Driving Climatic Factors at Critical Plant Developmental Stages for Qinghai–Tibet Plateau Alpine Grassland Productivity

Drier August and colder September slow down the delaying trend of leaf senescence in herbaceous plants on the Qinghai-Tibetan Plateau

Plant phenological shifts on the Qinghai-Tibetan Plateau (QTP) have gained considerable attention over the last few decades. However, temporal changes in plant autumn phenology and the main driving factors remain uncertain. Most previous studies used satellite-derived phenological transition dates and climatic statistics during the preseason, which have relatively large uncertainties and may mask some important climate change characteristics at the intra-annual scale, thus affecting exploration of the underlying phenological change causes. This study collected 1685 phenological records at 27 ground stations on the QTP during 1983-2017. Temporal change trends and break points in leaf senescence date (LSD) of 23 herbaceous species were assessed using least squares regression, a meta-analysis procedure, and the Pettitt test. The main drivers and causes were investigated through correlation analysis and contribution calculation based on LSD observations and monthly climatic data. Results showed that, LSD of QTP herbaceous plants was significantly delayed at a rate of 4.45 days/decade during 1983-2017. Break points were concentrated during 1999-2003, with an overall mean in 2001. After 2001, the delay trend in LSD decreased, falling from 5.26 days/decade to 2.54 days/decade. Air temperature and precipitation were the most important climatic factors that showed closer and more extensive correlations with LSD and greater contributions to the inter-annual variations in LSD. August and September were the most critical period during which climatic factors had higher contributions to the LSD shifts. However, August was drier, with precipitation significantly decreasing and temperature increasing, and September was colder after 2001. Therefore, the declining trend in LSD may be attributed to the drier August and colder September. This study has not only provided reliable field evidence on temporal changes in autumn phenology on the QTP, but has also provided valuable insights into autumn phenological modelling and regional carbon cycling in alpine regions.

The Impact of NPV on the Spectral Parameters in the Yellow-Edge, Red-Edge and NIR Shoulder Wavelength Regions in Grasslands

Even though research has shown that the spectral parameters of yellow-edge, red-edge and NIR (near-infrared) shoulder wavelength regions are able to estimate green cover and leaf area index (LAI), a large amount of dead materials in grasslands challenges the accuracy of their estimation using hyperspectral remote sensing. However, the exact impact of dead vegetation cover on these spectral parameters remains unclear. Therefore, we evaluated the influences of dead materials on the spectral parameters in the wavelength regions of yellow-edge, red-edge and NIR shoulder by comparing normalized difference vegetation indices (NDVI) including the common red valley at 670 nm and NDVI using the red valley extracted by a new statistical method. This method, based on the concept of segmented linear regression, was developed to extract the spectral parameters and calculate NDVI automatically from the hyper-spectra. To fully understand the impact of dead cover on the spectral parameters (i.e., consider full coverage combinations of green vegetation, dead materials and bare soil), both in situ measured and simulated hyper-spectra were analyzed. The impact of dead cover on LAI estimation by those spectral parameters and NDVI were also evaluated. The results show that: (i) without considering the influence of bare soil, dead materials decreases the slope of red-edge, the slope of NIR shoulder and NDVI, while dead materials increases the slope of yellow-edge; (ii) the spectral characteristics of red valley disappear when dead cover exceeds 67%; (iii) large amount of dead materials also result in a blue shift of the red-edge position; (iv) accurate extraction of the red valley position enhances LAI estimation and reduces the influences of dead materials using hyperspectral NDVI; (v) the accuracy of LAI estimation using the slope of yellow-edge, the slope of red-edge, red-edge position and NDVI significantly drops when dead cover exceeds 72.3–74.5% (variation among indices).