## 1 Introduction  
Crop yield prediction is essential in a variety of socioeconomic aspects, such as agricultural management [1; 2], economic planning and commodities forecasting [3; 4], as well as food security monitoring [5; 6]. However, climate change and extreme weather events rising in intensity and frequency are causing significant variability of crop production [7; 8; 9], imposing large challenges on yield prediction, especially at regional scales. Two categories of approaches, namely process and statistical-based modeling, have been extensively used to predict crop yield. Process-based models [10; 11; 12; 13] simulate crop growth mechanically. They could be used to predict crop yield and to quantify the roles of individual factors in determining crop yield. However, those models require various inputs such as cultivar and soil parameters, which are not always available or with significant uncertainties for many places around the world [14; 15]. On the other hand, statistical models, though their predicting performances are often data-dependent, have the advantages of simplicity and relatively high predictive ability if providing adequate training data [16; 17]. The widely-employed statistical methods usually predict crop yield by establishing simple linear or nonlinear relationships of yield with predictors, such as temperature, precipitation, and vapor pressure deficit (VPD) [18; 19; 20; 21]. In recent years, one data-driven approach--machine learning (ML)--has been increasingly adopted for crop yield estimation and shows its strong predictive power [22; 23; 24; 25; 26]. As one of the state-of-the-art ML techniques, deep neural network (DNN) has been successfully applied in many studies, such as speech processing [27; 28], image recognition [29; 30], and drug activity prediction [31]. Recently, the applicability of this method in crop yield prediction has been explored in some studies [25; 32; 33]. The DNN method has the merit of allowing much complex nonlinear interactions between predictors and multiple levels of abstract representation of relationships between input variables and output prediction (i.e., crop yield in this case), which is hard for conventional models to develop [34]. The prediction of crop yield generally relies on two sources of data (i.e., climate and satellite-derived), or their combination [35]. Integrating climate data into crop yield prediction has a long history [36; 37; 38]) since the growth and yield of crops are significantly affected by climate conditions [39; 40; 41; 42]. However, other factors, such as management, soil, fertilizer applications also play essential roles in controlling crop growth and yield. Therefore, other data are required for better prediction of crop yields. In recent decades, remote sensing data, covering visible, near-infrared (NIR), thermal, and microwave bands, has greatly facilitated direct crop monitoring [35]. Many greenness-based vegetation indices (VI), such as Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), contain useful information on green biomass [43; 44] and have been widely employed in the crop yield forecasting [45; 46; 47]. Recently, a novel satellite-derived product, i.e., sun-induced chlorophyll fluorescence (SIF), became available, providing a promising method to monitor photosynthetic activities directly from space [48; 49]. During the light reaction of the photosynthesis process, SIF is emitted as a weak electromagnetic signal from chlorophyll \(\alpha\) pigments after light absorption [50]. The SIF emission from vegetation is in a narrow spectral window ranging from 660 to 850 nm and has two peaks centered at 685 and 740 nm [51; 52]. Unlike active measurements of fluorescence that have been available for decades, remote sensing of passive SIF signals was only enabled recent years by retrieving SIF based on in-filling of narrow Fraunhofer lines [53; 54; 55].