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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » People » Feng Gao

Feng Gao

Research Physical Scientist

Feng Gao, Ph.D.
Research Physical Scientist
USDA-ARS Hydrology and Remote Sensing Laboratory
Bldg. 007, Rm. 101, BARC-West
Beltsville, MD 20705-2350 USA
Voice: (301) 504-6576
Fax: (301) 504-8931

Research Interests: (click here to see a list of current research projects)


Professional Experience:

Honors and Awards:

Publication Databases:

Selected Publications:

Xue, J., Anderson, M.C., Gao, F. et al. Improving the spatiotemporal resolution of remotely sensed ET information for water management through Landsat, Sentinel-2, ECOSTRESS and VIIRS data fusion. Irrig Sci. doi:10.1007/s00271-022-00799-7. 2022.

Bhattarai, N., D’Urso, G., Kustas, W.P., Bambach-Ortiz, N., Anderson, M., McElrone, A.J., Knipper, K.R., Gao, F., Alsina, M.M., Aboutalebi, M., Mckee, L., Alfieri, J.G., Prueger J.H., Belfiore, O.R. Influence of modeling domain and meteorological forcing data on daily evapotranspiration estimates from a Shuttleworth–Wallace model using Sentinel-2 surface reflectance data. Irrig Sci. doi:10.1007/s00271-022-00768-0. 2022.

Yang Y., Anderson M., Gao F., Xue J., Knipper K., Hain C. Improved Daily Evapotranspiration Estimation Using Remotely Sensed Data in a Data Fusion System. Remote Sensing. 14(8):1772. doi:10.3390/rs14081772. 2022.

Chen, F., Lei, F., Knipper, K., Gao, F. et al. Application of the vineyard data assimilation (VIDA) system to vineyard root-zone soil moisture monitoring in the California Central Valley. Irrig Sci. doi:10.1007/s00271-022-00789-9. 2022.

Burchard-Levine, V., Nieto, H., Kustas, W.P., Gao, F. et al. Application of a remote-sensing three-source energy balance model to improve evapotranspiration partitioning in vineyards. Irrig Sci. doi:10.1007/s00271-022-00787-x. 2022.

Kustas, W.P., Nieto, H., Garcia-Tejera, Bambach, O.N., McElrone, A.J., Gao, F. et al. Impact of advection on two-source energy balance (TSEB) canopy transpiration parameterization for vineyards in the California Central Valley. Irrig Sci. doi:10.1007/s00271-022-00778-y. 2022.

Nieto, H., Alsina, M.M., Kustas, W.P., García-Tejera, O., Chen, F., Bambach N., Gao, F. et al. Evaluating different metrics from the thermal-based two-source energy balance model for monitoring grapevine water stress. Irrig Sci. doi:10.1007/s00271-022-00790-2. 2022.

Gao, R., Torres-Rua, A.F., Aboutalebi, M., White, W.A., Anderson, M., Kustas, W.P., Agam, N., Alsina, M.M., Alfieri, J., Hipps, L., Dokoozlian, N., Nieto, H., Gao, F., McKee, L.G., Prueger, J.H., Sanchez, L., Mcelrone, A.J., Bambach-Ortiz, N., Coopmans, C., Gowing, I.. LAI estimation across California vineyards using sUAS multi-seasonal multi-spectral, thermal, and elevation information and machine learning. Irrig Sci. doi:10.1007/s00271-022-00776-0. 2022.

Gao F., Anderson M.C., Johnson D.M., Seffrin R., Wardlow B., Suyker A., Diao C., Browning D.M. Towards Routine Mapping of Crop Emergence within the Season Using the Harmonized Landsat and Sentinel-2 Dataset. Remote Sensing. 13(24):5074. doi:10.3390/rs13245074. 2021.

Kearney, S.P., Porensky, L.M., Augustine, D.J., Derner, J.D., Gao, F. 2021. Predicting spatial-temporal patterns of diet quality and large herbivore performance using satellite time series. Ecological Applications. doi:10.1002/eap.2503. 2021.

Yang, Y., Anderson, M.C., Gao, F., Wood, J.D., Gu, L., Hain, C. Studying drought-induced forest mortality using high spatiotemporal resolution evapotranspiration data from thermal satellite imaging. Remote Sensing of Environment. 265/112640. doi:10.1016/j.rse.2021.112640. 2021.

Xue, J., Anderson, M.C., Gao, F., Hain, C., Yang, Y., Knipper, K.R., Kustas, W.P., Yang, Y. Mapping daily evapotranspiration at field scale using the Harmonized Landsat and Sentinel-2 dataset, with sharpened VIIRS as a Sentinel-2 thermal proxy. Remote Sensing. 13:3420. doi:10.3390/rs13173420. 2021.

Kang, Y., Ozdogan, M., Gao, F., Anderson, M.C., White, W.A., Yang, Yun, Yang, Yang, Erickson, T.A. A data-driven approach to estimate leaf area index for Landsat images over the contiguous US. Remote Sens. Environ. 258, 112383. doi: 10.1016/j.rse.2021.112383. 2021.

Yang, Y., Anderson, M.C., Gao, F., Johnson, D.M., Yang, Y., Sun, L., Dulaney, W., Hain, C., Otkin, J.A., Prueger, J., Meyers, T., Bernacchi, C.J., Moore, C.E. Phenological corrections to a field-scale, ET-based crop stress indicator: an application to yield forecasting across the U.S. Corn Belt. Remote Sensing of Environment. 257, 112337. doi: 10.1016/j.rse.2021.112337. 2021.

Diao, C., Yang, Z., Gao, F., Zhang, X., Yang, Z. Hybrid phenology matching model for robust crop phenological retrieval. ISPRS Journal of Photogrammetry and Remote Sensing. 181, 308–326. doi: 10.1016/j.isprsjprs.2021.09.011. 2021.

Zhang, X., Gao. F., Wang, J., Ye, Y. Evaluating a spatiotemporal shape-matching model for the generation of synthetic high spatiotemporal resolution time series of multiple satellite data. International Journal of Applied Earth Observations and Geoinformation. 104, 102545. doi: 10.1016/j.jag.2021.102545. 2021.

Gao, F., Zhang, X.Y. Mapping crop phenology in near real-time using satellite remote sensing: challenges and opportunities. Journal of Remote Sensing. doi: 10.34133/2021/8379391. 2021.

