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This study performs the time series analysis of agriculture land in the Nainital District of Uttarakhand, India. The study utilizes Landsat satellite images for the classification of agriculture and non-agriculture land over a time duration of 21 years (2000–2021). Landsat 5, 7 and 8 satellites data have been used to classify the study area with Random Forest classifier. The Landsat satellite images are processed using the Google Earth Engine (GEE) platform. The selection of Random Forest classier has been based on a comparative analysis among Random Forest (RF), Support Vector Machines (SVM) and Classification and Regression Trees (CART). Overall accuracy, user accuracy and producer accuracy and Kappa coefficient has been evaluated to determine the best classifier for the study area. The overall accuracy for RF, SVM and CART for the year 2021 is 96.38%, 94.44% and 91.94% respectively. Similarly, the Kappa coefficient for RF, SVM and CART was 0.96, 0.89, 0.81 respectively. The classified images of Landsat in agriculture and non-agriculture area over a period of 21 years (2000–2021) shows a decrement of 4.71% in agriculture land which is quite significant. This study has also shown that the maximum decrease in agriculture area in last four years, i.e., from 2018 to 2021. This kind of study is very important for a developing country to access the change and take proper measure so that flora and fauna of the region can be maintained. [ABSTRACT FROM AUTHOR]