## 3 Remote Sensing of Ecosystem Health
### Remote Sensing of Vigor
#### 3.1.3 Vegetation Biochemical Properties  
Vegetation biochemical properties, such as chlorophyll (Ch), nitrogen (N), and phosphorous (P) are highly related to ecosystem functioning, thus are important indicators of ecosystem health assessment [74]. Ch controls photosynthesis, and is thus an indicator of plant health and GPP [75]. P as an indicator of nutrient quality of plant and plant growth rate [74] can also be an indicator of plant health. N being an important component of Ch is also strongly associated with plant health and GPP. Remote sensing of vegetation biochemical properties have been successfully conducted at a leaf level for a few decades using narrow band spectral indices derived from ground and space hyperspectral data. For a list of hyperspectral sensors please refer to [59]. Efforts have been made to scale up biochemical content to canopy level using remote sensing data in crops and forests [76] and semiarid mixed grassland [77]. Methods used for scaling up biochemical contents from leaf to canopy level were summarized by [76]. Yet it is still challenging for biochemical content estimation at a landscape level, despite promising [76,78].  
Due to the importance, Ch has drawn a particular attention of both ecologists and remote sensing scientists. Ch has been estimated using red edge position (REP) based on the finding that an increase in chlorophyll content will be reflected on the spectra as the wavelength edge of red absorption feature moves to even longer wavelengths [79]. However, REP cannot accurately estimate high Ch content [79,80]. In addition, spectral indices developed for chlorophyll estimation were summarized and compared by [81,82], and red-edge based vegetation index has demonstrated more potential for chlorophyll content estimation in a semiarid mixed grassland ecosystem of Canada [77]. The estimation of Ch mainly uses continuous wavelength ranges or narrow bands spectral indices. However, space sensed data with fine spectral resolution including the Medium Resolution Imaging Spectrometer (MERIS) and the upcoming new satellite sentinel-2 have also demonstrated great potential for Ch estimation [83,84]. Terrestrial chlorophyll index (MICI) was developed based on band 8, 9, and 10 of the MERIS, and global composites of MICI at 300 m spatial resolution, as a unique terrestrial chlorophyll product have been produced under the support of the European Space Agency (ESA) since 2006 [85].  
There are a number of studies on remote sensing of vegetation N at leaf and canopy level with high accuracy [74], while only limited research on P estimation and the accuracy of estimation is lower than N estimation [86]. The commonly used approach for estimating these vegetation biochemical properties are empirical methods based on _in situ_ measurements of biochemical content and remotely sensed data. The most widely used wavelengths for N and P estimation are the NIR and SWIR regions [86]. The estimation of these biochemical properties thus is highly influenced by canopy water content. To minimize water absorption effects and other influences from atmosphere, soil, and redundancy of hyperspectral data, spectral indices, first derivative, continuum removal, and log-transformed spectra have been used to boost the absorption features of vegetation biochemical properties. Water-removed spectra constructed based on a nonlinear combination of a dry-matter and a leaf water spectrum [87] increased the accuracy of N and P estimation of grass in savanna, compared to first derivative and continuum removal spectra [86]. The commonly used empirical models for predicting biochemical properties based on biochemical spectra features are simple linear regression, partial least-squares regression (PLSR), and stepwise multiple linear regression (SMLR). The spectral indices used for N estimation can be found in [88] but mainly for crops, while no VI was specifically designed for P estimation yet [74].