###### Abstract
The successful prediction of crop yield depends mainly on the capability of sensors or images and the spectral response of a crop [26, 27, 28, 29]. The crop yield variability can be assessed by addressing crop growth dynamics and multi-temporal data related to biophysical, physiological, or biochemical characteristics [30]. Fast and accurate information on crop phenology, crop health, crop water use, yield, etc. can be achieved with the use of deep-learning (DL) and artificial intelligence (AI) tools [31, 32, 33]. Machine learning (ML) is essential for improving the growth of the crop yield in a sustainable manner, helping to interpret and correlate field data with consumption techniques that can contribute to support decision-making in agriculture for crop health monitoring, agronomic suggestions, disease detection, weed-control, yield prediction, etc. [34, 35, 36]. Examples include sunflower [37], sugarcane [38], rice [39], corn [40], wheat [41], carrot [20, 21, 22], etc. The ML algorithms, including neural network (NN), stacked autoencoder (SAE), recurrent NN (RNN), graph NN (GNN), and restricted Boltzmann machine (RBM), have been widely used in agricultural applications [32]. Recently, machine-learning techniques, especially regression decision trees, random forest (RF) regression analysis, and artificial neural networks, have been widely used in the mapping and monitoring of crops [33, 34, 35, 36]. One of the main advantages of machine learning is the development of a trained model that can be used to classify any scene so that the procedure can be partially or fully automated. Many authors applied deep-learning tools for the extraction of information from various satellite datasets, including Landsat-8, Sentinel-2, World View-2, hyperspectral, POLSAR (Polarimetric SAR), etc. [23, 24, 25, 26, 27, 28, 29]. Leaf chlorophyll content (LCC) is an important parameter to understanding the dynamic changes in physiological aspects and is highly related to crop health and productivity. To characterize the spatial variability of LCC in large fields, using traditional methods such as plant nutrient analysis is a laborious, cost-effective, and time-consuming process. Given the strong correlation between chlorophyll and nitrogen content in green vegetation, the non-destructive mode of chlorophyll measurements recorded with the Soil Plant Analysis Development--SPAD meter (SPAD-502, Minolta Osaka Company, Ltd., Osaka, Japan) is considered a reliable representation of LCC [42, 43, 44]. Recent studies used the SPAD reading in conjunction with remotely sensed images, such as Landsat, Sentinel-2, and drone images, for monitoring crop health conditions and spatial variation [44; 45; 46; 47]. Satellites and ML algorithms have been used to predict crop production [20; 21; 22; 23]. However, such studies are very limited, especially under dry climatic conditions. Hence, the current study, which aimed to monitor crop health and generate carrot pre-harvest yield models using machine-learning techniques, was formulated as part of the self-sustainability of vegetable crops and their water footprint. The main objective of this study was to address crop-monitoring strategies for crop phenology, health stress, and yield through the retrieval of leaf chlorophyll content (LCC) by combining the SPAD readings and remotely sensed Sentinel-2 multispectral data employing ML techniques. The specific objectives were (i) to retrieve the leaf chlorophyll content (LCC) of carrot fields by combining the SPAD readings and Sentinel-2 data for crop health monitoring and yield assessment, and (ii) to incorporate LCC data for the development of carrot yield forecasting algorithms using the random forest (RF) approach, a machine-learning (ML) tool.