Project Funding: 2013 - 2021

NRA: 2013 NASA: Terra and Aqua: Algorithms--Existing Data Products   

Funded by NASA

Abstract:
Albedo, nadir reflectances, and measures of reflectance anisotropy are produced as standard MODIS (MCD43) products for the global and regional climate, biogeochemical, and vegetation monitoring communities. Directional reflectances from both the Terra and Aqua platforms are used to determine gridded 500m surface Bidirectional Reflectance Distribution Functions (BRDF). These in turn are used to generate Nadir BRDF adjusted reflectance (NBAR) and measures of intrinsic surface Albedo. This product also serves as the underlying data layers for the MODIS Land Cover, MODIS Phenology, and MODIS Cloud Properties products. The Collection V006 reprocessing of the entire archive (2000 to present) will be well underway at the start of this proposed research effort. Due to the extensive enhancements that were put in place for the V006 reprocessing (daily processing, increased observations at high latitudes, dynamic backup databases, improved quality flags and accuracy assessments), extensive evaluation is needed to quantify the improvements and establish the new product uncertainties. Special emphasis will be placed on evalating the products in difficult and rapidly changing conditions such as ephemeral snowfall, spring melt, natural and anthropogenic disturbances, harvest, timbering, and grazing, and coastal dynamics. New global 30arc second gap filled data sets (for both snow free and snow covered regimes) using these improved V006 results for the entire archive are required for the modeling communities. In addition to evaluation of the standard products, the popular Direct Readout codes (used primarily to produce high quality consistent nadir vegetation indices) also need to be updated. This modest proposal aims to proceed with these product evaluations and perform limited validation with readily available tower network albedometer data, as well as generate improved gapfilled and direct broadcast products and explore the increased capabilities possible with the daily high quality V006 results. As this project will cover the last Terra data acquisitions and thus the waning of the MODIS era, it is important to fully quantify and document this important climate quality albedo, nadir reflectance and reflectance anisotropy data record.

Publications:

Campagnolo, M. L., Sun, Q., Liu, Y., Schaaf, C., Wang, Z., Roman, M. O. 2016. Estimating the effective spatial resolution of the operational BRDF, albedo, and nadir reflectance products from MODIS and VIIRS. Remote Sensing of Environment. 175, 52-64. DOI: 10.1016/j.rse.2015.12.033

Hill, M. J., Zhou, Q., Sun, Q., Schaaf, C. B., Palace, M. 2017. Relationships between vegetation indices, fractional cover retrievals and the structure and composition of Brazilian Cerrado natural vegetation. International Journal of Remote Sensing. 38(3), 874-905. DOI: 10.1080/01431161.2016.1271959

Jiao, T., Williams, C. A., Ghimire, B., Masek, J., Gao, F., Schaaf, C. 2017. Global climate forcing from albedo change caused by large-scale deforestation and reforestation: quantification and attribution of geographic variation. Climatic Change. 142(3-4), 463-476. DOI: 10.1007/s10584-017-1962-8

Jiao, Z., Zhang, X., Breon, F., Dong, Y., Schaaf, C. B., Roman, M., Wang, Z., Cui, L., Yin, S., Ding, A., Wang, J. 2018. The influence of spatial resolution on the angular variation patterns of optical reflectance as retrieved from MODIS and POLDER measurements. Remote Sensing of Environment. 215, 371-385. DOI: 10.1016/j.rse.2018.06.025

Kim, Y., Kimball, J. S., Du, J., Schaaf, C. L. B., Kirchner, P. B. 2018. Quantifying the effects of freeze-thaw transitions and snowpack melt on land surface albedo and energy exchange over Alaska and Western Canada. Environmental Research Letters. 13(7), 075009. DOI: 10.1088/1748-9326/aacf72

Klosterman, S., Melaas, E., Wang, J. A., Martinez, A., Frederick, S., O'Keefe, J., Orwig, D. A., Wang, Z., Sun, Q., Schaaf, C., Friedl, M., Richardson, A. D. 2018. Fine-scale perspectives on landscape phenology from unmanned aerial vehicle (UAV) photography. Agricultural and Forest Meteorology. 248, 397-407. DOI: 10.1016/j.agrformet.2017.10.015

