###### Abstract  
Rice serves as the staple food for over 50% of the global population. Remotely-sensed based estimation of the gross primary production (GPP) and evapotranspiration (ET) of rice paddy fields is essential to assess global food security. In this study, we tested the application of a recently proposed remotely-sensed based water-carbon coupled model (PML-V2) in the lower reaches of the Poyang Lake plain, which is one of the nine production bases for crops in China. Evaluation using the eddy covariance measurements showed that, after parameter localization, the model reproduced the seasonal variations of GPP and ET for both the early rice and the late rice. The model performed reasonably well in the validation period because the key parameters (e.g., the quantum efficiency and the stomatal conductance coefficient) exhibited predictable seasonal variations. At the regional scale, the spatial distribution in multi-year GPP of rice (1365 \(\pm\) 326 gCm\({}^{-2}\)year\({}^{-1}\)) can be explained by the vegetation cover fraction (R\({}^{2}\) > 0.9); in comparison, the multi-year ET (1003 \(\pm\) 65 mm/year) exhibits smaller spatial variations due to the high evaporation rate of the saturated soil surface of paddy fields. The water use efficiency of rice in this region varies around 1.35 gC/kgH\({}_{2}\)O with a standard deviation of 0.30. Our study shows that GPP and ET of rice can be estimated by remote sensing models without detailed crop management information, which is usually unavailable at regional scales. gross primary production; evapotranspiration; rice paddy field; remote sensing model 25 August 202 With the development of remote sensing techniques, numerous crop models [5] and statistical models [6] have assimilated the remotely sensed vegetation information to estimate regional crop yields [7]. For example, [PERSON] et al. [8] estimated the spatial and temporal variations in the rice productions in Japan with a diagnostic biosphere model and the Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) product. The point-scale evaluation showed that, after introducing the harvest and soil oxidation-reduction processes, the root mean square errors were reduced to 27.31 gCm\({}^{-2}\)month\({}^{-1}\) and 36.33 gCm\({}^{-2}\)month\({}^{-1}\) for gross primary production (GPP) and net ecosystem production (NEP), respectively [8]. Other diagnostic models, such as the light use efficiency (LUE) model [9] and the light-response curve model [10] that were proposed to estimate ecosystem GPP without parameterizing the crop management information and the process at the vegetation-soil interface, were also widely used in the regional estimation of crop yields, for example, for the maize ecosystem [11], the wheat-maize rotating system [12], and rice paddy fields [13; 14]. Irrigated croplands consume most of the precipitation by evapotranspiration (ET) [15] and therefore have strong feedback to the terrestrial hydrology and the near-surface atmosphere [16; 17]. Compared to other crops and natural vegetation, the ET of the paddy field is generally much higher [18]. For example, [PERSON] et al. [19] reported that the ratio between ET and net radiation increases from 0.6 to 0.8 when a marshland was turned into a rice paddy field in the northeast region of China. Similarly, ET of rice takes up 63-67% of the total ET during the growing season for a rice-wheat rotation site in Nanjing, Eastern China [20]. Therefore, accurate estimation of regional ET for rice is essential for water management and sustainable agricultural development. The ET of rice can be estimated using the Priestley-Taylor (PT) method [21] or the Penman-Monteith model [22; 23]. For example, [PERSON] et al. [24] examined the sensitivity of rice paddy ET to varying patterns of warming using a modified PT model in the East Asian monsoon regions. [PERSON] et al.