## 4 Discussion  
The present investigation emphasizes the integration of crop simulation models with remote sensing techniques to assess rice yield at the village level, with the LAI serving as its core component. The initial step involves mapping the rice-growing regions, which subsequently leads to developing a rice crop mask. The selection of study locations was based on many factors encompassing a wide range of potential combinations. Multiple methodologies have been employed to assess the LAI in diverse agricultural crops, including soybean, maize, potato, wheat, and mustard [37]. The present work employed time series data from the Sentinel-2 satellite to estimate the LAI and utilized the DSSAT agricultural simulation model to enhance the accuracy of crop yield forecasting at the individual field level. The model's simulated LAI was correlated with the remote sensing outputs; nitrogen application was the most significant influencing factor. The necessary weather, soil, and crop data, including the day of transplantation and fertilizer application rates, were obtained and integrated into the crop simulation model. The model and field data exhibited a coefficient of determination (R\({}^{2}\)) of 0.8 and a Nash-Sutcliffe Efficiency  
Figure 8: Spatial yield map of Karimnagar at the Gram Panchayat (GP) level. (NSE) of 0.70. The model established a connection between the simulated LAI and remote sensing outputs, as nitrogen application was identified as the main governing factor of LAI. The model yielded an average district yield of 5350 kg/ha, comparable to the official statistics of 5014 kg/ha [53]. Based on the observations, it can be asserted that the model exhibits a high degree of reliability in accurately projecting crop yields over a range of different management approaches. Variations in crop output were identified among the villages, which might be attributed to differences in the agricultural management approaches employed in the respective fields. The practice of sowing crops earlier than the optimal period resulted in a decline in agricultural productivity in certain villages, in contrast to the outcomes observed when sowing was carried out at the correct time point. Moreover, applying fertilizers in quantities beyond the recommended dose decreased crop production. The purpose of this study was to demonstrate the significance and essential role of LAI in conjunction with crop models in the evaluation of crop yield assessment. However, relying solely on LAI may not generate accurate estimations since the most notable results have been observed when both LAI and vegetation indices are incorporated into crop models [24, 29]. One notable constraint and potential area for improvement identified in this study is the zeptometer's inability to function well when the photosynthetically active radiation (PAR) falls below 400 units. Consequently, this restricts its practicality for data collection under cloudy weather conditions. Utilizing high-resolution data is advisable to achieve a high level of accuracy in the outcomes, given that the farmers possessed small-scale agricultural plots in the villages. The resolution employed in this investigation was set at 10 m. The capacity of DSSAT to address the impact of biotic stress caused by pests, diseases, and weeds is constrained. Hence, integrating dynamic pest and disease models is of utmost importance. It is necessary to calibrate the genotypic coefficients of primary locally cultivated cultivars to ensure their suitability for practical implementation. Adapting the model to fulfill the prescribed criteria presents a formidable task due to its inherent incorporation of intricate protocols and requisite detailed documentation. This study focused primarily on Sustainable Development Goal (SDG) number 2, pertaining to Zero Hunger and the promotion of sustainable agriculture. The implementation of crop-cutting experiments in a limited area of a field, as opposed to measuring the yield of the entire field, also contributes to the promotion of sustainable agriculture.