## 1 Introduction  
China is an agricultural country with the largest population in the world, which only accounts for 7% of the earth's cropland resources but it needs to feed 22% of the world population [1]. Precise prediction of crop production in China and any other countries with the same situations is very significant [2; 3]. Autumn crop refers to the food crops planted in spring or summer and harvested in autumn (e.g., middle rice, late rice, corn, sorghum, millet, sweet potato, soybeans, and so on), which is important as the main food in China [4]. Therefore, large-scale and accurate estimation of autumn crop production is critical for policy makers and grain marketing agencies in planning for exports and imports [5] and to solve food security issues [6]. Satellite remote sensing has been widely used to manage cropland [7; 8; 9; 10; 11] and estimate crop production [5; 12; 13; 14; 15]. Vegetation indices (VIs) (e.g., normalized difference vegetation index, NDVI; enhanced vegetation index, EVI), chlorophyll content, leaf area index (LAI), and the fraction of absorbed photosynthetically active radiation by vegetation (fAPAR) have been used to estimate the production of corn, rice, and other crops [16; 17; 18; 19]. For example, crop production was forecasted by using VIs that were derived from satellite data and machine learning methods on the Canadian Prairies [5], and by inputting NDVI and daytime land surface temperature (LST) to build a regression tree-based model in America [18]. However, these satellite-derived products that were used in previous studies, such as VIs, are slightly sensitive to the photosynthesis of plant and significantly indicative of vegetation \"greenness\" [20; 21; 22], thereby resulting in low accuracy of crop production forecast. Solar-induced chlorophyll fluorescence (SIF) is essentially an energy escaped from plant chlorophyll during photosynthesis, with the wavelength ranging from 600 to 800 nm [23]. SIF is directly related to the crop photosynthesis and is recognized as a more reliable indicator than the VIs [20; 24; 25]. The Greenhouse Gases Observing Satellite (GOSAT), the Global Ozone Monitoring Experiment-2 (GOME-2), and the Scanning Imaging Absorption Spectrometer for Atmospheric Chatography (SCIAMACHY) are common SIF product sources [26; 27; 28; 29]. Nevertheless, the spatial resolutions of these satellite-derived SIF products are coarse (e.g., GOME-2: 40 \(\times\) 80 km\({}^{2}\); GOSAT: 10 km diameter; SCIAMACHY: 30 \(\times\) 240 km\({}^{2}\)). The spatial resolution of the Orbiting Carbon Observatory-2 (OCO-2) SIF product reached 1.29 \(\times\) 2.25 km\({}^{2}\) with the development of satellite-based measurement of SIF. Recently, a new global \"Orbiting Carbon Observatory-2 (OCO-2)\" SIF dataset (GOSIF) was developed [30] with spatially continuous data. It is possible to widely monitor terrestrial photosynthesis [24; 25; 26; 27; 28; 29; 30] and estimate large-scale crop production by using SIF product. Although the spatial resolution of satellite-derived SIF product is not high enough for precise agriculture, these data are quite useful in monitoring large-scale crop photosynthesis and predicting crop yield over national or global scales [31; 32]. Currently, the studies on estimating autumn crop production while using SIF products remain scarce [33]. As the world's most populous nation with 1.4 billion people, it is particularly important to ensure food security by mainly relying on domestic production. In this study, we examined the correlations between the converted OCO-2 SIF products and autumn crop production statistics at the monthly and seasonal scales during the summer of 2015. Besides, we evaluated the correlations between the converted moderate resolution imaging spectroradiometer (MODIS) VIs (EVI, NDVI) and autumn crop production statistics to compare the performance between SIF and these widely-used VIs.