Wong, A., Jin, Y., Medellin-Azuara, J., Paw, K., Kent, E., Clay, J., Gao, F., Fisher, J., Rivera, G., Lee, C., Hemes, K., Echelmann, E., Baldocchi, D., Hook, S. Multiscale assessment of agricultural consumptive water use in California's central valley. Water Resources Research. 5. doi: 10.1029/2020WR028876. 2021.

D’Urso G., Bolognesi S.F., Kustas W.P., Knipper K.R., Anderson M.C., Alsina M.M., Hain C.R., Alfieri J.G., Prueger J.H., Gao F., McKee L.G., De Michele C., McElrone A.J., Bambach N., Sanchez L., Belfiore O.R. Determining evapotranspiration by using combination equation models with Sentinel-2 data and comparison with thermal-based energy balance in a California irrigated vineyard. Remote Sensing. 13(18), 3720. doi: 10.3390/rs13183720. 2021.

Taylor, S.D., Browning, D.M., Baca, R.A., Gao, F. Constraints and Opportunities for Detecting Land Surface Phenology in Drylands. Journal of Remote Sensing. doi: 10.34133/2021/9859103. 2021.

Carpintero, E., Anderson, M.C., Andreu, A., Hain, C., Gao, F., Kustas, W.P., González-Dugo, M.P. Estimating evapotranspiration of mediterranean oak savanna at multiple temporal and spatial resolutions. Implications for water resources management. Remote Sensing. 13(18), 3701. doi: 10.3390/rs13183701. 2021.

Gao, F., Anderson, M.C., Daughtry, C.S., Karnieli, A., Hively, W.D., Kustas, W.P. A within-season approach for detecting early growth stages in corn and soybean using high temporal and spatial resolution imagery. Remote Sensing of Environment. 242, 11752. doi: 10.1016/j.rse.2020.111752. 2020.

Cao, Z., Chen, S., Gao, F., Li, X. Improving phenological monitoring of winter wheat by considering sensor spectral response in spatiotemporal image fusion. Physics and Chemistry of the Earth. doi: 10.1016/j.pce.2020.102859. 2020.

Shuai, Y., Tuerhanjiang, L., Shao, C., Gao, F., Zhou, Y., Xie, D., Liu, T., Liang, J., Chu, N. Re-understanding of land surface albedo and related terms in satellite-based retrievals. Big Earth Data. doi: 10.1080/20964471.2020.1716561. 2020.

Zhang, X., Wang, J. , Henebry, G., Gao, F. Development and evaluation of a new algorithm for detecting 30m land surface phenology from VIIRS and HLS time series. Journal of Photogrammetry and Remote Sensing. doi: 10.1016/j.isprsjprs.2020.01.012. 2020.

Lei, F., Crow, W.T., Kustas, W.P., Dong, J., Yang, Y., Knipper, K., Anderson, M.C., Gao, F., Notarnicola, C., Greifeneder, F., McKee, L.G., Alfieri, J.G., Hain, C., Dokoozlian, N. Data assimilation of high-resolution thermal and radar remote sensing retrievals for soil moisture monitoring in a drip-irrigated vineyard. Remote Sensing of Environment. 239, 111622. doi: 10.1016/j.rse.2019.111622. 2020.

Gao, F., Anderson, M.C., Hively, W.D. Detecting cover crop end-of-season using VENµS and Sentinel-2 satellite imagery. Remote Sensing. 12, 3524. doi: 10.3390/rs12213524. 2020.

Sun, L., Gao, F., Xie, D., Anderson, M., Chen, R., Yang, Y., Yang, Y., Chen, Z. Reconstructing daily 30 m NDVI over complex agricultural landscapes using a crop reference curve approach. Remote Sensing of Environment. 112156. doi: 10.1016/j.rse.2020.112156. 2020.

Xue, J., Anderson, M.C., Gao, F., Hain, C., Sun, L., Yang, Y., Knipper, K., Kustas, W.P.,  Torres-Rua, A., Schull, M. Sharpening ECOSTRESS and VIIRS land surface temperature using harmonized Landsat-Sentinel surface reflectances. Remote Sensing of Environment. 251, 112055. doi: 10.1016/j.rse.2020.112055. 2020.

Anderson, M.C., Yang, Y., Xue, J., Knipper, K.R., Yang, Y., Gao, F. , Hain, C., Kustas, W.P., Cawse-Nicholson, K., Hulley, G., Fisher, J.B., Alfieri, J.G., Meyers, T., Prueger, J., Baldocchi, D., Rey-Sanchez, C. Interoperability of ECOSTRESS and Landsat for mapping evapotranspiration time series at sub-field scale. Remote Sensing of Environment. 252, 112189. doi: 10.1016/j.rse.2020.112189. 2020.

Mourad, R., Jaafar, H., Anderson, M., Gao, F. Assessment of leaf area index models using harmonized Landsat and Sentinel-2 surface reflectance data over a semi-arid irrigated landscape. Remote Sensing. 12(19), 3121. doi: 10.3390/rs12193121. 2020.

Ohana-Levi, N., Knipper, K., Kustas, W.P., Anderson, M.C., Netzer, Y., Gao, F., Alsina, M.M., Sanchez, L.A., Karnieli, A. Using satellite thermal-based evapotranspiration time series for defining management zones and spatial association to local attributes in a vineyard. Remote Sensing. 12(15), 2436. doi: 10.3390/rs12152436. 2020.

Wilson, T.G., Kustas, W.P., Alfieri, J.G., Anderson, M.C., Gao, F., Prueger, J.H., McKee, L.G., Alsina, M.M., Sanchez, L.A., Alstad, K.P. Relationships between soil water content, evapotranspiration, and irrigation measurements in a California drip-irrigated Pinot noir vineyard. Agricultural Water Management. 237, 106186. doi: 10.1016/j.agwat.2020.106186. 2020.

Anderson, M., Diak, G., Gao, F., Knipper, K., Hain, C., Eichelmann, E., Hemes, K., Baldocchi, D., Kustas, W., Yang, Y. Impact of insolation data source on remote sensing retrievals of evapotranspiration over the California Delta. Remote Sensing. 11(3), 216. doi: 10.3390/rs11030216. 2019.

