Retrieval of Green-up Onset Date From MODIS Derived NDVI in Grasslands of Inner Mongolia

Abstract

In this paper, moderate resolution imaging spectroradiometer (MODIS) derived normalized difference vegetation index (NDVI) was used to retrieve the onset date of spring phenology in grasslands of Inner Mongolia from 2002 to 2017. To validate the application of MODIS derived data, long-term retrieval and investigation on its reliability was performed. The correlation between MODIS derived green-up onset date and ground observations from 2002 to 2012 were given medium to high strength (R 2 : 0.58-0.81). Besides, the relationship between seasonal climate variables, including winter and spring precipitation and temperature, and MODIS derived green-up onset date from 2002 to 2017 were established. Overall, linear regression analysis shows that there are 8-12 days RMSE for climate variables explaining green-up onset date shifting. This satellite method might have a poor performance in sparse and snow-cover grasslands but is fitted well in medium to dense vegetation and snow-free grasslands. These suggest that MODIS estimated spring phenology has the potential to be applied in grass resources utilization in Inner Mongolia.INDEX TERMS Grassland, moderate resolution imaging spectroradiometer (MODIS), normalized difference vegetation index (NDVI), spring phenology.

Diurnal and Seasonal Dynamics of Solar‐Induced Chlorophyll Fluorescence, Vegetation Indices, and Gross Primary Productivity in the Boreal Forest

“…Remotely sensed metrics are a powerful tool for understanding and scaling plant responses to the environment across space and time (Frankenberg & Berry, 2017; Gamon et al., 1997, 2016; Jeong et al., 2017; Tucker, 1979). Vegetation indices (VIs) that measure greeness, such as the Normalized Difference Vegetation Index (NDVI) or the Near‐Infrared Reflectance from vegetation (NIRv) have proven to be effective at tracking plant productivity in ecosystems where chlorophyll content and carbon uptake are closely correlated, such as deciduous forests and grasslands (Badgley et al., 2017; Tucker, 1979; H. Yang et al., 2017; Wang et al., 2019). These measures often fail to predict productivity in evergreen systems, such as the boreal forest, where changes to photosynthesis often occur without significant changes in canopy structure or chlorophyll content (Jeong et al., 2017; Magney, Bowling, et al., 2019; Pierrat et al., 2021; Sims, Rahman, et al., 2006; Springer et al., 2017; Walther et al., 2016).…”

Tower‐Based Remote Sensing Reveals Mechanisms Behind a Two‐phased Spring Transition in a Mixed‐Species Boreal Forest

“…), have been used to successfully track vegetation cover and productivity across biomes (Badgley et al., 2019; Tucker, 1979). NDVI provides an estimate of canopy chlorophyll content in a pixel and therefore tracks the spring onset well in deciduous forests and grasslands (Wang et al., 2019; Yang et al., 2017). However, NDVI is sensitive to non‐vegetated surfaces such as clouds, snow, and water, and saturates at high leaf area index (LAI), all of which present challenges for decoupling vegetation productivity from other artifacts that are particularly present in the boreal forest (Gamon et al., 2013).…”

Seasonal Response of the NDVI to the SPEI at Different Time Scales in Yinshanbeilu, Inner Mongolia, China

“…As the temperature starts to rise, the plants return to the greening and nodulation stage, and thus, the NDVI gradually increases. In summer, the solar altitude angle reaches its highest angle in the year, and solar radiation and air temperature also reach their highest values; thus, the NDVI reaches its highest value in a year in summer [53][54][55]. In autumn, when the solar radiation decreases, the temperature decreases, as the solar altitude angle decreases; thus, the vegetation cover decreases in autumn.…”