Development and evaluation of a new algorithm for detecting 30m land surface phenology from VIIRS and HLS time series

Authors: ZHANG, X. - South Dakota State University; WANG, J. - South Dakota State University; HENEBRY, G. - South Dakota State University; Gao, Feng

Submitted to: Journal of Photogrammetry and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/9/2020
Publication Date: 1/15/2020
Citation: Zhang, X., Wang, J., Henebry, G., Gao, F.N. 2020. 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. 161:37-51. https://doi.org/10.1016/j.isprsjprs.2020.01.012.
DOI: https://doi.org/10.1016/j.isprsjprs.2020.01.012

Interpretive Summary: Land surface phenology (LSP) provides critical information for investigating forest and crop growth. Although operational 500 m LSP products are available from coarse resolution data observed from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS), an LSP product is needed at the Landsat pixel resolution (30 m) for field scale monitoring. However, the temporal frequency of 30 m satellite data is inadequate for reliable LSP detection. This paper presents a new algorithm to detect LSP at 30 m resolution using routine Harmonized Landsat and Sentinel-2 (HLS) and VIIRS surface reflectance products. The greenup onset detected from the new algorithm agrees well with the standard VIIRS LSP product and observations from the ground. The resulting 30 m LSP map would enable accurate crop growth and condition monitoring at the sub-field scale.

Technical Abstract: Land surface phenology (LSP) provides critical information for investigating vegetation growth and development, studying ecosystem biodiversity, modeling terrestrial carbon and surface energy budgets, detecting land cover and land use change, and monitoring climate change. Although operational 500 m LSP products have been produced from coarse resolution data observed from Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS), an LSP product is also needed at the Landsat scale (30 m) to enhance the environmental monitoring and modeling. However, temporal frequency of 30 m satellite data is always inadequate for reliable LSP detection, despite enrichment by the operational harmonized Landsat and Sentinel-2 (HLS) product. In this study, we propose a new algorithm of LSP detection for the generation of a 30 m LSP product using routinely produced HLS and VIIRS surface reflectance products. Specifically, the new algorithm compares a HLS EVI2 (two-band enhanced vegetation index) time series at a given 30 m pixel with the set of 500 m VIIRS EVI2 time series neighboring the HLS pixel and selects the most similar temporal shape of VIIRS time series even though the amplitude and/or phase between HLS and VIIRS EVI2 time series may be mismatched. The shape of the selected VIIRS EVI2 time series is then used to match to the given HLS EVI2 time series to generate a synthetic HLS-VIIRS time series. The HLS-VIIRS time series is subsequently processed using the hybrid piecewise logistic model to detect the phenological transition dates and to quantify the confidence of LSP detection. This new algorithm is evaluated by implementing 30 m LSP detection in eight HLS tiles in the northeastern (forests), central (croplands), and western (shrublands) United States. Evaluation finds that the new-algorithm-detected greenup onset (1) agrees well with the standard VIIRS LSP product without bias, (2) closely correlates to PhenoCam observations with a slope close to one, and (3) compares well with both PhenoCam and field species-specific observations with a mean absolute difference of 8 days and a difference less than 10 days in more than 70% of the validation samples. This implementation suggests that the new algorithm could be implemented for regional and global LSP product generation at a 30 m resolution.