## 1 Introduction  
Accurate estimation of crop biophysical and biochemical parameters during crop growing season is important for improving crop field management [1]. Two important indicators of these parameters-leaf area index (LAI) and above ground biomass (AGB)-were used to monitor crop canopy structural development and growth changes and to estimate yield. The reasonable and reliable estimation of LAI and biomass can improve crop fertilizer applications [2], water irrigation [3,4], disease and weed control [5,6], and grain production marketing [7, 8, 9]. LAI and biomass change seasonally under different environmental conditions, and therefore, it is important to timely estimate their values. These parameters are traditionally estimated through destructive, time-consuming _in situ_ methods, which are difficult to conduct when crops cover large regions. Owing to its capacity to obtain information on global and regional scales, remote sensing has become an effective tool for estimating LAI and biomass over large areas. Crop canopy structure mainly affects the spectral reflectance of crop canopy in the near-infrared (NIR) and visible spectrums. Numerous studies have shown a strong correlation between vegetation indices (VIs) and LAI and biomass using different integrations of visible and NIR reflectance [10, 11, 12, 13, 14, 15, 16, 17]. A previous study has shown that normalized difference vegetation index (NDVI) was very sensitive to low LAI values (_i.e._, LAI \(<\) 3) and saturation exists at medium to high LAI values (_i.e._, LAI \(>\) 3) [16]. Similarly, the saturation of NDVI values was shown at medium to high values of fresh biomass (around 2000 g/m\({}^{2}\)) [13]. The simple ratio [16], the modified triangular vegetation index 2 (MTVI2) [18], and the cumulative MTVI2 [19] have shown better sensitivity at medium to high LAI and biomass. Previous results have shown that VIs based on the reflectance of red-edge bands (e.g., the red-edge triangular vegetation index (RTVI) and the modified chlorophyll absorption ratio index (MCARI2)) have great potential for improving estimations of LAI and biomass [13, 18]. Most VIs have mainly been derived from field spectral radiometers [16, 17, 20], airborne spectrographic imagers [18], medium resolution spectrometers [20], and high-resolution spectrometers [21]. However, optical satellite images often have some limitations with respect to VIs because of the saturation problem and the subsequent reduction in estimation accuracy at medium to high LAI and biomass [13, 14, 15, 16, 17, 22]. Compared with optical satellite images, synthetic aperture radars (SARs) have some advantages for monitoring crop growth status at medium to high LAI and biomass owing to the fact that microwave sensors have longer wavelengths, can penetrate crop canopies, and are not influenced by the presence of clouds or haze [23]. However, SAR images are limited by the technique's imaging geometry and radiation mechanism [24]. Several SARs have been launched, such as ALOS-PALSAR (Japan), TerraSAR-X (Germany), Sentinel 1 (European Space Agency), and Radarsat1 and 2 (Canada). Some SARs have a short revisit time and high spatial resolution, which could be beneficial for monitoring crop development and health status [25, 26]. Many studies have estimated LAI and biomass based on SAR images data acquired from either airborne or space-borne platforms [27, 28, 29, 30, 31]. Some studies have shown that SAR backscattering was well correlated with biomass, especially that characterized by medium fractional cover [32, 33, 34]. Since optical and SAR image data respond to crop characteristics differently, their complementary information content can support the estimation of crop conditions [12]. The combination of optical and SAR image data has been used for the estimation of the LAI and biomass of crops and forests, and the results have shown that the estimated values agree well with the actual values [27, 35, 36, 37].