Liu, Y., Hill, M. J., Zhang, X., Wang, Z., Richardson, A. D., Hufkens, K., Filippa, G., Baldocchi, D. D., Ma, S., Verfaillie, J., Schaaf, C. B. 2017. Using data from Landsat, MODIS, VIIRS and PhenoCams to monitor the phenology of California oak/grass savanna and open grassland across spatial scales. Agricultural and Forest Meteorology. 237-238, 311-325. DOI: 10.1016/j.agrformet.2017.02.026

Liu, Y., Wang, Z., Sun, Q., Erb, A. M., Li, Z., Schaaf, C. B., Zhang, X., Roman, M. O., Scott, R. L., Zhang, Q., Novick, K. A., Syndonia Bret-Harte, M., Petroy, S., SanClements, M. 2017. Evaluation of the VIIRS BRDF, Albedo and NBAR products suite and an assessment of continuity with the long term MODIS record. Remote Sensing of Environment. 201, 256-274. DOI: 10.1016/j.rse.2017.09.020

Moustafa, S. E., Rennermalm, A. K., Roman, M. O., Wang, Z., Schaaf, C. B., Smith, L. C., Koenig, L. S., Erb, A. 2017. Evaluation of satellite remote sensing albedo retrievals over the ablation area of the southwestern Greenland ice sheet. Remote Sensing of Environment. 198, 115-125. DOI: 10.1016/j.rse.2017.05.030

Pahlevan, N., Schott, J. R., Franz, B. A., Zibordi, G., Markham, B., Bailey, S., Schaaf, C. B., Ondrusek, M., Greb, S., Strait, C. M. 2017. Landsat 8 remote sensing reflectance (Rrs) products: Evaluations, intercomparisons, and enhancements. Remote Sensing of Environment. 190, 289-301. DOI: 10.1016/j.rse.2016.12.030

Paynter, I., Genest, D., Peri, F., Schaaf, C. 2018. Bounding uncertainty in volumetric geometric models for terrestrial lidar observations of ecosystems. Interface Focus. 8(2), 20170043. DOI: 10.1098/rsfs.2017.0043

Paynter, I., Genest, D., Saenz, E., Peri, F., Boucher, P., Li, Z., Strahler, A., Schaaf, C. 2017. Classifying ecosystems with metaproperties from terrestrial laser scanner data. Methods in Ecology and Evolution. 9(2), 210-222. DOI: 10.1111/2041-210X.12854

Riihela, A., Manninen, T., Key, J., Sun, Q., Sutterlin, M., Lattanzio, A., Schaaf, C. 2018. A Multisensor Approach to Global Retrievals of Land Surface Albedo. Remote Sensing. 10(6), 848. DOI: 10.3390/rs10060848

Sun, Q., Wang, Z., Li, Z., Erb, A., Schaaf, C. B. 2017. Evaluation of the global MODIS 30 arc-second spatially and temporally complete snow-free land surface albedo and reflectance anisotropy dataset. International Journal of Applied Earth Observation and Geoinformation. 58, 36-49. DOI: 10.1016/j.jag.2017.01.011

Trlica, A., Hutyra, L. R., Schaaf, C. L., Erb, A., Wang, J. A. 2017. Albedo, Land Cover, and Daytime Surface Temperature Variation Across an Urbanized Landscape. Earth's Future. 5(11), 1084-1101. DOI: 10.1002/2017EF000569

Wang, Z., Erb, A. M., Schaaf, C. B., Sun, Q., Liu, Y., Yang, Y., Shuai, Y., Casey, K. A., Roman, M. O. 2016. Early spring post-fire snow albedo dynamics in high latitude boreal forests using Landsat-8 OLI data. Remote Sensing of Environment. 185, 71-83. DOI: 10.1016/j.rse.2016.02.059

Wang, Z., Schaaf, C. B., Sun, Q., Kim, J., Erb, A. M., Gao, F., Roman, M. O., Yang, Y., Petroy, S., Taylor, J. R., Masek, J. G., Morisette, J. T., Zhang, X., Papuga, S. A. 2017. Monitoring land surface albedo and vegetation dynamics using high spatial and temporal resolution synthetic time series from Landsat and the MODIS BRDF/NBAR/albedo product. International Journal of Applied Earth Observation and Geoinformation. 59, 104-117. DOI: 10.1016/j.jag.2017.03.008

More details may be found in the following project profile(s):

• NASA Terrestrial Ecology (TE) Project Profile