Rice Yield Estimation Based on Vegetation Index and Florescence Spectral Information from UAV Hyperspectral Remote Sensing  
[PERSON]  
1 Institute of Applied Remote Sensing &Information Technology, Zhejiang University, Hangzhou 310058, China; [EMAIL_ADDRESS]  
[PERSON]  
1 Institute of Applied Remote Sensing &Information Technology, Zhejiang University, Hangzhou 310058, China; [EMAIL_ADDRESS]  
[PERSON]  
1 Institute of Applied Remote Sensing &Information Technology, Zhejiang University, Hangzhou 310058, China; [EMAIL_ADDRESS]  
[PERSON]  
1 Institute of Applied Remote Sensing &Information Technology, Zhejiang University, Hangzhou 310058, China; [EMAIL_ADDRESS]  
[PERSON]  
1 Institute of Applied Remote Sensing &Information Technology, Zhejiang University, Hangzhou 310058, China; [EMAIL_ADDRESS]  
Footnote 1: email: {xie_  
main food crops and the primary staple food for about half the people of the world [4]. The production is related to the national economy and people's livelihoods, so timely and non-destructive assessments of rice yield information is of great significance to the development of grain procurement, storage, allocation, and foreign trade [5]. Since remote sensing technology has the advantages of a large-area and dynamic monitoring, it is widely used for regional and global crop yield estimation, including rice [6; 7; 8]. There are three types of crop yield estimation methods based on remote sensing data; (1) common empirical models based on vegetation indices (VIs) [9; 10; 11; 12; 13], (2) crop yield estimation by mechanistic models combined with remote sensing data [14; 15; 16; 17; 18], and (3) semi-empirical production estimation models based on Gross Primary Production (GPP) or Net primary production (NPP) [19; 20]. Among them, empirical models based on VIs are the most widely used method for crop yield estimation due to their simplicity and ease of use [21; 22]. This method generally uses the vegetation index at a single growth stage or the combined vegetation indices from multiple growth stages to build univariate or multivariate models for yield estimation [23]. For those statistical rice yield estimation models that are constructed by spectral indices from multiple growth stages such as the filtering stage, jointing stage, booting stage, heading stage, filling stage and maturity stage [24], spectral information at the flowering stage is rarely used, because vegetation indices directly developed by spectral information at the flowering stage is interfered with by white florets, which may result in the error for yield estimates. However, the spectrum at the flowering stage contains the information of rice floret number per unit area, which is the most important factor relating to yield [25; 26; 27; 28], making the spectral information at the flowering stage crucial for rice yield estimation. To date, the spectral features at the flowering stage has not been considered in rice yield estimation, although some researchers have observed that the presence of flowers can cause canopy spectral changes in certain bands [29; 30]. At present, research on remote sensing monitoring of flowering can be categorized into three types: (1) the extraction of floret amount information (flower coverage ratio and flower number) basing on remote sensing data. [PERSON] et al. [31] estimated the floret number of leskelaole rape by flower coverage proportion. [PERSON] et al. [32] extracted the proportion of flower area and estimated the florets amount of acacia using unmanned aerial vehicle (UAVs) images. (2) The effects of flowering information on estimation of such vegetation parameters as biomass, coverage and LAI. [PERSON] et al. [33] found that it was difficult to estimate biomass using NDVI and EVI that contain flowering information, but they could estimate biomass more accurately after the removal of flowering information. [PERSON] et al.