Wulder, M., Loveland, T., Roy, D., Crawford, C., Masek, J., Woodcock, C., Allen, R., Anderson, M.C., Belward, A., Cohen, W., Dwyer, J., Erd, A., Gao, F., Griffiths, P., Helder, D., Hermosilla, T., Hipple, J., Hostert, P., Hughes, M., Huntington, J., Johnson, D., Kennedy, R., Kilic, A., Li, Z., Lymburner, L. Current status of Landsat program, science, and applications. Remote Sensing of Environment. 225, 127-147. doi: 10.1016/j.rse.2019.02.015. 2019.

Tao, H., Gao, F., Liang, S., Peng, Y. Mapping climatological bare soil albedos over the contiguous United States using MODIS data. Remote Sensing. 11, 666. doi: 10.3390/rs11060666. 2019.

Knipper K., Kustas, W., Anderson, M., Alfieri, J., Prueger, J., Gao, F., McKee, L., Alisina, M., Hain, C., and Sanchex, L. Using high-spatiotemporal thermal satellite ET retrievals for near-real time water use and stress monitoring in a California vineyard. Remote Sensing. 11(18), 2124. doi: 10.3390/rs11182124. 2019.

Qian, Y., Yang, Z., Di, L., Rahman, M., Xue, L., Tan, Z., Gao, F., Yu, E., Zhang, X. Crop Growth Condition Assessment at County Scale Based on Heat-Aligned Growth Stages.  Remote Sensing. 11, 2439. doi:10.3390/rs11202439. 2019.

He, T., Liang, S., Wang, D., Cao, Y., Gao, F., Yu, Y and Feng, M. Evaluating land surface albedo estimation from Landsat MSS, TM, ETM+, and OLI data based on the unified direct estimation approach. Remote Sensing of Environment. 204, 181-196. doi:10.1016/j.rse.2017.10.031. 2018.

Yang, Y., Anderson, M.C., Gao, F., Wardlow, B., Hain, C., Otkin, J., Alfieri, J.G., Yang, Yun, Sun, L., Dulaney, W.P. Field-scale mapping of evaporative stress indicators of crop yield: an application over Mead, Nebraska Remote Sensing of Environment. 210, 387-402. doi:10.1016/j.rse.2018.02.020. 2018.

Kwan, C., Budavari, B., Gao, F. and Zhu, X. A hybrid color mapping approach to fusing MODIS and Landsat images for forward prediction. Remote Sensing. 10(4), 520. doi:10.3390/rs10040520. 2018.

Kwan, C., Zhu, X., Gao, F., Chou1, B., Perez, D., Li, J., Shen, Y., Koperski, K. and  Marchisio, G. Assessment of spatiotemporal fusion algorithms for Planet and Worldview images. Sensors. 18(4), 1051. doi:10.3390/s18041051. 2018.

Kustas, W., Anderson, M.C., Alfieri, J.G., Knipper, K., Torres-Rua, A., Parry, C.K., Nieto, H., Agam, N., White, A., Gao, F., McKee, L., Prueger, J.H., Hipps, L.E., Los, S., Alsina, M., Sanchez, L., Sams, B., Dokoozlian, N., Jones, S., McKee, M., McElrone, A., Heitman, J.L., Howard, A., Post, K., Melton, F.S. and Hain, C. The Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). Bulletin of the American Meteorological Society. doi: 10.1175/BAMS-D-16-0244.1. 2018.

Guan, K., Li, Z., Rao, N., Gao, F., Xie, D., Hien, N. and Zeng, Z. Mapping Paddy Rice Area and yields over Thai Binh province in Viet Nam from MODIS, Landsat and ALOS-2/PALSAR-2. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. doi: 10.1109/JSTARS.2018.2834383. 2018.

Alfieri, J.G., Kustas, W.P., Nieto, H., Prueger, J.H., Hipps, L.E., McKee, L.G. and Gao, F.  A multi-year intercomparison of micrometeorological observations at adjacent vineyards in California’s Central Valley during GRAPEX. Irrigation Science. doi: 10.1007/s00271-018-0599-3. 2018.

Knipper, K. R., Kustas, W. P., Anderson, M. C., Alfieri J. G., Prueger, J. H., Hain, C., Gao, F., Yang, Y., McKee, L. G., Nieto, H., Hipps, L. E., Alsina, M. and Sanchez, L. Evapotranspiration estimates derived using thermal-based satellite remote sensing and data fusion for irrigation management in California vineyards. Irrigation Science. doi: 10.1007/s00271-018-0591-y. 2018.

Kustas, W.P., Alfieri, J.G., Nieto, H., Wilson, T.G., Gao, F., Anderson, M.C. Utility of the two-source energy balance (TSEB) model in vine and inter-row flux partitioning over the growing season. Irrigation Science. doi: 10.1007/s00271-018-0586-8. 2018.

Nieto, H., Kustas, W. P., Torres, A., Alfieri J. G., Gao, F., Anderson, M. C., White, W. A., Song, L., Alsina M., Prueger, J. H. and McKee, L. G. Evaluation of TSEB turbulent fluxes using different methods for the retrieval of soil and canopy component temperatures from UAV thermal and multispectral imagery. Irrigation Science. doi: 10.1007/s00271-018-0585-9. 2018.

Nieto, H., Kustas, W. P., Alfieri, J. G., Gao, F., Hipps, L. E., Los, S., Prueger, J. H., McKee, L. G. and Anderson, M. C. Impact of different within‑canopy wind attenuation formulations on modelling sensible heat flux using TSEB. Irrigation Science. doi: 10.1007/s00271-018-0611-y. 2018.

Prueger, J.H., Parry, C.K., Kustas, W.P., Alfieri, J.G., Alsina, M.M., Nieto, H., Wilson, T.G.,   Hipps, L.E., Anderson, M.C., Hatfield, J.L., Gao, F., McKee, L.G., McElrone, A.J., Agam, N. and Los, S.A. Crop water stress index of an irrigated vineyard in the Central Valley of California. Irrigation Science. doi: 10.1007/s00271-018-0598-4. 2018.

White, W.A., Alsina, M.M., Nieto, H., McKee, L.G., Gao, F., Kustas, W.P. Determining a robust indirect measurement of leaf area index in California vineyards for validating remote sensing-based retrievals. Irrigation Science. doi: 10.1007/s00271-018-0614-8. 2018.

Anderson, M.C., Gao, F., Knipper, K., Hain, C., Dulaney, W.P., Baldocchi, D., Eichelmann, E., Hemes, K., Yang, Yun, Medellin-Azuara, J., Kustas, W.P. Field-scale assessment of land and water use change over the California Delta using remote sensing. Remote Sensing.  10(6):889. doi: 10.3390/rs10060889. 2018.

