## 4 Discussion
Generally, crops are more dynamic throughout the growing season; therefore, radar systems that function under varying cloud conditions are useful for temporal or time-series crop analysis and can be used even during seasons of high cloud cover. Although grasslands are homogenous and less dynamic throughout the growing season, they usually cover larger areas, which poses a challenge when obtaining and processing data. The number of studies using unmanned aerial vehicles (AUV) increased since 2010, which enabled a quick turnaround time and real-time crop monitoring. This increase was especially notable in LAI estimation for crops. AUVs are equipped with micro sensors which can be used to obtain crop physiological and biochemical information at ultralow altitudes and low cost. Although satellite sensors can obtain crop image over a large area, it can be difficult to obtain accurate biophysical information of crop canopy from a low spatial and temporal resolution satellite sensor. Remote sensing studies on LAI for forests, crop and grasslands were conducted across a wide range of scales. Although the majority of studies were conducted at local and sub country scales (110), there were also studies conducted at global (7), continental (19) and country scales (29). There were also 14 studies that were conducted at variable scales. Analyzing the data of studies conducted at continental or global levels is challenging as it requires high computer processing power. Empirical, physical and hybrid model types have different advantages and limitations. Empirical models, as shown in the results section, were most commonly used models and were effective as they can be used across different data types to determine the relationship between LAI and spectral reflectance data. However, these methods utilized large statistical data inputs and can only be applied at certain locations, as they are highly dependent on vegetation types and canopy structural change [58]. Another limitation of empirical models is saturation problems. Although some studies showed that this problem can be mitigated by incorporating modified indices, this problem cannot be completely avoided when using optical imagery. According to [59], machine-learning algorithms offered a better way to analyze remote sensing data, as they do not make assumptions about variable distributions. However, it is important to understand the limitations of empirical methods such as machine-learning algorithms (e.g., multi-collinearity) before applying them. Physical models, unlike empirical models, provide solutions developed without strong reliance on field data [47]. The great advantage of physical models is their applicability at wide range locations and scales, where they can also be applied to a wide range of vegetation types and canopy structures. The results showed that there was an increase in the application of all model types. However, in terms of physical models, PROSAIL was the most commonly used radiative transfer model. A challenge associated with physical models is that they are complicated, as they require many data inputs and are time-consuming to process. Hybrid models are a combination of empirical models including machine-learning algorithms, regression methods and physical models. Ref. [47] used the combined version of PROSPECT and SAIL known as PROSAIL to retrieve the LAI of saltmarsh from Sentinel-2 and RapidEye satellite data. This study demonstrated that RTM was one of the best methods to estimate LAI from remotely sensed data. However, the adaptability of this model is highly dependent on the type of remote sensing data, ecosystem and the texture of the vegetation canopy. As mentioned in the results section, the current trend for forest, crop and grassland LAI estimation is the exploration of newly developed algorithms based on machine-learning methods, especially for big data calculation. However, there are still challenges associated with the use of hybrid models, which include the problem of instability and model overfitting.