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Landsat 9, the latest launched Landsat satellite, is the successor and continuation of Landsat 8, providing a highly promising data resource for land cover change, forest surveys, and terrestrial ecosystem monitoring. Regression kriging was developed in the study to improve the AGB estimation and mapping using the Landsat 9 image in Wangyedian forest farm, northern China. Multiple linear regression (MLR), support vector machine (SVM), back propagation neural network (BPNN), and random forest (RF) were used as the original models to predict the AGB trends, and the optimal model was used to overlay the results of kriging interpolation based on the residuals to obtain the new AGB predictions. In addition, Landsat 8 images in Wangyedian were used for comparison and verification with Landsat 9. The results showed that all bands of Landsat 8 and Landsat 9 maintained a high degree of uniformity, with positive correlation coefficients ranging from 0.77 to 0.89 (p &lt; 0.01). RF achieved the highest estimation accuracy among all the original models based on the two data sources. However, kriging regression can significantly reduce the estimation error, with the root mean square error (RMSE) decreasing by 55.4% and 51.1%, for Landsat 8 and Landsat 9, respectively, compared to the original RF. Further, the R2 and the lowest RMSE for Landsat 8 were 0.88 and 16.83 t\/ha, while, for Landsat 9, they were 0.87 and 17.91 t\/ha. The use of regression kriging combined with Landsat 9 imagery has great potential for achieving efficient and highly accurate forest AGB estimates, providing a new reference for long-term monitoring of forest resource dynamics.<\/jats:p>","DOI":"10.3390\/rs14225734","type":"journal-article","created":{"date-parts":[[2022,11,14]],"date-time":"2022-11-14T04:24:10Z","timestamp":1668399850000},"page":"5734","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Above-Ground Biomass Estimation for Coniferous Forests in Northern China Using Regression Kriging and Landsat 9 Images"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1940-9952","authenticated-orcid":false,"given":"Fugen","family":"Jiang","sequence":"first","affiliation":[{"name":"Research Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China"},{"name":"Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China"},{"name":"Key Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing 100091, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5401-6783","authenticated-orcid":false,"given":"Hua","family":"Sun","sequence":"additional","affiliation":[{"name":"Research Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China"}]},{"given":"Erxue","family":"Chen","sequence":"additional","affiliation":[{"name":"Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China"},{"name":"Key Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing 100091, China"}]},{"given":"Tianhong","family":"Wang","sequence":"additional","affiliation":[{"name":"Research Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China"}]},{"given":"Yaling","family":"Cao","sequence":"additional","affiliation":[{"name":"Research Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2339-6223","authenticated-orcid":false,"given":"Qingwang","family":"Liu","sequence":"additional","affiliation":[{"name":"Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China"},{"name":"Key Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing 100091, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111341","DOI":"10.1016\/j.rse.2019.111341","article-title":"Estimating aboveground biomass in subtropical forests of China by integrating multisource remote sensing and ground data","volume":"232","author":"Zhang","year":"2019","journal-title":"Remote Sens. 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