## 1 Introduction  
Understanding the spatiotemporal dynamics of cropping intensity is pivotal for effective cropland management and sustainable agricultural development. Cropping intensity, defined as the cultivation frequency on a cropland plot within a year, serves as a crucial indicator of cropland utilization efficiency and productivity [1]. In China, the multiple cropping system, i.e., harvesting two or more crops within a single year, has a long history dating back to the first century [2]. This strategic approach to land use aims to increase crop production within the constraints of limited cropland area, thereby maximizing the utilization of agroclimatic resources [3]. Shaanxi Province, located in northwestern China, is a typical agricultural region characterized by diverse topography and climatic conditions. Shaanxi was one of three pilot provinces at the beginning of the Grain for Green program in 1999 in which steeply sloping cropland was converted to forest or grassland [4]. Consequently, there has been a substantial reduction in cropland area in Shaanxi [5;6]. Yet, large portions of abandoned cropland have also been observed in gently sloping areas [7]. Since 2020, Shaanxi has implemented several measures aimed at the re-cultivation of abandoned cropland to increase food supply. Thus, the period from 2019 to 2023 marked a critical juncture in the trajectory of cropland management in Shaanxi. Remote sensing technology has played a crucial role in agricultural land use mapping since the 1970s [8]. Over the past few decades, research in crop mapping using remote sensing has grown significantly, with an increasing reliance on time series imagery to monitor crop phenology and seasonal variability [9;10]. Medium-resolution imagery, such as from the Moderate Resolution Imaging Spectroradiometer (MODIS), has been extensively used for generating cropping intensity assessments [11;12]. For example, [12] produced a global cropping intensity dataset from 2001 to 2019 using MODIS enhanced vegetation index (EVI) time series with a 16-day temporal resolution. While earlier studies have made significant progress in crop intensity mapping using remote sensing, many still rely on data from a single sensor [8], which involves tradeoffs between spatial and temporal resolution due to the physical limitations of individual sensors [13]. For example, Landsat imagery provides a high spatial resolution (30 m), but its temporal resolution is often insufficient to capture cropping patterns [14]. To address these limitations, spatiotemporal fusion techniques have been developed, combining fine- and coarse-resolution imagery to generate datasets with high spatiotemporal resolutions [15;16]. By fusing Landsat and MODIS data, it is possible to create 8-day-interval time series with a 30 m spatial resolution [17]. Additionally, the fusion of Landsat-8 and Sentinel-2 imagery offers even richer observations, achieving a temporal resolution of less than 5 days [18], enabling more detailed analysis of seasonal cropland dynamics. Given the substantial data volume and computational complexity involved, cloud computing platforms like Google Earth Engine (GEE) have become indispensable, offering planetary-scale processing capabilities for handling large datasets efficiently [19]. By leveraging its multi-petabyte data catalog and freely accessible computational service, GEE has been widely used for large-scale crop mapping and agricultural monitoring [20; 21; 22]. Despite these advancements, the potential of high-spatiotemporal-resolution imagery to accurately reveal cropping intensity remains underexplored, particularly in fragmented agricultural regions with complex cropping patterns like Shaanxi Province [23]. Although some studies have employed fused data to develop cropping intensity maps for individual years [14;24;25], these is still a notable gap in the availability of continuous, high-resolution cropping intensity maps for such challenging regions.