Gaffney, R., Porensky, L.M., Gao, F., Irisarri, J.G., Durante, M., Derner, J.D., Augustine, D.J. Using APAR to predict aboveground plant productivity in semi-arid rangelands: spatial and temporal relationships differ. Remote Sensing. 10, 1474. doi: 10.3390/rs10091474. 2018.

Gao, F., Anderson, M., Daughtry, C. and Johnson, D. Assessing variability of corn and soybean yields in central Iowa using high spatiotemporal resolution multi-satellite imagery. Remote Sensing. 10, 1489. doi:10.3390/rs10091489. 2018.

Li, Z., Huang, C., Zhu, Z., Gao, F., Tang, H., Xin, X., Ding, L., Shen, B., Liu, J., Chen, B., Wang, X. and Yan, R. Mapping daily leaf area index at 30m resolution over a meadow steppe area by fusing Landsat, Sentinel-2A and MODIS data. International Journal of Remote Sensing. 39(23), 9025-9053. doi: 10.1080/01431161.2018.1504342. 2018.

Zhou, J., Zhang, S., Yang, H., Xiao, Z., Gao, F. The retrieval of 30-m resolution LAI from Landsat data by combining MODIS products. Remote Sensing. 10, 1187, doi: 10.3390/rs10081187. 2018.

Qi, J., Zhang, X., McCarty, G., Sadeghi, A., Cosh, M., Zeng, X., Gao, F., Daughtry, C., Huang, C., Lang, M., Arnold, J. Assessing the performance of a physically-based soil moisture module integrated within the Soil and Water Assessment Tool. Environmental Modelling & Software. 109, 329-341. doi: 10.1016/j.envsoft.2018.08.024. 2018.

Liu, L., Zhang, X., Yu, Y., Gao, F., Yang,Z. Real-time monitoring of crop phenology in the midwestern United States using VIIRS observations. Remote Sensing. 10, 1640. doi: 10.3390/rs10101540. 2018.

Yang, Y., Anderson, M., Gao, F., Hain, C., Noormets, A., Sun, G., Wynne, R., Thomas, V., Liang, S. Investigating impacts of drought and disturbance on evapotranspiration over a forested landscape in North Carolina, USA using high spatiotemporal resolution remotely sensed data. Remote Sensing of Environment. doi: 10.1016/j.rse.2018.12.017. 2018.

Xie, D., Gao, F., Sun, L., Anderson, M. Improving spatial-temporal data fusion by choosing optical input image pairs. Remote Sensing. 10:1142. doi:10.3390/rs10071142. 2018.

Moller, M., Gerstmann, H., Gao, F., Dahms, T. and Forster, M. Coupling of phenological information and simulated vegetation index time series: Limitations and potentials for the assessment and monitoring of soil erosion risk. Catena.150:192-205. doi: 10.1016/j.catena.2016.11.016. 2017.

Jiao, T., Williams, C., Ghimire, B., Masek, J., Gao, F., Schaaf, C. Global climate forcing from albedo change caused by large-scale deforestation and reforestation: quantification and attribution of geographic variation. Climatic Change. doi:10.1007/s10584-017-1962-8. 2017.

Sun, L., Chen, Z., Gao, F., Anderson, M., Song, L., Wang, L., Hu, B. and Yang, Y. Reconstructing daily clear-sky land surface temperature for cloudy regions from MODIS data. Computers & Geosciences. 105:10-20. doi:10.1016/j.cageo.2017.04.007. 2017.

Yang, Y., Anderson, M, Gao, F., Hain, C. R., Semmens, K. A., Kustas, W. P., Noormets, A., Wynne, R. H., Thomas, V. A. and Sun, G. Daily Landsat-scale evapotranspiration estimation over a forested landscape in North Carolina, USA, using multi-satellite data fusion. Hydrology and Earth System Sciences. 21:1017-1037. doi:10.5194/hess-21-1017-2017. 2017.

Yang, Y., Anderson, M., Gao, F., Hain, C., Kustas, W., Meyers, T., Crow, W., Finocchiaro, R.,  Otkin, J., Sun, L. and Yang Y. Impact of tile drainage on evapotranspiration in South Dakota, USA, based on high spatiotemporal resolution evapotranspiration time series from a multisatellite data fusion system. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. PP(99):1-15. doi: 10.1109/JSTARS.2017.2680411. 2017.

Sun, L., Gao, F., Anderson, M., Kustas, W.P., Alsina, M., Sanchez, L., Sams, Brent, McKee, L.G., Dulaney, W.P., White, A., Alfieri, J.G., Prueger, J.H., Melton, F. and Post, K. Daily mapping of 30m LAI and NDVI for grape yield prediction in California vineyards. Remote Sensing. 9, 317. doi:10.3390/rs9040317. 2017.

Sun, L., Anderson, M., Gao, F., Hain, C., Alfieri, J., Sharifi, A., McCarty, G., Yang, Y., Yang, Y., Kustas, W. and McKee, L. Investigating water use over the Choptank River Watershed using a multi-satellite data fusion approach. Water Resources Research. 53, 5298–5319, doi:10.1002/2017WR020700. 2017.

Zhang, X., Wang, J., Gao, F., Liu, Y., Schaaf, C., Friedl, M., Yu, Y., Jayavelu, S., Gray, J., Liu, L., Yan, D. and Henebry, G. Exploration of scaling effects on coarse resolution land surface phenology. Remote Sensing of Environment. 190:318-330. doi: 10.1016/j.rse.2017.01.001. 2017.

Gao, F., Anderson, M., Zhang, X., Yang, Z., Alfieri, J., Kustas, B., Mueller, R., Johnson, D. and Prueger, J. Mapping crop progress at field scales using Landsat and MODIS imagery. Remote Sensing of Environment. 188:9-25. doi: 10.1016/j.rse.2016.11.004. 2017.

Gervais, N., Buyantuev, A., and Gao, F.  Modeling the effects of the urban built-up environment on plant phenology using fused satellite data. Remote Sensing. doi: 10.3390/rs9010099. 2017.

Houborg, R., Mccabe, M. and Gao, F. A Spatio-Temporal Enhancement Method for medium resolution LAI(STEM-LAI). International Journal of Applied Earth Observation and Geoinformation. 47:15-29. doi: 10.1016/j.jag.2015.11.013. 2016.

