Cloud cover throughout the agricultural growing season: Impacts on passive optical earth observations

Remote Sensing of Environment

Volumn 156, Issue , 2015, Pages 438-447

  Whitcraft, Alyssa K ^a   Vermote, Eric F ^b   Becker Reshef, Inbal ^a   Justice, Christopher O ^a  

^a UNIVERSITY OF MARYLAND   (United States)

^b NASA GODDARD SPACE FLIGHT CENTER   (United States)

Author keywords

Acquisition planning; Agriculture; Cloud cover; Earth observations; GEOGLAM; MODIS; Optical data

Indexed keywords

CROPS; OBSERVATORIES; ORBITS; RADAR IMAGING; RADIOMETERS; REMOTE SENSING; SPACE-BASED RADAR; SYNTHETIC APERTURE RADAR;

ACQUISITION PLANNING; CLOUD COVER; EARTH OBSERVATIONS; GEOGLAM; MODIS; OPTICAL DATA;

AGRICULTURE;

AGRICULTURAL LAND; AQUA (SATELLITE); BIOMASS; CLOUD COVER; EOS; GROWING SEASON; MODIS; MONITORING SYSTEM; RADAR IMAGERY; REMOTE SENSING; SPATIOTEMPORAL ANALYSIS;

EID: 84922252429     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2014.10.009     Document Type: Article

References (48)

