## 3 Remote Sensing of Ecosystem Health
### Remote Sensing of Vigor
#### 3.1.2 Green Vegetation, NPV, BSC, and Bare Soil Cover  
The fractional cover of green vegetation, NPV, and bare soil can be estimated simultaneously using a spectral unmixing approach (SMA) (e.g., [44; 45; 46]), or using the empirical relationships between each coverand spectral indices (e.g., [47, 48]). Green vegetation cover and bare soil estimation will not be discussed in depth here, as the former has been routinely produced as traditional remotely sensed products from MODIS, AVHRR, and SPOT-VGT _etc._ and the latter can be estimated together with NPV and green vegetation. NPV is a significant component of vegetation productivity in grasslands, savannas, shrublands, and dry woodlands [49] as well as wetlands [50]. BSC is present in semiarid and arid areas worldwide [51]. Both NPV and BSC are ecologically important, yet estimating their abundance using remote sensing appraoches are still very challenging. Thus they are our review focus in this subsection. NPV can be separated from green vegetation in the visible (VIS, 400-700 nm) and near-infrared (NIR, 700-1200 nm) wavelength regions due to the lack of absorption of pigments [49], and from bare soil in shortwave infrared (SWIR, 1100-2500 nm) region due to the unique absorption features of lignin and cellulose of NPV [49, 52]. Efforts have been made to estimate NPV cover using optical remote sensing data, especially hyperspectral images, in croplands (e.g., [47, 53, 54, 55]), grassland [56], savannah [46], and forest ecosystems [44]. However, the estimation of NPV abundance is highly affected by soil mineralogy and soil organic carbon [57, 58], moisture content of both green vegetation and bare soil [52, 57], and the decomposition of NPV [52]. In this regard, radar and LiDAR remote sensing which have an advantage to detect the structure of NPV can be good data sources for NPV estimation. For detailed information on remote sensing of NPV abundance using optical, radar, and LiDAR data, please refer to [59]. The spectral characteristics of BSC have been investigated by many researchers (e.g., [60, 61, 62, 63]) using field measurements. Although an absorption feature at approximately 680 nm has been always observed in BSC samples, there is noticeable difference in the spectra as dominant BSC species change [61, 64]. A recent study found that water absorption feature at about 1450 nm can be used to differentiate BSC from green vegetation and the spectra of the most developed BSCs is characterized by a steeper slope between about 680 and 750 nm [65]. Based on the spectra characteristics, many efforts have been made to detect and map BSC using Landsat MSS, TM, or ETM\({}^{+}\) images [61, 64, 66, 67, 68]. These studies have demonstrated that remote sensing has a great potential to detect and map spatial distribution of BSC on a large spatial extent timely and efficiently [67, 69]. However, the variations in spectra of different BSC communities make the derived spectral indices less universally applicable for mapping BSC cover. The crust index developed for mapping cyanobacteria-dominated BSC [64] is not suitable for lichen-dominated BSC covering large areas of cool and cold deserts [70]. Another BSC index (BSCI) was proposed to discriminate lichen-dominated BSC from land surface of bare sand and dry plant material in a desert [67], however, the usage of BSCI was highly affected by the predetermined lower and upper thresholds of BSCI. Besides the crust indices, continuum removal [71], SMA approaches [72], and a partial least squares regression-linear discriminant analysis [64] were also used for BSC investigation. The conclusions of these studies are not always consistent for different study areas.