Yang, G., Weng, Q., Pu, R., Gao, F., Sun, C., Li H., Zhao, C. Evaluation of ESTARFM based algorithm for generating land surface temperature products by fusing ASTER and MODIS data during the HiWATER-MUSOEXE. Remote Sensing. 8(1). doi: 10.3390/rs8010075. 2016.

Semmens, K., Anderson, M., Kustas, W., Gao, F., Alfieri, J., Mckee, L., Prueger, J. H., Hain, C., Cammalleri, C., Yang, Y., Xia, T., Sanchez, L., Alsina, M. and Velez, M. Monitoring daily evapotranspiration over two California vineyards using Landsat 8 in a multi-sensor data fusion approach. Remote Sensing of Environment. 185:155-170. doi: 10.1016/j.rse.2015.10.025. 2016.

Sheng, Y., Song, C., Wang, J., Lyons, E., Knox, B., Cox, J. and Gao, F. Representative lake water extent mapping at continental scales using multi-temporal Landsat-8 imagery. Remote Sensing of Environment. 185:129-141. doi: 10.1016/j.rse.2015.12.041. 2016. 

Zhu, X., Helmer, E., Gao, F., Liu, D., Chen, J. and Lefsky, M. A flexible spatiotemporal method for fusing satellite images with different resolutions. Remote Sensing of Environment. 172:165-177. doi: 10.1016/j.rse.2015.11.016. 2016.

Xia, T., Kustas, W. P., Anderson, M. C., Alfieri, J. G., Gao, F., McKee, L., Prueger, J. H., Geli, H. M. E., Neale, C. M. U., Sanchez, L., Mar Alsina, M., and Wang, Z. Mapping evapotranspiration with high resolution aircraft imagery over vineyards using one and two source modeling schemes, Hydrology and Earth System Sciences Discuss. 20:1523-1545. doi: 10.5194/hess-20-1523-2016, 2016.

Gao, F., Hilker, T., Zhu, X., Anderson, M. A., Masek, J., Wang, P. and Yang, Y. Fusing Landsat and MODIS data for vegetation monitoring, IEEE Geoscience and Remote Sensing Magazine. 3(3): 47-60. doi: 10.1109/MGRS.2015.2434351. 2015.

Houborg, R., McCabe, M., Cescatti, A., Gao, F., Schull, M.A. and Gitelson, A. Joint leaf chlorophyll and leaf area index retrieval from Landsat data using a regularized model inversion system. Remote Sensing of Environment. 159:203-221. doi: 10.1016/j.rse.2014.12.008. 2015.

Anderson, M. C., Zolin, C., Hain, C., Semmens, K., Yilmaz, M. T. and Gao, F. Comparison of satellite-derived LAI and precipitation anomalies over Brazil with a thermal infrared-based Evaporative Stress Index for 2003-2013. Journal of Hydrology. 526:287-302. doi: 10.1016/j.jhydrol.2015.01.005. 2015.

Gao, F., He, T., Wang, Z., Ghimire, B., Shuai, Y., Masek, J., Schaaf, C. and Williams, C. Multi-scale climatological albedo look-up maps derived from MODIS BRDF/albedo products. Journal of Applied Remote Sensing. doi: 10.1117/1.JRS.8.083532. 2014.

Gao, F., He, T., Masek, J., Shuai, Y. Schaaf, C. and Wang, Z. Angular effects and correction for medium resolution sensors to support crop monitoring. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 7(11):4480-4489. doi: 10.1109/JSTARS.2014.2343592. 2014.

Weng, Q., Fu, P. and Gao, F. Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data. Remote Sensing of Environment. 145:55-67. doi:10.1016/j.rse.2014.02.003. 2014

Ghimire, B., Williams, C., Masek, J., Gao, F., Wang,Z., Schaaf, C. and He, T. Global albedo change and radiative cooling from anthropogenic land-cover change, 1700 to 2005 based on MODIS, land-use harmonization and radiative kernels. Geophysical Research Letters. 41(24):9087–9096. doi:10.1002/2014GL061671. 2014.

Shuai, Y., Masek, J., Gao, F., Schaaf, C. and Tao, H. An approach for the long-term 30-m land surface snow-free albedo retrieval from historic Landsat surface reflectance and MODIS-based a priori anisotropy knowledge. Remote Sensing of Environment. 152:467-479. doi:10.1016/j.rse.2014.07.009. 2014.

Zhang, Q., Cheng, Y., Lyapustin, A., Wang, Y., Gao, F., Suyker, A., Verman, S. and Middleton, E.M. Estimation of crop gross primary production (GPP): fAPAR_chl versus MOD15A2 FPAR. Remote Sensing of Environment. 153:1-6. doi:10.1016/j.rse.2014.07.012. 2014.

Roy, D., Wulder, M., Loveland, T., Woodcock, C., Allen, R.G., Anderson, M.C., Helder, D., Irons, J., Johnson, D., Kennedy, R., Scambos, T., Schott, J., Sheng, Y., Vermote, Eric, Belward, A., Bindschadler, R., Cohen, Warren, Gao, F., Hipple, J., Hostert, P., Huntington, J., Justice, C., Kilic, A., Kovalskyy, V. and Lee, Z. Landsat-8: science and product vision for terrestrial global change research. Remote Sensing of Environment. 145:154-172. doi:10.1016/j.rse.2014.02.001. 2014.

Wang, P., Gao, F. and Masek, J. Operational data fusion framework for building frequent Landsat-like images in cloudy regions. IEEE Transactions on Geoscience and Remote Sensing. 52(11):7353-7365. doi: 10.1109/TGRS.2014.2311445. 2014.

Liang, L., Schwartz, M., Wang, Z., Gao, F., Schaaf, C., Tan, B., Morisette, J. and Zhang, X. A cross comparison of spatiotemporally enhanced springtime phenological measurements from satellites and ground in a northern U.S. mixed forest. IEEE Transactions on Geoscience and Remote Sensing. 52(12): 7513-7526. doi: 10.1109/TGRS.2014.2313558. 2014.

Cammalleri, C., Anderson, M. C., Gao, F., Hain, C. R. and Kustas, W. P. Mapping daily evapotranspiration at field scales over rainfed and irrigated agricultural areas using remote sensing data fusion. Agricultural and Forest Meteorology. 186:1-11. doi: 10.1016/j.agrformet.2013.11.001. 2014.

