This paper reports our current activities in Laos for monitoring rice production with remote sensing technologies in lowland field toward efficient farm management at small farm scale (<1 km²) or local scale (village, <100 km²). At farm scale (Research 1), the grain yield of lowland rice was evaluated from field hyperspectral data of paddy fields during the reproductive stage to the ripening stage in conjunction with iterative stepwise elimination partial least squares (ISE-PLS) regression. At local scale (Research 2), we established a land infrastructure data set in a lowland rice field at Koudkher village combining with UAV images and geographic information system (GIS) in order to assess the potential yield and its environmental effects. In Research 1, the highest R² values and the lowest root mean squared error of cross-validation (RMSECV) values were obtained from the ISEPLS model at the booting stage (R² = 0.873, RMSECV = 22.903); the residual predictive deviation was >2.4. Selected hyperspectral (HS) wavebands in the ISE-PLS model were identified in the rededge (710–740 nm) and near-infrared (830 nm) regions. These results confirmed that the booting stage might be the best time for in-season rice grain assessment and that rice yield could be evaluated accurately from the HS sensing data via the ISE-PLS model. In Research 2, a GIS-based land infrastructure information dataset was established in rain-fed paddy field (Koudkher village) from UAV images and digital terrain model. Using the data set, Ikeura et al. (2019) assessed the relationship between rice yield and water/soil conditions in the rainfed rice fields in Koudkher village.