## 1 Introduction  
Soil pH, often referred to as the \"master soil variable\", has an enormous influence on soil biogeochemical processes, thereby affecting nutrient cycling, crop growth and yield, and environmental health [1]. For example, soil biodiversity-function relationships and the mobility and bioavailability of toxic elements in soils and crops are all closely related to soil pH [2,3]. Characterized by a decrease in soil pH, soil acidification has been a major global issue [4, 5, 6]. Soils with a pH of less than 5.5 could cause stunted plant growth due to factors such as aluminum toxicity [7,8], while root systems can by damaged in highly acidic soils with a pH of less than 4.5, resulting in heavy crop yield losses [9]. Soil acidification is particularly severe in soils of intensive Chinese agricultural systems, reflected by an average pH decline between 0.13 and 0.80 units in major croplands between 1980s and 2000s [10,11]. Without sufficient mitigation measures, soil pH could decline by about 1 unit with relative yield losses increasing from 4% to 24% during 2010-2050 in China [12]. To support site-specific decision making for alleviating soil acidification and promoting environmental health, detailed and accurate spatial information on soil pH is urgently required. Cropland soil pH mapping is especially important for the sustainability of both food production and agricultural ecosystems. Currently, digital soil mapping (DSM) is the most widely used method, which infers spatial soil information from soil observation and related environmental variables based on quantitative soil-landscape relationships [13,14]. Because cropland soils are actively managed by humans, human interventions must be considered in the modeling process [15]. For Chinese croplands, nitrogen (N) fertilizer use is a key influencing factor of soil pH and has led to severe soil acidification [11,12,16]. Directly mapping N fertilization is rather challenging due to the intricate nature of farmer activities [17]. Given that crops' nutritional needs vary considerably and that crops may be under different fertilization regimes, maps on cropping systems somehow indicate the spatial and temporal variability of N fertilization [18]. It was shown that Chinese vegetable production involves more intensive N fertilization than cereal crops [19]. Orchards have also been severely overfertilized in China, and farmland-to-orchard conversion causes soil pH decline [20]. Cropping systems can also reflect other management practices related to soil pH change, such as irrigation which leads to temporally flooded fields and a larger acid buffering capacity [21]. The conversion from upland to paddy systems modifies N-cycling microbes and increases soil pH [22]. Moreover, soil pH is strongly affected by cropping and harvesting which removes base cations by crop growth and residue removal [11,23]. Maize-wheat rotations have been shown to have higher acidification rates than rice-wheat and rice-fallow, with crop harvesting being the main driver [24]. These imply that spatial data on cropping systems could provide information on fertilization, cropping frequency, and other management practices relevant to the spatial heterogeneity of soil pH. The great advancement in the satellite-based mapping of crop rotation offers a unique opportunity for integrating cropping systems and DSM [25, 26, 27, 28, 29]. For example, crop rotation in the northern Hunan Province was mapped by a hybrid deep learning model that synergized Sentinel-1 and Sentinel-2 time series [28]. As a result, cropping system maps are increasingly used as inputs for predicting soil properties, especially in agricultural regions with intense human activity and homogenous climate and parent material [30]. For example, incorporating crop rotation in soil organic matter (SOM) prediction led to a decrease in the RMSE of 7% and an increase in the R\({}^{2}\) of 24% [31].