Gao, F., Anderson, M.C., Kustas, W.P. and Houborg, R. Retrieving leaf area index from Landsat using MODIS LAI products and field measurements. IEEE Geoscience and Remote Sensing Letters. 11:773-777, doi: 10.1109/LGRS.2013.2278782. 2013.

He, T., Liang, S., Yu, Y., Gao, F. and Liu, Q. Greenland surface albedo changes in July 1981-2012 from satellite observations. Environmental Research Letters, doi:10.1088/1748-9326/8/4/044043. 2013.

Cammalleri, C., Anderson, M. C., Gao, F., Hain, C. R. and Kustas W. P.  A data fusion approach for mapping daily evapotranspiration at field scale. Water Resources Research. 49:4672-4686, doi:10.1002/wrcr.20349. 2013.

Roman, M., Gatebe, C., Shuai, Y., Wang, Z., Gao, F., Masek, J., He, T., Liang, S. and Schaaf, C. Use of in situ and airborne multiangle data to assess MODIS- and Landsat-based estimates of directional reflectance and albedo. IEEE Transactions on Geoscience and Remote Sensing. 51(3), 1393-1404, doi: 10.1109/TGRS.2013.2243457. 2013.

Tan, B., Masek, J., Wolfe, R., Gao, F., Huang, C., Vermote, E., Sexton, J. and Ederer, G. Improved forest change detection with terrain illumination corrected Landsat images. Remote Sensing of Environment. 136:469-483, doi: 10.1016/j.rse.2013.05.013. 2013.

Feng, M., Sexton, J., Huang, C., Masek, J., Vermote, E., Gao, F., Narasimhan, R., Channan, S., Wolfe, R. and Townshend, J. Global surface reflectance products from Landsat: Assessment using coincident MODIS observations. Remote Sensing of Environment. 134:276-293, doi:10.1016/j.rse.2013.02.031. 2013.

Townshend, J., Masek, J., Huang, C., Vermote, E., Gao, F., Channan, S., Sexton, J., Feng, M. Narasimhan, R., Kim, D., Song, K., Song, D., Song, X., Noojipady, P., Tan, B., Hansen, M., Li, M. and Wolfe, R. Global characterization and monitoring of forest cover using Landsat data: opportunities and challenges. International Journal of Digital Earth. 5(5):373-397, doi:10.1080/17538947.2012.713190. 2012.

Gao, F., Anderson, M. C., Kustas, W. P. and Wang, Y. Simple method for retrieving leaf area index from Landsat using MODIS LAI products as reference. Journal of Applied Remote Sensing. 6, 063554. doi:10.1117/1.JRS.6.063554. 2012.

Gao, F., Kustas, W.P. and Anderson M.C. A Data Mining Approach for Sharpening Satellite Thermal Imagery over Land. Remote Sensing. 4(11):3287-3319, doi:10.3390/rs4113287. 2012.

Anderson, M. C., Kustas, W. P., Alfieri, J. G., Gao, F., Hain, C., Prueger, J. H., Evett, S. R., Colaizzi, P. D., Howell, T. A. and Chaves, J. L. Mapping daily evapotranspiration at Landsat spatial scales during the BEAREX'08 field campaign. Advances in Water Resources, 50:162-177, doi:10.1016/j.advwatres.2012.06.005. 2012.

Walker, J., Beurs, K. M., Wynne, R. H., and Gao, F. Evaluation of Landsat and MODIS data fusion products for analysis of dryland forest phenology, Remote Sensing of Environment. 117:381-393, doi:10.1016/j.rse.2011.10.014. 2012.

Zhu, X., Gao, F., Liu, D. and Chen, J. A modified neighborhood similar pixel interpolator approach for removing thick clouds in Landsat images. IEEE Geoscience and Remote Sensing Letters. 9(3):521-525, doi: 10.1109/LGRS.2011.2173290. 2012

Gao, F., Colstoun, E., Ma, R., Weng, Q., Masek, J., Chen, J., Pan, Y. and Song, C. Mapping impervious surface expansion using medium resolution satellite image time series: a case study in Yangtze River Delta, China. International Journal of Remote Sensing. 33(24):7609-7628, doi:10.1080/01431161.2012.700424. 2012.

Ganguly, S., Nemani, R., Zhang, G., Hashimoto, H., Milesi, C., Michaelis, A., Wang, W., Votava, P., Samanta, A., Melton, F., Dungan, J., Vermote, E., Gao, F., Knyazikhin, Y. and Myneni, R. Generating global Leaf Area Index from Landsat: Algorithm formulation and demonstration. Remote Sensing of Environment. 122:185-202, doi:10.1016/j.rse.2011.10.032. 2012.

Liu, S., Ding, W., Gao F., and Stepinski T. F. Adaptive selective learning for automatic identification of sub-kilometer craters. Neurocomputing, 92:78-87, doi:10.1016/j.neucom.2011.11.023. 2012.

Shuai, Y., Masek, J. G., Gao, F. and Schaaf, C. B. An algorithm for the retrieval of 30-m snow-free albedo from Landsat surface reflectance and MODIS BRDF. Remote Sensing of Environment. 115(9):2204-2216.  doi:10.1016/j.rse.2011.04.019. 2011.

Chen, J., Zhu, X., Vogelmann, J. E., Gao, F. and Jin, S. A simple and effective method for filling gaps in Landsat ETM+ SLC-off images. Remote Sensing of Environment. 115(4): 1053-1064.  doi:10.1016/j.rse.2010.12.010. 2011.

Zhu, X., Chen, J., Gao, F., Chen, X. and Masek, J. An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions, Remote Sensing of Environment. 114: 2610-2623. doi:10.1016/j.rse.2010.05.032. 2010.

Tan, B., Morisette, J. T., Wolfe, R. E., Gao, F., Ederer, G. A., Nightingale, J. and Pedelty, J. A. An Enhanced TIMESAT algorithm for estimating vegetation phenology metrics from MODIS data. IEEE Journal of Selected Topics in Earth Observations and Remote Sensing. doi:10.1109/JSTARS.2010.2075916. 2010.

