###### Abstract  
Remotely sensed images provide effective sources for monitoring crop growth and the early prediction of crop productivity. To monitor carrot crop growth and yield estimation, three 27 ha center-pivot irrigated fields were studied to develop yield prediction models using crop biophysical parameters and vegetation indices (VIs) extracted from Sentinel-2A (S2) multi-temporal satellite data. A machine learning (ML)-based image classification technique, the random forest (RF) algorithm, was used for carrot crop monitoring and yield analysis. The VIs (NDVI, RDVI, GNDVI, SIPI, and GLI), extracted from S2 satellite data for the crop ages of 30, 45, 60, 75, 90, 105, and 120 days after plantation (DAP), and the chlorophyll content, SPAD (Soil Plant Analysis Development) meter readings, were incorporated as predictors for the RF algorithm. The RMSE of the five RF scenarios studied ranged from 7.8 t ha\({}^{-1}\) (R\({}^{2}\)\(\geq\) 0.82 with Scenario 5) to 26.2 t ha\({}^{-1}\) (R\({}^{2}\)\(\leq\) 0.46 with Scenario 1). The optimal window for monitoring the carrot crop for yield prediction with the use of S2 images could be achieved between the 60 DAP and 75 DAP with an RMSE of 8.6 t ha\({}^{-1}\) (i.e., 12.4%) and 11.4 t ha\({}^{-1}\) (16.2%), respectively. The developed RF algorithm can be utilized in carrot crop yield monitoring and decision-making processes for the self-sustainability of carrot production. carrot crop monitoring; machine learning; Sentinel-2 images; vegetation indices; yield prediction; random forest (RF) +
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generated from moderate- to low-resolution satellite images [7,8]. Feature extraction and the assessment of the temporal dynamics of spectral information on agricultural lands are also crucial in decision processes [9, 10, 11]. Information and data analytics on seasonal crop conditions with periodic advisories on yield prediction in the early stages of the crop or before harvests are essential and allow a timely intervention to improve crop conditions [3,4]. Advanced remote-sensing techniques, multi-sensor satellite data, drones, mobile-based field data, and many other modeling techniques offer the opportunity to identify crop risk assessment, agro-advisory, yield assessment, and water use efficiency as well as solutions for farmer-centric and planning-centric activities in the agricultural decision-making process [2]. Spectral analyses such as vegetation indices, leaf area index (LAI), leaf chlorophyll content, etc., have been used to estimate the biophysical characteristics of plants [4, 5, 6]. For example, crop vegetation indices (VIs) extracted from satellite imagery, such as the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), and enhanced vegetation index (EVI), have been widely used for the early prediction of crop yields [12, 13, 14]. The VIs related to different crop growth parameters, such as plant cover (%), leaf area index (LAI), and chlorophyll and nitrogen contents, contribute to describing the growth status of different crops [15, 16, 17, 18]. Numerous studies have proven the effectiveness of using various vegetation indices derived from multispectral wavebands to assess crop growth conditions and yield modeling in conjunction with ground-truth data on crop characteristics [3,10, 11, 12, 13, 14]. [PERSON] et al. [19] reported the extent of studies on the yield forecasting of horticultural crops, while studies by [PERSON] et al. [20,21] described the importance of carrot yield forecasting and explored satellite images used for carrot yield prediction. [PERSON] [22] generated carrot yield maps using planetScope hyperspectral data with a high accuracy (R\({}^{2}\) = 0.68-0.82), and a study by [PERSON] [23] achieved a good carrot predicted yield accuracy (R\({}^{2}\) = 0.68) using planetScope CubeSat Platform. Similarly, studies [24,25] achieved carrot yields with a 33-35% variation using Landsat-8 and Sentinel-2 data, respectively.