###### Abstract  
Due to the declining land resources over the past few decades, the intensification of land uses has played a significant role in balancing the ever-increasing demand for food in developing nations such as India. To optimize agricultural land uses, one of the crucial indicators is cropping intensity, which measures the number of times a single parcel of land is farmed. Therefore, it is imperative to create a timely and accurate cropping intensity map so that landowners and agricultural planners can use it to determine the best course of action for the present and for the future. In the present study, we have developed an algorithm on Google Earth Engine (GEE) to depict cropping patterns and further fused it with a GIS environment to depict cropping intensity in the arid western plain zone of Rajasthan, India. A high-resolution multi-temporal harmonized product of the Sentinel-2 dataset was incorporated for depicting the growth cycle of crops for the year 2020-2021 using the greenest pixel composites. Kharif and Rabi accounted for 73.44% and 26.56% of the total cultivated area, respectively. Only 7.42% was under the double-cropped area to the total cultivated area. The overall accuracy of the classified image was 90%. For the Kharif crop, the accuracy was 95%, while for Rabi and the double-cropped region, the accuracy was 88%, with a kappa coefficient of 0.784. The present study was able to depict the seasonal plantation system in arid arable land with higher accuracy. The proposed work can be used to monitor cropping patterns and cost-effectively show cropping intensities. cropping pattern; cropping intensity; Google Earth Engine; arid western plain zone +
Footnote †: journal: Medical Imaging  
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Footnote †: journal: Medical Imaging  
is the feasible source of timely, cost-effective, and relatively accurate information in spatial extent regarding the present cropping systems. Using multi-temporal and multi-sensor remote sensing data, many researchers have demonstrated that it is possible to map cropping patterns, crop rotation, and other agricultural practices [14; 15; 16; 17]. A remote sensing and geographic information system approach was adopted to depict cropping pattern mapping in Tumkur Taluk using the NDVI Technique [18]. The study showed how to employ remote sensing and GIS to map crop patterns and suitability. Cropping information is derived from Landsat 8 data using NDVI and supervised classification. The analysis considered the drainage, slope, and current crop activity maps as important aspects. The study concluded that ragi, coconut plantation, and paddy fields had a definite relation with water resource potential and crop suitability maps that helps farmers in achieving a high spatial yield. Few studies focused on integrating remote sensing and GIS to analyze the impact of cropping patterns on the environment [19]. The information used in this study was from both field observations and observations made via remote sensing. According to the findings, during the summer months, the region exhibited a uniform cropping pattern; most of the district was left follow; the three most important crops were fodder, dhaicha, and sunflowers. During the winter months, the areas that exhibited a dissimilar crop pattern underwent a dramatic shift. A phenology-based technique was developed in studies to identify double-cropping fields by analyzing time-series Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2000 to 2020 with the Google Earth Engine [20]. The findings revealed that throughout the course of the past 20 years, the northern limit of double crop (NLDC) has usually trended both eastward and southward. The study provided a scientific foundation for the continued expansion and strategic planning for agricultural production in China.