Assessment of the SeaWinds scatterometer for vegetation phenology monitoring across China

Abstract

Vegetation phenology tracks plants' lifecycle events, revealing the response of vegetation to global climate changes. Changes in vegetation phenology also influence fluxes of carbon, water, and energy at local and global scales. In this study, we analysed a time series of Ku-band radar backscatter measurements from the SeaWinds scatterometer on board the Quick Scatterometer (QuickSCAT) to examine canopy phenology from 2003 to 2005 across China. The thaw season SeaWinds backscatter and Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) time series were significantly correlated in 20 of the 22 sites (p < 0.05). A weighted curve-fitting method was applied to detect the start of season and end of season from both data sets. The SeaWinds scatterometer generally detected earlier timing of spring leaf-out and later fall senescence than the MODIS LAI data sets. The SeaWinds backscatter detected phenological metrics in 75.85% of mainland China. Similar spatial patterns were observed from the SeaWinds backscatter and MODIS LAI time series; however, the average standard deviation of the scatterometer-detected metrics was lower than that of MODIS LAI products. Overall, the phenological information from the SeaWinds scatterometer could provide an alternative view on the growth dynamics of land-surface vegetation.

Remote sensing of temperate and boreal forest phenology: A review of progress, challenges and opportunities in the intercomparison of in-situ and satellite phenological metrics

“…Dostalova et al (2016), Ruetschi et al (2018)), although to significantly less of an extent than with optical data (at least for forest phenology detection). Microwave data has the advantage of being less (or not) affected by factors commonly hampering the data acquisition of high quality optical data: darkness, atmospheric composition, clouds, and off-nadir viewing angles (but depending on surface roughness) (Ryan et al, 2012;Lu et al, 2013a). Nevertheless, the two data sources are sensitive to different characteristics of the land surface: microwave remote sensing data are likely to respond to vegetation structure and moisture content (including soil moisture) (Jones et al, 2011;Ryan et al, 2012).…”

Mapping Asian Cropping Intensity With MODIS

“…Here, the data do not suggest more than two cycles, but an additional harvest may have occurred during the long period of missing data from May to July (a time period corresponding to the "Aus" cropping season). Thus, future improvements in mapping cropping intensity in Asia will only be realized with methodologies which reduce the missing data problem through incorporation of ancillary and alternative remote sensing data sources such as moderate resolution geostationary satellites (e.g., SEVIRI [28], [29]), microwave sensors (e.g., SeaWinds [30]), and subnational-scale inventory data. In a multisensor fusion context, higher resolution multispectral sensors may provide additional and complementary information, even without benefit of high temporal resolution (e.g., pan-sharpening coarser resolution data).…”

Energy crop mapping with enhanced TM/MODIS time series in the BCAP agricultural lands

“…Due to the limited layers (26 scenes) in a yearly period, the TM-like NDVI time series in this study cannot be as smooth as those from the 8-day, 46-layer MODIS data in our past studies (e.g., those demonstrated in Wang et al 2011). Theoretical simulations such as the Asymmetric Gaussian model (Lu et al 2013) are also problematic. Therefore, the enhanced TM/MODIS time series could not simply adopt the regular phenology-assisted classification approaches.…”