# Improving the Retrieval of Crop Canopy Chlorophyll Content Using Vegetation Index Combinations
## 1 Introduction  
Chlorophyll is the main photosynthetic leaf pigment, playing a critical role by converting solar radiation into stored chemical energy [1]. Canopy chlorophyll content (CCC) is calculated based on the leaf area index (LAI) and leaf chlorophyll content (LCC) and expressed per unit leaf area. This measure is useful for monitoring the productivity and growth status of vegetation [2, 3]. Over the past few decades, extensive research has found that CCC is the primary driving force for estimating gross primary productivity (GPP) [1, 4], so its accurate determination is extremely important for agricultural applications. The methods currently used to determine CCC consist of two approaches: (1) A laboratory-based approach, and (2) non-destructive remote sensing technology. The first is highly accurate but also time consuming, resource intensive, and destructive, limiting its large-scale application [5]. The development of remote-sensing technology has enabled the estimation of CCC using satellite data with various temporal and spatial resolutions [6, 7, 8]. The vegetation index (VI) approach is widely used to estimate CCC due to its simplicity, convenience, and high computational efficiency [9; 10; 11]. Retrieving CCC requires a vegetation index that is sensitive to both LCC and the LAI, since both of these influence CCC. A variety of VIs have been published that are based on field measurements and simulated datasets obtained from the radiation transfer model [8; 9; 12]. The MERIS terrestrial chlorophyll index (MTCI) was developed by utilizing red and red-edge position bands and is commonly used to estimate CCC based on both MERIS and other hyperspectral data [13]. Due to the structural characteristics of leaves, the canopy architecture and soil background can significantly affect the optical properties of leaves and canopies [14; 15; 16; 17]. Therefore, issues can arise when estimating CCC using VIs calculated based on canopy reflectance. Several studies have indicated that a strong correlation exists between the MTCI and CCC, but this gradually weakens as the LAI increases [18; 19]. The spectral saturation associated with high LAI values has always been an issue when estimating canopy population parameters and when using VIs to retrieve CCC. Various studies have indicated that vegetation spectra and VIs can become saturated when high LAI levels are observed for different types of vegetation [20; 21]. The vegetation indices sensitive to the LAI are named LAI-related vegetation indices (LAI-VIs) and derived from spectral reflectance at red and near-infrared bands. LAI-VIs also encounter saturation issues when used for estimating LAIs. Some modified LAI-VIs have been developed to reduce the saturation produced by high LAI values [16; 22; 23]. However, little is known about CCC retrieval when in the presence of high LAI values. Additionally, the MTCI is observed to have a weaker relationship with CCC at low coverage [24]; this is because the soil background has a significant impact on the red-edge reflectance when the canopy is sparse or has low coverage [25]. More information related to leaf chlorophyll and canopy structure can be obtained by using a combination of multiple VIs rather than a single VI [26; 27]. A combination of the chlorophyll-related VI (CHL-VI) and LAI-VI has previously been used for estimating LCC and is resistant to LAI variations [28; 29; 30]. The ratio of the transformed chlorophyll absorption in reflectance index (TCARI) to the optimized soil-adjusted vegetation index (OSAVI), called the TCARI/OSAVI, has been used to accurately retrieve crop chlorophyll with hyperspectral airborne imagery [28]. Several other ratio-based VIs have been developed and applied for the ground and satellite-based remote sensing of crop and forest LCC [29; 30]. The combination of the CHL-VI and LAI-VI with multiple regression or cost functions is a radiative transfer model inversion that has also improved LCC estimation [31].