###### Abstract  
Leaf area index (LAI) is an important parameter in plant ecophysiology; it can be used to quantify foliage directly and as a measure of the photosynthetic active area and, thus, the area subject to transpiration in vegetation. The aim of this paper was to review work on remote sensing methods of estimating LAI across different forest ecosystems, crops and grasslands in terms of remote sensing platforms, sensors and models. To achieve this aim, scholarly articles with the title or keywords \"Leaf Area Index estimation\" or \"LAI estimation\" were searched on Google Scholar and Web of Science with a date range between 2010 and 2020. The study's results revealed that during the last decade, the use of remote sensing to estimate and map LAI increased for crops and natural forests. However, there is still a need for more research concerning commercial forests and grasslands, as the number of studies remains low. Of the 84 studies related to forests, 60 were related to natural forests and 24 were related to commercial forests. In terms of model types, empirical models were most often used for estimating the LAI of forests, followed by physical models. LAI estimation; sensors; models; passive; active; data source LAI products +
Footnote †: journal: Medical  
different advantages and disadvantages. However, indirect methods were shown to offer more advantages than disadvantages, especially when a large area was studied. Remote sensing is one of the commonly used indirect methods of estimating LAI, as remote sensing technology offers a better alternative to estimating and mapping LAI for larger landscapes more efficiently and accurately [11]. Remote sensing also offers a less time-consuming and cost-effective method of estimating LAI [12]. The development of high spatial resolution satellite data enabled researchers around the world to more effectively monitor vegetation at higher accuracy [13]. Remote sensing enables convenient collection of data dating back to several years while providing reliable and accurate estimates of LAI and other biophysical attributes for different vegetation types. Although a number of remote sensing methods of estimating LAI were developed, not a single method can be applied consistently and repeatedly for estimating LAI locally and regionally [14]. The reason for this is variations in biophysical, environmental and topographic traits of vegetation in space and time [15; 16]. LAI can be derived by using empirical, statistical and hybrid methods. Deriving LAI by means of statistical and physical approaches was first carried out on crop canopies in the beginning of the 1970s. One of the earliest studies on estimating LAI using statistical approaches was carried out in 1974 by [17], who used Landsat MSS to derive wheat LAI. In recent years, the number of studies on crop LAI increased, while recent studies on crop LAI were published by [9; 13; 18; 19]. After research on crop LAI estimations using statistical approaches started showing positive results in the 1980s, the number of forest LAI studies increased. With regard to commercial forests and grasslands, there is still a limited number of studies; therefore, this area requires further investigation. During the last decade, the use of remote sensing to estimate and map LAI increased [20]. High spatial and spectral resolution sensors such as QuickBird, IKONOS and WorldView-3 showed great potential in achieving acceptable levels of accuracy in estimating LAI. The availability of high spatial resolution sensors, such as the ones mentioned above, also led researchers to investigate the effects of spatial and spectral resolution in effectively estimating LAI. A number of studies were conducted about satellite remote sensing for forestry planning to show that 30 m spatial resolution provides insignificant results when it comes to forest management planning. Ref. [21] conducted a similar study as the one mentioned above but used a higher spatial resolution satellite sensor.