# Assessment of Grassland Degradation on the Tibetan Plateau Based on Multi-Source Data
## 2 Materials and Methods
### Datasets
#### 2.2.2 Vegetation Index Datasets  
Six vegetation index datasets were used to evaluate grassland degradation on the TP (Table 2). The spatial distribution dataset of monthly normalized difference vegetation index (NDVI) in China is based on continuous time series of SPOT/VEGFATION NDVI satellite remote sensing data ([[http://www.resdc.cn](http://www.resdc.cn)]([http://www.resdc.cn](http://www.resdc.cn)), accessed on 28 January 2021). NDVI has a significant linear correlation with vegetation cover and can be used to estimate grassland cover [39]. The enhanced vegetation index (EVI) minimizes canopy-soil variations and improves sensitivity to dense vegetation conditions. Compared with NDVI, the influence of clouds is reduced and it is more sensitive to vegetation canopy changes [40]. Leaf Area Index (LAI) is defined as half of the total green leaf area per unit of the flat ground area [41]. LAI was significantly correlated with the quality of the ecosystem [42]. Solar-induced chlorophyll fluorescence (SIF) is an indicator of the functional status of actual plant photosynthesis [43]. A global \"OCO-2\" SIF dataset (GOSIF) with a finer spatial resolution was developed for assessing terrestrial photosynthesis and ecosystem function [44]. NPP is  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **MCD12Q1** & **CCI-LC** & **Globeland30** & **CNLUCC** & **CLCD** \\ \hline Spatial & & & & & \\ Resolution (m) & 500 & 300 & 30 & 1000 & 30 \\ \hline \multirow{3}{*}{Sensor} & \multicolumn{4}{c}{MERIS Full and} \\  & & Reduced & Landsat-TM, & Landsat- & \\ Resolution/SPOT & & ETM7, HJ-1A/b/ & TM/ETM+/OLI & TM/ETM+/OLI \\ \hline Classification & Supervised & Unsupervised & Supervised & Supervised & \\ Technique & classification & classification & classification & classification & \\ \hline Classification & IGBP (17 classes) & \begin{tabular}{c} UN-LCC (22 classes) \\ \end{tabular} & \begin{tabular}{c} (10 classes) \\ \end{tabular} & \begin{tabular}{c} (9 classes) \\ \end{tabular} & \begin{tabular}{c} (9 classes) \\ \end{tabular} \\ \hline Period of data & 2001–2019 & 1992–2020 & 2000, 2010, 2020 & 2000, 2005, 2010, 2015, 2018, 2020 & 1985, 1990–2020 \\ \hline \multirow{3}{*}{Institution} & \multicolumn{4}{c}{National} \\  & \begin{tabular}{c} Aeronautics and \\ Space \\ Administration \\ (NASA) \\ \end{tabular} & \begin{tabular}{c} European Space \\ Agency (ESA) \\ \end{tabular} & \begin{tabular}{c} National \\ Geomatics Center \\ of China (NGCC) \\ \end{tabular} & \begin{tabular}{c} Institute of \\ Geographic \\ Sciences and \\ Natural Resources \\ Research \\ \end{tabular} & Wuhan University \\ \hline \multirow{3}{*}{Source} & \multicolumn{4}{c}{https://ladsweb.} \\  & \begin{tabular}{c} modaps.eosdis. \\ nasa.gov/, \\ accessed on 11 \\ September 2020 \\ \end{tabular} & \
\begin{tabular}{c} [[https://www.esa-](https://www.esa-)]([https://www.esa-](https://www.esa-)) \\ landcover-cci. \\ org/, accessed on 28 \\ \end{tabular} &
\begin{tabular}{c} http: \\ \(\!\!a measure of net carbon and a vital parameter for ecological modeling [45]. Gross primary productivity (GPP) is the amount of energy that is produced at a given time by the major energy producers of the ecosystem in a particular area [46]. Since data for 2000-2020 for each vegetation index were not accurately available, only the available data were analyzed.