## 4 Discussion  
This literature review investigated forms of LAI estimations using remote sensing technology for forests, crop and grasslands. The paper further unpacked trends in the use of different sensor systems, models, number of journal articles published and LAIproducts. The review found that there were considerable advancements in terms of satellite system technology and models used for LAI and this led to improvements in LAI index estimation accuracy. Satellite systems and data improved in terms of spatial resolution, data accessibility, spectral resolution and satellite technology diversity. Satellites such as Pleiades Neo offer a spatial resolution of 30 cm, which is the best spatial resolution available today. New commercial and free satellite with higher spectral resolution entered the market, giving consumers a wide range of remote sensing technologies to choose from. The introduction of red edge band in most remote sensing technologies provided means for scientists to better understand vegetation attributes. Accurate LAI estimates of forests, crops and grassland are essential for improving the management and enhancement of the health of these vegetation types. There was great progress in terms of satellite data availability and access since the launch of Landsat in 1972. The development of cloud satellite data access and processing platforms such as Google Earth Engine and Sentinel Hub enabled data scientists and researchers to access and process large data sets efficiently and quickly. Furthermore, these platforms were found to have built-in data modelling and analysis tools. Easy access to data meant the increase in the number of studies using free satellite data such as Landsat, Sentinel and lower spatial resolution moderate resolution imaging spectroradiometer (MODIS). Landsat is characterized by a spatial resolution of 30 m, and is the only existing satellite offering a multidecadal data for assessing, monitoring and mapping land changes. While Sentinel offers higher spatial resolution of 10 m and 13 spectral bands, it does not offer multidecadal data. MODIS is categorized as a coarse spatial resolution sensor as it offers a spatial resolution of 250 m to 1 km. Although MODIS provides multidecadal and consistent data, it cannot be used effectively to model ecosystems at local and regional scales [57]. The defining characteristics of commercial satellite include their high spatial and spectral resolution. WorldView-3 is one of the commonly used commercial satellite which was launched in 2014, making it the sixth Digital Globe's satellite in orbit (Satellite Imaging Corporation Houston, 2017). It joined satellites such as Ikonos which was launched in 1999, QuickBird in 2001, WorldView-1 in 2007, GeoEye in 2008 and WorldView-2 in 2009. Although commercial satellite data offer high spatial resolution and a better spectral and spatial resolution, they are still relatively costly. The most commonly used commercial satellite data in this review were SPOT, QuickBird, IKONOS and WorldView (Figure 5). The popularity of the aforementioned commercial satellite is because of high spatial resolution and higher revisit times. The results also showed an increasing trend in the fusion of data from different sensors and data with different spatial, spectral and temporal resolution. Of the 168 forest, crop and grassland LAI estimation papers reviewed, the use of passive remote sensing in these studies was almost three times higher when compared to active remote sensing studies. In terms of active remote sensors, LiDAR was more widely used for forests than crops and grassland. This may be because discreet LiDAR data tended to have a poor penetration ability in short vegetation. These findings are consistent with the previous review paper conducted by [12], which reviewed research trends and future directions of LAI estimations using remote sensing from 1990 to 2020. Radar systems such as synthetic aperture radar (SAR) were mostly used for crops compared to forests and grasslands.