<![CDATA[Climate uncertainty demands rapid and adaptive agricultural statistics. This study aims to analyze the trend alignment of Sentinel-2 time-series based on Google Earth Engine (GEE) in monitoring growth phases and estimating rice harvested area in Lombok Island in 2025 to strengthen official statistics. A monthly rice phenology analysis method was developed using NDVI and LSWI spectral indices, then validated against NTB Province BPS KSA data. The satellite estimation recorded a harvested area of 112,343 Ha, showing a systematic underestimation of 28.93% compared to the KSA BPS data (158,069 Ha) due to mixed pixels and cloud cover. Despite the absolute area bias, statistical validation indicated highly synchronized monthly trends with a Pearson correlation (r) of 0.9950 (p < 0.0001) and an RMSE of 5,769.96 Ha. It is concluded that this spatial data science application is reliable in detecting temporal harvest trends and holds potential as a complement to official agricultural statistics.]]>
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