1. Arvidson T., Gasch J., Goward S.N. Landsat 7's long-term acquisition plan - An innovative approach to building a global imagery archive. Remote Sensing of Environment 2001, 78(1):13-26.
2. Arvidson T., Goward S., Gasch J., Williams D. Landsat-7 long-term acquisition plan: Development and validation. Photogrammetric Engineering and Remote Sensing 2006, 72(10):1137.
3. Becker-Reshef I., Justice C., Sullivan M., Vermote E., Tucker C., Anyamba A., et al. Monitoring global croplands with coarse resolution earth observations: The Global Agriculture Monitoring (GLAM) project. Remote Sensing 2010, 2(6):1589-1609.
4. Becker-Reshef I., Vermote E., Lindeman M., Justice C. A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data. Remote Sensing of Environment 2010, 114(6):1312-1323.
5. Boken V.K., Shaykewich C.F. Improving an operational wheat yield model using phenological phase-based Normalized Difference Vegetation Index. International Journal of Remote Sensing 2002, 23(20):4155-4168.
6. Bolton D.K., Friedl M.A. Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics. Agricultural and Forest Meteorology 2013, 173:74-84. 10.1016/j.agrformet.2013.01.007.
7. Cairns B. Diurnal variations of cloud from ISCCP data. Atmospheric Research 1995, 37(1-3):133-146. 10.1016/0169-8095(94)00074-N.
8. Chen P.-Y., Fedosejevs G., Tiscareño-LóPez M., Arnold J.G. Assessment of MODIS-EVI, MODIS-NDVI and VEGETATION-NDVI composite data using agricultural measurements: An example at corn fields in western Mexico. Environmental Monitoring and Assessment 2006, 119(1-3):69-82. 10.1007/s10661-005-9006-7.
9. Chernokulsky A.V., Mokhov I.I. Comparison of global cloud climatologies. Current Issues of Remote Sensing of Earth from Space 2009, 6(2):235-243.
10. Duveiller G., López-Lozano R., Seguini L., Bojanowski J.S., Baruth B. Optical remote sensing requirements for operational crop monitoring and yield forecasting in Europe. Proceedings of Sentinel-3 OLCI/SLSTR and MERIS/(A) ATSR Workshop, Frascati, Italy 2013, (ESA SP-711. Retrieved from http://www.researchgate.net/publication/235968254_Optical_Remote_Sensing_Requirements_for_Operational_Crop_Monitoring_and_Yield_Forecasting_in_Europe/file/3deec514f64c07ff5c.pdf).
11. Fritz S., See L., You L., Justice C., Becker-Reshef I., Bydekerke L., et al. The need for improved maps of global cropland. Eos, Transactions American Geophysical Union 2013, 94(3):31-32. 10.1002/2013EO030006.
12. Gao F., Masek J., Schwaller M., Hall F. On the blending of the Landsat and MODIS surface reflectance: Predicting daily Landsat surface reflectance. IEEE Transactions on Geoscience and Remote Sensing 2006, 44(8):2207-2218.
13. Goward S.N., Arvidson T., Williams D.L., Irish R., Irons J.R. Moderate spatial resolution optical sensors 2009, SAGE Publications Ltd., London, UK, (Retrieved from http://books.google.fr/books?hl=fr&lr=&id=uIv82prTp4IC&oi=fnd&pg=PA123&dq=Goward,+S.N.,+2009&ots=Q7gUx-hBWm&sig=rS4YRIHrWu7o-171Ad5oec84eII).
14. Goward S., Chander G., Pagnutti M., Marx A., Ryan R., Thomas N., et al. Complementarity of ResourceSat-1 AWiFS and Landsat TM/ETM+sensors. Remote Sensing of Environment 2012, 123:41-56.
15. Goward S., Williams D., Arvidson T., Irons J. The future of Landsat-class remote sensing. Land Remote Sensing and Global Environmental Change 2011, 807-834. Springer, (Retrieved from http://link.springer.com/chapter/10.1007/978-1-4419-6749-7_35).
16. Guindin-Garcia N., Gitelson A.A., Arkebauer T.J., Shanahan J., Weiss A. An evaluation of MODIS 8- and 16-day composite products for monitoring maize green leaf area index. Agricultural and Forest Meteorology 2012, 161:15-25. 10.1016/j.agrformet.2012.03.012.
17. Gunderson A., Chodas M. An investigation of cloud cover probability for the HyspIRI mission using MODIS cloud mask data. Aerospace Conference, 2011 IEEE 2011, 1-14. (Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5747393).
18. Irish R.R., Barker J.L., Goward S.N., Arvidson T. Characterization of the Landsat-7 ETM+automated cloud-cover assessment (ACCA) algorithm. Photogrammetric Engineering and Remote Sensing 2006, 72(10):1179.
19. Johnson D.M. An assessment of pre-and within-season remotely sensed variables for forecasting corn and soybean yields in the United States. Remote Sensing of Environment 2014, 141:116-128.
20. Ju J., Roy D.P. The availability of cloud-free Landsat ETM+data over the conterminous United States and globally. Remote Sensing of Environment 2008, 112(3):1196-1211.
21. Justice C.O., Becker-Reshef I. Report from the workshop on developing a strategy for global agricultural monitoring in the framework of Group on Earth Observations (GEO) 2007, UN FAO, (July).
22. Justice C.O., Román M.O., Csiszar I., Vermote E.F., Wolfe R.E., Hook S.J., et al. Land and cryosphere products from Suomi NPP VIIRS: Overview and status. Journal of Geophysical Research, [Atmospheres] 2013, 118(17):9753-9765.
23. Kaufman Y.J., Remer L.A., Tanré D., Li R.-R., Kleidman R., Mattoo S., et al. A critical examination of the residual cloud contamination and diurnal sampling effects on MODIS estimates of aerosol over ocean. IEEE Transactions on Geoscience and Remote Sensing 2005, 43(12):2886-2897.
