Grasslands on the Mongolian Plateau are critical for supporting local sustainable development. Sufficientmeasuredsampleinformationisthebasisofremotesensingmodelingandestimation ofgrasslandproduction. Limitedbyfieldinventorycosts,itisdifficulttocollectsufficientandwidely distributed samples in the Mongolian Plateau, especially in transboundary areas, which affects the results of grassland production estimation. Here, considering that the measured sample points are sparse, this study took Xilingol League of Inner Mongolia Autonomous Region in China and Dornogovi Province in Mongolia as the study areas, introduced multiple interpolation methods for interpolation experiments, established a statistical regression model based on the above measured and interpolated samples combined with the normalized differential vegetation index, and discussed the applicability of grassland production estimation. The comparison results revealed that the point estimation biased sample hospital-based area disease estimation method and radial basis function showed the best interpolation results for grassland production in Xilingol League and Dornogovi Province,respectively. Thepowerfunctionmodelwassuitableforgrasslandproductionestimationin both regions. By inversion, we obtained annual grassland production for 2010–2021 and the uneven spatial distribution of grassland production in both regions. In these two regions, the spatial change in grassland production showed a decreasing trend from northeast to southwest, and the interannual changegenerallyshowedadynamicupwardtrend. Thegrowthrateofgrasslandoutputwasfasterin Xilingol League than in Dornogovi Province with similar physical geography and climate conditions, indicating that the animal husbandry regulation policies play important roles beyond the influence of climate change. The study recommended grassland estimation methods for an area with sparse samples and the results can be used to support decision making for sustainable animal husbandry and grassland succession management.