A widely used index is the NDVI, defined as the normalized ratio between NIR and red bands (Tucker 1979) and scaled to fall in the range of +1 to –1.<sup>4</sup> The NDVI is closely correlated with the leaf area index, a biophysical measure of the vegetation canopy (Gao et al. 2000), and has also been used to estimate carbon stocks. Photosynthetically absorbed radiation (FAPAR) is a function of the NDVI (Myneni et al. 2002), prompting wide use of the index in global models of gross or net primary biomass production (Justice et al _._ 1998) and for more localized estimates of soil erosion (Tadesse et al. 2017). To overcome the NDVI’s limitations, especially a tendency to <mark>asymptotically saturate that limits its usefulness for dense canopies or bare soils,</mark> a soil-adjusted vegetation index or SAVI (Huete 1988) and a modified (Qi et al _._ 1994) or generalized SAVI have been suggested (Gilabert et al. 2002).<sup>5</sup> In addition to the measures of photosynthetic activity that can be derived from the measures mentioned, thermal infrared bands can help to monitor evapotranspiration (Semmens et al. 2016) and plant or soil water content. The LSWI, based on the normalized ratio between NIR and SWIR bands (Xiao et al _._ 2002) also ranges from 1 to –1, with positive values for green vegetation and negative ones for dried (brown) leaves. It usefully complements the NDVI (Sakamoto et al. 2007), especially as an indicator of droughts or floods (Bajgain et al. 2015). Comparing seasonal cycles of NDVI and LSWI for different types of vegetation (deciduous broadleaf or evergreen needle forest, temperate grassland, cropland, and tropical moist forest) suggests that, except for cropland, values of these indices differ considerably from each other, with the LSWI finding more pronounced intra-seasonal variation than the NDVI (Xiao et al _._ 2009).

Though the potential for using remotely sensed imagery to measure vegetative cover has been available for some time, recent advances in access to data and computing platforms have greatly enhanced the scope for using these in routine program evaluations. Since the late 1970s optical sensors on satellites in the Landsat family have been providing data at reasonably high levels of temporal and spatial resolution (Markham and Helder 2012; Schott et al _._ 2012), a fact exploited for monitoring changes in global forest cover over rather long periods (Hansen et al. 2013), and in ways that can be updated in near-real time given the satellite’s 16day revisit time. Free public access to the entire Landsat archive (Wulder et al _._ 2012) and the availability of powerful free computing platforms greatly reduce the related costs. With GEE allowing access to vast amounts of consistently preprocessed imagery in the cloud, it is possible to generate time series of land cover classifications at pixel level (Gómez, White, and Wulder 2016) without time- and hardware-intensive processing. Resulting data can be used for a range of policy purposes, such as analysis of changes in forest

> 4 Areas with values close to -1 are water bodies; those around zero (-0.1 to 0.1) correspond to barren areas of road, sand, or snow; values 0.2 to 0.5 represent crops, shrubs, and grassland; and values above 0.5 correspond to dense vegetation.

> 5<sup>A related measure is the EVI, which is calculated from red, NIR, and blue bands (Huete et al. 1997), is linearly correlated to the green leaf area</sup> index in crop fields (Boegh et al. 2002), is very sensitive to variation in canopy conditions (Huete et al. 2002), and is  less affected by atmospheric aerosol contamination (Xiao et al. 2003).

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