24. Kotchenova S.Y., Vermote E.F. Validation of a vector version of the 6S radiative transfer code for atmospheric correction of satellite data. Part II. Homogeneous Lambertian and anisotropic surfaces. Applied Optics 2007, 46(20):4455-4464.
25. Kotchenova S.Y., Vermote E.F., Matarrese R., Klemm F.J. Validation of a vector version of the 6S radiative transfer code for atmospheric correction of satellite data. Part I: Path radiance. Applied Optics 2006, 45(26):6762-6774.
26. Li A., Liang S., Wang A., Qin J. Estimating crop yield from multi-temporal satellite data using multivariate regression and neural network techniques. Photogrammetric Engineering and Remote Sensing 2007, 73(10):1149.
27. Meng J., Wu B. Study on the crop condition monitoring methods with remote sensing. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2008, 37(B8):945-950.
28. Mercury M., Green R., Hook S., Oaida B., Wu W., Gunderson A., et al. Global cloud cover for assessment of optical satellite observation opportunities: A HyspIRI case study. Remote Sensing of Environment 2012, 126:62-71.
29. Minnis P. Viewing zenith angle dependence of cloudiness determined from coincident GOES East and GOES West data. Journal of Geophysical Research: Atmospheres (1984-2012) 1989, 94(D2):2303-2320.
30. Minnis P., Harrison E.F. Diurnal variability of regional cloud and clear-sky radiative parameters derived from GOES data. Part I: Analysis method. Journal of Climate and Applied Meteorology 1984, 23:993-1011.
31. Minnis P., Sun-Mack S., Young D.F., Heck P.W., Garber D.P., Chen Y., et al. CERES Edition-2 cloud property retrievals using TRMM VIRS and Terra and Aqua MODIS Data - Part I: Algorithms. IEEE Transactions on Geoscience and Remote Sensing 2011, 49(11):4374-4400.
32. Minnis P., Trepte Q.Z., Sun-Mack S., Chen Y., Doelling D.R., Young D.F., et al. Cloud detection in nonpolar regions for CERES using TRMM VIRS and Terra and Aqua MODIS data. IEEE Transactions on Geoscience and Remote Sensing 2008, 46(11):3857-3884.
33. Ramankutty N., Foley J.A., Norman J., McSweeney K. The global distribution of cultivable lands: Current patterns and sensitivity to possible climate change. Global Ecology and Biogeography 2002, 11(5):377-392. 10.1046/j.1466-822x.2002.00294.x.
34. Rossow W.B., Schiffer R.A. Advances in understanding clouds from ISCCP. Bulletin of the American Meteorological Society 1999, 80(11):2261-2287.
35. Roy D.P., Ju J., Kline K., Scaramuzza P.L., Kovalskyy V., Hansen M., et al. Web-enabled Landsat Data (WELD): Landsat ETM+ composited mosaics of the conterminous United States. Remote Sensing of Environment 2010, 114(1):35-49.
36. Roy D.P., Lewis P., Schaaf C.B., Devadiga S., Boschetti L. The global impact of clouds on the production of MODIS bidirectional reflectance model-based composites for terrestrial monitoring. IEEE Geoscience and Remote Sensing Letters 2006, 3(4):452-456.
37. Sakamoto T., Gitelson A.A., Arkebauer T.J. MODIS-based corn grain yield estimation model incorporating crop phenology information. Remote Sensing of Environment 2013, 131:215-231. 10.1016/j.rse.2012.12.017.
38. Sakamoto T., Yokozawa M., Toritani H., Shibayama M., Ishitsuka N., Ohno H. A crop phenology detection method using time-series MODIS data. Remote Sensing of Environment 2005, 96(3):366-374.
39. Singh Parihar J., Justice C., Soares J., Leo O., Kosuth P., Jarvis I., et al. GEO-GLAM: A GEOSS-G20 initiative on Global Agricultural Monitoring. 39th COSPAR Scientific Assembly. Held 14-22 July 2012, in Mysore, India. Abstract D2.2-9-12 2012, vol. 39:1451. (Retrieved from http://adsabs.harvard.edu/abs/2012cosp. . .39.1451S).
40. Stubenrauch C.J., Rossow W.B., Kinne S., Ackerman S., Cesana G., Chepfer H., et al. Assessment of global cloud datasets from satelites. Bulletin of the American Meteorological Society 2013, 94(7).
41. Vermote E.F., El Saleous N.Z., Justice C.O. Atmospheric correction of MODIS data in the visible to middle infrared: First results. Remote Sensing of Environment 2002, 83(1):97-111.
42. Vermote E.F., Kotchenova S. Atmospheric correction for the monitoring of land surfaces. Journal of Geophysical Research, [Atmospheres] 2008, 113(D23). (n/a-n/a). 10.1029/2007JD009662.
43. Wardlow B.D., Kastens J.H., Egbert S.L. Using USDA crop progress data for the evaluation of greenup onset date calculated from MODIS 250-meter data. Photogrammetric Engineering and Remote Sensing 2006, 72(11):1225-1234.
44. Whitcraft A.K., Becker-Reshef I., Justice C.O. Agricultural growing season calendars derived from MODIS surface reflectance. International Journal of Digital Earth 2014, 1-25. 10.1080/17538947.2014.894147.
45. Whitcraft A.K., Becker-Reshef I., Killough B.D., Justice C.O. 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 2014, (in review).
46. Wylie D., Jackson D.L., Menzel W.P., Bates J.J. Trends in global cloud cover in two decades of HIRS observations. Journal of Climate 2005, 18(15):3021-3031.
47. Wylie D., Menzel W.P. Eight years of high cloud statistics using HIRS. Journal of Climate 1999, 12(1):170-184.
48. Zhang X., Friedl M.A., Schaaf C.B., Strahler A.H., Hodges J.C., Gao F., et al. Monitoring vegetation phenology using MODIS. Remote Sensing of Environment 2003, 84(3):471-475.