevu KP, Tran VT, Ellicott E, Nguyen TTN, Bui HQ, Justice C. 2017. Satellites may underestimate rice residue and associated burning emissions in Vietnam. Environmental Research Letters, 12(8), 085006.
  • Laso Bayas, J.C., See, L., Perger, C., Justice C., Nakalembe, C., Dempewolf, J., & Fritz, S. (2017). Validation of Automatically Generated Global and Regional Cropland Data Sets: The Case of Tanzania. Remote Sensing 9 (8). e815. DOI: https://doi.org/10.3390/rs9080815.
  • Li, Y., Sulla-Menashe, D., Motesharrei, S., Song, X. P., Kalnay, E., Ying, Q., ... & Ma, Z. (2017). Inconsistent estimates of forest cover change in China between 2000 and 2013 from multiple datasets: differences in parameters, spatial resolution, and definitions. Scientific Reports, 7(1), 8748. doi:10.1038/s41598-017-07732-5
  • Lunt, T., A.W. Jones, W.S. Mulhern, D.P.M. LeZaks, M.M. Jahn. 2016. Vulnerabilities to agricultural production shocks: An extreme, plausible scenario for assessment of risk for the insurance sector.  Climate Risk Management.  DOI 10.1016/j.crm.2016.05.001
  • McNally, A., Arsenault, K., Kumar, S., Shukla, S., Peterson, P., Wang, S., ... & Verdin, J. P. (2017). A land data assimilation system for sub-Saharan Africa food and water security applications. Scientific data, 4, 170012. doi:10.1038/sdata.2017.12
  • Mladenova, I. E., Bolten, J. D., Crow, W. T., Anderson, M. C., Hain, C. R., Johnson, D. M., & Mueller, R. (2017). Intercomparison of Soil Moisture, Evaporative Stress, and Vegetation Indices for Estimating Corn and Soybean Yields Over the US. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(4), 1328-1343. doi: 10.1109/JSTARS.2016.2639338
  • Molinario, G., Hansen, M. C., Potapov, P. V., Tyukavina, A., Stehman, S., Barker, B., & Humber, M. (2017). Quantification of land cover and land use within the rural complex of the Democratic Republic of Congo. Environmental Research Letters, 12(10), 104001. https://doi.org/10.1088/1748-9326/aa8680
  • Nakalembe, C., Dempewolf, J., & Justice, C. (2017). Agricultural land use change in Karamoja Region, Uganda. Land Use Policy, 62, 2-12. https://doi.org/10.1016/j.landusepol.2016.11.029
  • Niles, M. T., & Brown, M. E. (2017). A multi-country assessment of factors related to smallholder food security in varying rainfall conditions. Scientific reports, 7(1), 16277. doi:10.1038/s41598-017-16282-9
  • Pittman, K., Hansen, M. C., Becker-Reshef, I., Potapov, P. V., & Justice, C. O. (2010). Estimating global cropland extent with multi-year MODIS data. Remote Sensing, 2(7), 1844-1863. doi:10.3390/rs2071844
  • Roy, S. K., Rowlandson, T. L., Berg, A. A., Champagne, C., & Adams, J. R. (2016). Impact of sub-pixel heterogeneity on modelled brightness temperature for an agricultural region. International journal of applied earth observation and geoinformation, 45, 212-220. https://doi.org/10.1016/j.jag.2015.10.003
  • Schulthess, U., Krupnik, T.J., Ahmed, Z.U., McDonald, A.J. (2015). Technology targeting for sustainable intensification of crop production in the Delta region of Bangladesh, in: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives. doi:10.5194/isprsarchives-XL-7-W3-1475-2015
  • See, L., S. Fritz, L. You, N. Ramankutty, M. Herrero, C. Justice, I. Becker-Reshef, P.Thornton, K. Erb, P. Gong, H. Tang, M.van der Velde, P. Ericksen, I. McCallum, F. Kraxner, M. Obersteiner. 2015. Improved global cropland data as an essential ingredient for food security. Global Food Security, Volume 4, page 37-45
  • Shukla, S., McNally, A., Husak, G., & Funk, C. (2014). A seasonal agricultural drought forecast system for food-insecure regions of East Africa. Hydrology and Earth System Sciences, 18(10), 3907-3921. doi: 10.5194/hess-18-3907-2014.
  • Skakun S., Roger J.-C. , Vermote E.F. , Masek J.G. & Justice C.O. (2017). Automatic sub-pixel co-registration of Landsat-8 Operational Land Imager and Sentinel-2A Multi-Spectral Instrument images using phase correlation and machine learning based mapping. International Journal of Digital Earth (2016 IF: 2.292; 5-year IF: 2.978), vol. 10, no. 12, pp. 1253–1269. doi:10.1080/17538947.2017.1304586.