Anderson, M., Kustas, W. P., Norman, J. M., Hain, C. R., Mecikalski, J. R., Schultz, L., Gonzalez-Dugo, M. P., Cammalleri, C., d’Urso, G., Pimstein, A. and Gao, F. Mapping daily evapotranspiration at field to global scales using geostationary and polar orbiting satellite imagery. Hydrology and Earth System Sciences Discussion. 7:1-34, doi:10.5194/hessd-7-1-2010. 2010.

Gao, F., Masek, J., Wolfe, R. and Huang, C. Building consistent medium resolution satellite data set using moderate resolution imaging spectroradiometer products as reference, Journal of Applied Remote Sensing. 4, 043526. doi:10.1117/1.3430002. 2010.

Román M., Schaaf, C., Lewis, P., Gao, F., Anderson, G. P., Privette, J., Strahler, A. H., Woodcock, C. and Barnsley, M. Assessing the coupling between surface albedo derived from MODIS and the fraction of diffuse skylight over spatially-characterized landscapes. Remote Sensing of Environment. 114:738-760. doi: 10.1016/j.rse.2009.11.014. 2010.

Huang, C., Goward, S. N., Masek, J. G., Gao, F., Vermote, E. F., Thomas, N., Schleeweis, K., Kennedy, R. E., Zhu, Z., Eidenshink, J. C. and Townshend, J. R. G. Development of time series stacks of Landsat images for reconstructing forest disturbance history. International Journal of Digital Earth. doi:10.1080/17538940902801614. 2009.

Gao, F., Masek, J. and Wolfe, R. An automated registration and orthorectification package for Landsat and Landsat-like data processing, Journal of Applied Remote Sensing. doi:10.1117/1.3104620. 2009.

Hilker, T., Wulder, M. A., Coops, N. C., Seitz, N., White, J. C., Gao, F., Masek, J. G. and Stenhouse, G. Generation of dense time series synthetic Landsat data through data blending with MODIS using a spatial and temporal adaptive reflectance fusion model. Remote Sensing of Environment.  113(9):1988-1999. doi:10.1016/j.rse.2009.05.011. 2009.

Hilker, T., Wulder, M. A., Coops, N. C., Linke, J., McDermid, G., Masek, J. G., Gao, F. and White, J. C. A new data fusion model for high spatial- and temporal- resolution mapping of forest disturbance based on Landsat and MODIS. Remote Sensing of Environment. 113:1613-1627. doi:10.1016/j.rse.2009.03.007. 2009.

Nightingale, J., Morisette, J., Wolfe, R., Tan, B., Gao, F., Ederer, G., Collatz, J. and Turner, D. Temporally smoothed and gap-filled MODIS land products for carbon modeling: application of the FPAR product. International Journal of Remote Sensing Research Letter. 30 (4):1083-1090. doi: 10.1080/01431160802398064. 2008.

Roy D. P., Ju, J., Lewis, P., Choate, M. J., Schaaf, C., Gao, F., Hansen, M. and Lindquist, E. Multi-temporal MODIS-Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data. Remote Sensing of Environment. 112:3112-3130. doi: 10.1016/j.rse.2008.03.009. 2008.

Gao, F., Morisette, J. T., Wolfe, R. E., Ederer, G., Pedelty, J., Masuoka, Ed., Myneni, R., Tan B. and Nightingale, J. An algorithm to produce temporally and spatially continuous MODIS LAI time series, IEEE Geoscience and Remote Sensing Letters. 5 (1):60-64. doi: 10.1109/LGRS.2007.907971. 2008.

Gao, F., Masek, J., Schwaller M. and Hall, F. On the blending of the Landsat and MODIS surface reflectance: predict daily Landsat surface reflectance. IEEE Transactions on Geoscience and Remote Sensing. 44 (8): 2207-2218. doi: 10.1109/TGRS.2006.872081. 2006.

Tsvetsinskaya, E. A., Schaaf, C. B., Gao, F., Strahler, A. H. and Dickinson, R. E. Spatial and temporal variability in Moderate Resolution Imaging Spectroradiometer-derived surface albedo over global arid regions. J. Geophys. Res. 111 (D20106). doi:10.1029/2005JD006772. 2006.

Salomon, J. G., Schaaf, C. B., Strahler, A. H., Gao, F. and Jin, Y. Validation of the MODIS bidirectional reflectance distribution function and albedo retrievals using combined observations from the Aqua and Terra platforms. IEEE Transactions on Geoscience and Remote Sensing. 44 (6): 1555-1565. doi: 10.1109/TGRS.2006.871564. 2006.

Masek, J. G., Vermote, E. F., Saleous, N. E., Wolfe, R., Hall, F. G., Huemmrich, F., Gao, F., Kutler, J. T. and Lim, T. K. A Landsat surface reflectance data set for North America. 1990-2000, IEEE Geoscience and Remote Sensing Letters. 3 (1):69-72. doi: 10.1109/LGRS.2005.857030. 2006.

Gao, F., Schaaf, C. B., Strahler, A. H., Roesch, A., Lucht, W. and Dickinson, R. MODIS bidirectional reflectance distribution function and albedo climate modeling grid products and the variability of albedo for major global vegetation types. Journal of Geophysical Research. 110 (D01104). doi:10.1029/2004JD005190. 2005.

Moody, E. G., King, M. D., Platnick, S. P., Schaaf, C. B. and Gao, F. Spatially complete global spectral surface albedos: value-added datasets derived from Terra MODIS land products. IEEE Transactions on Geoscience and Remote Sensing. 43 (1):144-158. doi:10.1109/TGRS.2004.838359. 2005.

Stroeve, J., Box, J. E., Gao, F., Liang, S., Nolin A. and Schaaf, C. B. Accuracy assessment of the MODIS 16-day albedo product for snow: comparisons with Greenland in situ measurements. Remote Sensing of Environment. 94:46-60. doi: doi:10.1016/j.rse.2004.09.001. 2005.

Liang, X.-Z., Xu, M., Gao, W., Kunkel, K., Slusser, J., Dai, Y., Min, Q., Houser, P. R., Rodell, M., Schaaf C. B. and Gao, F. Development of land surface albedo parameterization based on Moderate Resolution Imaging Spectroradiometer (MODIS) data. Journal of Geophysical Research. 110 (D11107). doi:10.1029/2004JD005579. 2005.

Roesch, A., Schaaf C. B. and Gao, F. Use of Moderate-Resolution Imaging Spectroradiometer bidirectional reflectance distribution function products to enhance simulated surface albedos. Journal of Geophysical Research. 109 (D12). doi:10.1029/2004JD004552. 2004.

