Zhang, Xiaoyang: South Dakota State University (Project Lead)
Project Funding:
2014 - 2017
NRA: 2013 NASA: Suomi National Polar-orbiting Partnership Science Team
Funded by NASA
Abstract:
This proposal responds to ROSES2013 A.29 (NPP Science Team And Science Investigator-Led Processing Systems for Earth System Data Records from Suomi NPP), focusing on the development of science quality standard data products using NPP VIIRS that will enable continuity of a key standard Earth System Data Record (ESDR) from EOS data. Specifically, we propose to develop and implement an operational ESDR from VIIRS data focused on land surface phenology (land cover dynamics). This product is part of the MODIS land data product suite, but is not currently produced as a NOAA JPSS Environmental Data Record (EDR). Growing evidence has recently emerged that climate change-induced shifts and extremes in phenology have substantial impacts on agriculture, ecosystem function, biodiversity, and terrestrial carbon budgets at multiple scales. Timing of leaf-on and leaf-off periods also affects land surface albedo, exerting strong control on surface radiation budgets and the partitioning of net radiation between latent and sensible heat fluxes, impacting atmospheric boundary layer processes and affecting weather prediction. At multiple levels, there is critical need to produce accurate and timely global land surface phenology data sets from VIIRS.
The activity described in this proposal builds off more than a decade of previous work in this area by the PI and his co-investigators. Over the past twelve years, we developed and refined the only operational global land surface phenology product (MODIS MCD12Q2; a.k.a. land cover dynamics), which provides metrics characterizing seasonal dynamics in global vegetated land surfaces during the MODIS era. In addition, the PI has worked extensively with AVHRR data to produce land surface phenology data sets spanning the 30-year AVHRR record. Together, these products characterize the nature, magnitude, and timing of changes in phenology over past three decades. The work described in this proposal addresses two urgent needs: (1) to provide continuity with these data sets using VIIRS; and (2) to provide operational data streams for use in numerical weather prediction, carbon monitoring, agricultural planning, and disaster management.
The primary objective of this proposal is, therefore, to develop and implement an algorithm to produce continuous and well-calibrated time series of global land surface phenology metrics from VIIRS that provides continuity with the MCD12Q2 product from MODIS. The resulting datasets will be suitable for characterizing and understanding

interannual-to-decadal scale changes in ecosystem responses to a variable and changing climate. Specific tasks we propose include: (1) to develop and implement a land surface phenology product with a spatial resolution of 375-m from NPP VIIRS that provides continuity with MODIS phenology products; (2) to develop a phenology product with a spatial resolution of 0.05 degree from the VIIRS surface reflectance CMG (Climate Modeling Grid) that extends phenology measurements begun by AVHRR; (3) to conduct inter-comparisons between the VIIRS and MODIS phenology products to ensure continuity of the VIIRS product with the MODIS data record; (4) to evaluate and validate the stability, precision, uncertainty, accuracy, and spatial and temporal continuity of the proposed VIIRS phenology product; and (5) to deliver scientific code, and an associated Algorithm Theoretical Basis Document to the NASA-designed Science Investigator-led Processing Systems (SIPS).
Publications:
Wang, J., Zhang, X. 2017. Impacts of wildfires on interannual trends in land surface phenology: an investigation of the Hayman Fire. Environmental Research Letters. 12(5), 054008. DOI: 10.1088/1748-9326/aa6ad9
Yan, D., Zhang, X., Nagai, S., Yu, Y., Akitsu, T., Nasahara, K. N., Ide, R., Maeda, T. 2019. Evaluating land surface phenology from the Advanced Himawari Imager using observations from MODIS and the Phenological Eyes Network. International Journal of Applied Earth Observation and Geoinformation. 79, 71-83. DOI: 10.1016/j.jag.2019.02.011
Zhang, X., Liu, L., Henebry, G. M. 2019. Impacts of land cover and land use change on long-term trend of land surface phenology: a case study in agricultural ecosystems. Environmental Research Letters. 14(4), 044020. DOI: 10.1088/1748-9326/ab04d2
Zhang, X., Wang, J., Gao, F., Liu, Y., Schaaf, C., Friedl, M., Yu, Y., Jayavelu, S., Gray, J., Liu, L., Yan, D., Henebry, G. M. 2017. 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
Zhang, X., Zhang, Q. 2016. Monitoring interannual variation in global crop yield using long-term AVHRR and MODIS observations. ISPRS Journal of Photogrammetry and Remote Sensing. 114, 191-205. DOI: 10.1016/j.isprsjprs.2016.02.010