  • Skakun S., Vermote E., Roger J.-C., & Justice C. (2017) Multi-spectral misregistration of Sentinel-2A images: analysis and implications for potential applications. IEEE Geoscience and Remote Sensing Letters (2016 IF: 2.761; 5-year IF: 2.899), vol. 14, no. 12, pp. 2408-2412. doi:10.1109/LGRS.2017.2766448.
  • Skakun, S., Franch, B., Vermote, E., Roger, J.-C. Becker-Reshef, I., Justice, C., Kussul, N. (2017). Early season large-area winter crop mapping using MODIS NDVI data, growing degree days information and a Gaussian mixture model. Remote Sensing of Environment (2016 IF: 6.265; 5-year IF: 7.653), vol. 195, 244–258. doi:10.1016/j.rse.2017.04.026.
  • Song, X. P., Potapov, P. V., Krylov, A., King, L., Di Bella, C. M., Hudson, A., ... & Hansen, M. C. (2017). National-scale soybean mapping and area estimation in the United States using medium resolution satellite imagery and field survey. Remote sensing of environment, 190, 383-395. https://doi.org/10.1016/j.rse.2017.01.008
  • Tadesse, T., Champagne, C., Wardlow, B. D., Hadwen, T. A., Brown, J. F., Demisse, G. B., ... & Davidson, A. M. (2017). Building the vegetation drought response index for Canada (VegDRI-Canada) to monitor agricultural drought: first results. GIScience & Remote Sensing, 54(2), 230-257. https://doi.org/10.1080/15481603.2017.1286728
  • Tesfaye, K., Kassie, M., Cairns, J.E., Michael, M., Stirling, C., Abate, T., Prasanna, B.M., Mekuria, M., Hailu, H., Erenstein, O., Gerard, B. (2017). Potential for Scaling up Climate Smart Agricultural Practices: Examples from Sub-Saharan Africa, in: Climate Change Adaptation in Africa. Springer, pp. 185–203. https://doi.org/10.1007/978-3-319-49520-0_12
  • Torbick, N., Chowdhury, D., Salas, W., & Qi, J. (2017). Monitoring rice agriculture across myanmar using time series Sentinel-1 assisted by Landsat-8 and PALSAR-2. Remote Sensing, 9(2), 119. doi:10.3390/rs9020119
  • Turner, M.D., Butt, B., Singh, A., Brottem, L., Ayantunde, A., Gerard, B. (2016). Variation in vegetation cover and livestock mobility needs in Sahelian West Africa. J. Land Use Sci. 11, 76–95. https://doi.org/10.1080/1747423X.2014.965280
  • Vadrevu, K.P., Nemani, R., Justice, C., and Gutman, G. (Eds). (2017). Mapping, Monitoring and Impact Assessment of Land Cover/Land Use Changes in South and South East Asia. Remote Sensing (MDPI) Special Issue. (ISSN 2072-4292).
  • Vadrevu, K.P., Ohara, T., and Justice, C. (Eds). (2017). Land Atmospheric Research Applications in Asia. 30-Chapters. Springer Verlag. (ISBN: 978-3-319-67473-5)
  • Vanlauwe, B., Barrios, E., Robinson, T., Van Asten, P., Zingore, S., Gerard, B. (2017). System productivity and natural resource integrity in smallholder farming: Friends or foes?, in: Oborn, I., Vanlauwe, B., Phillips, M., Thomas, R., Brooijmans, W., Atta-Krah, K. (eds.), Sustainable
  • Whitcraft, A. K., Becker-Reshef, I., & Justice, C. (2015). A Framework for Defining Spatially Explicit Earth Observation Requirements for a Global Agricultural Monitoring Initiative (GEOGLAM). Remote Sensing. Remote Sensing 7(2), 1461-1581.
  • Whitcraft, A. K., Becker-Reshef, I., & Justice, C. O. (2015). Agricultural growing season calendars derived from MODIS surface reflectance. International Journal of Digital Earth, 8(3), 173-197. https://doi.org/10.1080/17538947.2014.894147
  • Whitcraft, A. K., Becker-Reshef, I., Killough, B. D., & Justice, C. O. (2015). Meeting Earth Observation Requirements for Global Agricultural Monitoring: An Evaluation of the Revisit Capabilities of Current and Planned Moderate Resolution Optical Earth Observing Missions. Remote Sensing 7(2), 1482-1503.
  • Whitcraft, A. K., Vermote, E. F., Becker-Reshef, I., & Justice, C. O. (2015). Cloud cover throughout the agricultural growing season: Impacts on passive optical earth observations. Remote Sensing of Environment, 156, 438–447. doi:10.1016/j.rse.2014.10.009
  • White, J. W., Hunt, L. A., Boote, K. J., Jones, J. W., Koo, J., Kim, S., ... & Hoogenboom, G. (2013). Integrated description of agricultural field experiments and production: The ICASA Version 2.0 data standards. Computers and Electronics in Agriculture, 96, 1-12. ISSN 0168-1699, doi: 10.1016/j.compag.2013.04.003