Tian, Y., Dickinson, R. E., Zhou, L., Myneni, R. B., Friedl, M., Schaaf, C. B., Carroll, M. and Gao, F. Land boundary conditions from MODIS data and consequences for the albedo of a climate model. Geophysical Research Letters. 31. doi:10.1029/2003GL019104. 2004.

Wang, Z., Zeng, X., Barlage, M., Dickinson, R. E., Gao, F. and Schaaf, C. B. Using MODIS BRDF/albedo data to evaluate global model land surface albedo. Journal of Hydrometeorology. 5:3-14. doi: 10.1175/1525-7541. 2004.

Gao, F., Schaaf, C. B., Li, X., Jin, Y. and Strahler, A. H. Detecting vegetation structure using a kernel-based BRDF model. Remote Sensing of Environment. 86:198-205. doi: 10.1016/S0034-4257(03)00100-7. 2003.

Zhou, L., Dickinson, R. E., Tian, Y., Zeng, X., Dai, Y., Yang, Z., Schaaf, C. B., Gao, F., Jin, Y., Strahler, A., Myneni, R. B., Yu, H., Wu, W. and Shaikh, M. Comparison of seasonal and spatial variations of albedo from MODIS and common land model. Journal of Geophysical Research. 108 (D15). doi:10.1029/2002JD003326. 2003.

Oleson, K. W., Bonan, G. B., Schaaf, C. B., Gao, F., Jin, Y. and Strahler, A. H. Assessment of global climate model land surface albedo using MODIS data. Geophysical Research Letters. 30 (8). doi:10.1029/2002GL016749. 2003.

Zhang, X., Friedl, M. A., Schaaf, C. B., Strahler, A. H., Hodges, J. C. F., Gao, F. and Reed, B. C. Monitoring vegetation phenology using MODIS. Remote Sensing of Environment. 84 (3):471-475. doi: 10.1016/S0034-4257(02)00135-9. 2003.

Jin, Y., Schaaf, C. B., Gao, F., Li, X., Strahler, A. H., Lucht, W. and Liang, S. Consistency of MODIS surface bidirectional reflectance distribution function and albedo retrievals: 1. Algorithm performance. Journal of Geophysical Research. 108 (D5). doi:10.1029/2002JD002803. 2003.

Jin, Y., Schaaf, C. B., Woodcock, C. E., Gao, F., Li, X., Strahler, A. H., Lucht, W. and Liang, S. Consistency of MODIS surface bidirectional reflectance distribution function and albedo retrievals: 2. Validation. Journal of Geophysical Research. 108 (D5). doi:10.1029/2002JD002804. 2003.

Gao, F., Jin, Y., Li, X., Schaaf, C. B. and Strahler, A. H. Bidirectional NDVI and atmospherically resistant BRDF inversion for vegetation canopy, IEEE Transactions on Geoscience and Remote Sensing. 40 (6):1269-1278. doi: 10.1109/TGRS.2002.800241. 2002.

Jin, Y., Gao, F., Schaaf, C. B., Li, X., Strahler, A. H., Bruegge, C. and Martonchik, J. Improving MODIS surface BRDF/Albedo retrieval with MISR multiangle observations. IEEE Transactions on Geoscience and Remote Sensing. 40 (7): 1593-1604. doi: 10.1109/TGRS.2002.801145. 2002.

Schaaf, B. C., Gao, F., Strahler, A. H., Lucht, W., Li, X. and Tsang T. First operational BRDF, albedo and nadir reflectance products from MODIS. Remote Sensing of Environment. 83:135-148. doi: 10.1016/S0034-4257(02)00091-3.2002.

Friedl, M. A., McIver, D. K., Hodges, J. C. F., Zhang, X., Muchoney, D., Strahler, A. H., Woodcock, C. E., Gopal, S., Schnieder, A., Cooper, A., Baccini, A., Gao, F. and Schaaf, C. B. Global land cover from MODIS: Algorithms and early results, Remote Sensing of Environment, 83:135-148. doi: 10.1016/S0034-4257(02)00078-0. 2002.

Jin, Y., Schaaf, C. B., Gao, F., Li, X., Strahler, A. H., Zeng, X. and Dickinson, R. E. How does snow impact the albedo of vegetated land surfaces as analyzed with MODIS data?. Geophysical Research Letters. 29 (10). doi:10.1029/2001GL014132. 2002.

Tsvetsinskaya, E., Schaaf, C. B., Gao, F., Strahler, A. H., Dickinson, R. E., Zeng, X. and Lucht, W. Relating MODIS derived surface albedo to soils and landforms over Northern Africa and the Arabian peninsula. Geophysical Research Letters. 29 (9). doi:10.1029/2001GL014096. 2002.

Gao, F., Schaaf, C. B., Strahler, A. H. and Lucht, W.  Using a multi-kernel least variance approach to retrieve and evaluate albedo from limited bi-directional measurements, Remote Sensing of Environment. 76:57-66. doi: 10.1016/S0034-4257(00)00192-9. 2001.

Li, X., Gao, F., Wang J. and Strahler, A. H. A priori knowledge accumulation and its application to constrain inversion of kernel-driven linear BRDF models. Journal of Geophysical Research. 106 (D11):11,925-11,936. doi: 10.1029/2000JD900639. 2001.

Gao, F., Li, X., Strahler, A. H. and Schaaf, C. B. Comparison and validation of the new Li-Transit kernel, Remote Sensing Reviews, 19:205-224. doi: 10.1080/02757250009532419. 2000.

Li, X., Gao, F., Wang, J. and Strahler, A. H. Estimation of the parameter error propagation in inversion based on BRDF observations at single sun position. Science in China (Series E), 43(supp):9-16. doi: 10.1007/BF02916573. 2000.

Friedl, M. A., Muchoney, D., McIver, D., Gao, F., Hodges, J. F. C. and Strahler, A. H. Characterization of North American land cover from NOAA-AVHRR data using the EOS MODIS land cover classification algorithm. Geophysical Research Letters. 27 (7):977-980. doi:10.1029/1999GL011010. 2000.

Kimes, D., Knjazikhin, Y., Privette, J., Abuelgasim, A. and Gao, F. Inversion methods for physically based models. Remote Sensing Review. 18 (2-4):381-439. doi: 10.1080/02757250009532396. 2000. 

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