## 1 Introduction  
Gross primary productivity (GPP), defined as the total photosynthetic carbon uptake by terrestrial plants, is the first phase of atmospheric CO\({}_{2}\) reaching the biosphere [1, 2]. Currently, farmland accounts for \(\sim\)12% of the Earth's land surface [3], and around 15% of global CO\({}_{2}\) fixation is contributed by crop GPP [4]. Therefore, accurately quantifying crop GPP can provide valuable information on the ecosystem's carbon cycle, agricultural applications and climate change [5]. For assessing GPP in crops, eddy covariance (EC) systems, satellite-driven methods, and process-based models are frequently employed. Among these, the EC technique allows the direct and continuous monitoring of land-atmosphere net ecosystem exchange (NEE) [6]. The gathered NEE data are routinely partitioned into GPP and ecosystem respiration [7]. However, these EC measurements only represent the fluxes at the scale of the tower footprint, with an along-wind extent ranging between hundreds of meters and several kilometers [8]. Although over 600 EC flux towers are currently in operation around the world, their point-based measurements are insufficient to cover continuous regions in space [9]. To deal with the problem of spatial discontinuity in the EC technique, satellite remote sensing, light use efficiency models and process-oriented land surface models are adopted [6]. However, remote sensing-based GPP may not fully guarantee the accuracy of data. For instance, [PERSON] et al. [2] assessed the latest Moderate Resolution Imaging Spectroradiometer (MODIS) GPP product (MOD17A2H) in different biome types compared with global EC flux-estimated GPP and found that MOD17A2H GPP performed poorly at both annual (coefficient of determination: 0.62) and 8-day scales (coefficient of determination: 0.52). Thus, a more advanced calibration model is required for large-scale applications [10]. Process-based land surface models (e.g., Community Land Model [11] and Simple Biosphere Model 2 [12]) have been designed for cropland GPP, but are subject to complicated scientific assumptions and model parameters [13]. GPP in crops is a complicated non-linear function due to the spatial heterogeneity of vegetation and soil properties, and the temporal heterogeneity of the environmental factors (meteorological conditions and agricultural managements) [9; 11]. Currently, data-driven machine learning algorithms constitute another popular method for predicting GPP, because they can elucidate precisely nonlinear processes of CO\({}_{2}\) exchanges in agroecosystems [14]. Although, in principle, they are black-box models, machine learning methods (e.g., model tree ensembles [15], support vector machines [13], and neural network models [16; 17], random forest models [18; 19; 20; 21]) have good performance for multi-ecosystem GPP estimations. For instance, [PERSON] et al. [22] quantified the 8-day GPP and the mean European annual carbon budget across ecosystems (e.g., forest, grassland, cropland and wetland) by using a random forest (RF) algorithm, remote sensing and EC data. Recently, an RF model was also adopted to upscale the EC-based GPP to regional scales in an arid and semi-arid area in Northwestern China [23]. Previous studies have evaluated the latest MOD17A2H GPP product across various ecosystems (e.g., forests and grasslands) with global EC data [1; 2]. However, the validation has rarely been performed for double-cropping agriculture, especially in rice-wheat-rotation cropland, which is the most extensive land cover type in the northern Yangtze River Delta (NYRD) region, China [24]. Furthermore, the existing studies on the GPP changes in the NYRD mainly focused on the temporal characteristics of carbon exchanges [25; 26; 27], leaving a knowledge gap with respect to the upscaling of GPP and the calibration of the MOD17A2H GPP product.