Rapid and accurate acquisition of soil organic matter(SOM)information in cultivated land is important for sustainable agricultural development and carbon balance management.This study proposed a novel approach to pred...Rapid and accurate acquisition of soil organic matter(SOM)information in cultivated land is important for sustainable agricultural development and carbon balance management.This study proposed a novel approach to predict SOM with high accuracy using multiyear synthetic remote sensing variables on a monthly scale.We obtained 12 monthly synthetic Sentinel-2 images covering the study area from 2016 to 2021 through the Google Earth Engine(GEE)platform,and reflectance bands and vegetation indices were extracted from these composite images.Then the random forest(RF),support vector machine(SVM)and gradient boosting regression tree(GBRT)models were tested to investigate the difference in SOM prediction accuracy under different combinations of monthly synthetic variables.Results showed that firstly,all monthly synthetic spectral bands of Sentinel-2 showed a significant correlation with SOM(P<0.05)for the months of January,March,April,October,and November.Secondly,in terms of single-monthly composite variables,the prediction accuracy was relatively poor,with the highest R^(2)value of 0.36 being observed in January.When monthly synthetic environmental variables were grouped in accordance with the four quarters of the year,the first quarter and the fourth quarter showed good performance,and any combination of three quarters was similar in estimation accuracy.The overall best performance was observed when all monthly synthetic variables were incorporated into the models.Thirdly,among the three models compared,the RF model was consistently more accurate than the SVM and GBRT models,achieving an R^(2)value of 0.56.Except for band 12 in December,the importance of the remaining bands did not exhibit significant differences.This research offers a new attempt to map SOM with high accuracy and fine spatial resolution based on monthly synthetic Sentinel-2 images.展开更多
应用地统计学与地理信息系统相结合的方法,研究了杭州湾南岸慈溪市域内不同土层(0～20、20～40、40～60、60～80、80～100、0～100 cm)的土壤有机碳含量空间变异特征。结果表明:研究区各土层土壤有机碳平均含量变化范围为3.49～7.95 g k...应用地统计学与地理信息系统相结合的方法,研究了杭州湾南岸慈溪市域内不同土层(0～20、20～40、40～60、60～80、80～100、0～100 cm)的土壤有机碳含量空间变异特征。结果表明:研究区各土层土壤有机碳平均含量变化范围为3.49～7.95 g kg-1,变异系数介于54.51%～67.34%之间,属中等程度变异;地统计分析得出块金效应变化范围为0.141～0.372,表现为较强空间自相关性;自表层至底层最优半方差模型依次为高斯、指数、指数、高斯和球状模型;Kriging插值结果显示各土层土壤有机碳含量自滩涂向内陆呈递增趋势,其中0～20 cm土层土壤有机碳含量呈平行于海岸线的带状分布;土壤有机碳含量随剖面深度增加呈递减规律;不同土地利用方式和不同围垦时期均增加了土壤有机碳的空间变异性。从研究结果看,慈溪市土壤有机碳空间异质性主要由结构性因素引起,研究结果可为了解杭州湾南岸土壤有机碳分布特征提供参考。展开更多
基金National Key Research and Development Program of China(2022YFB3903302 and 2021YFC1809104)。
文摘Rapid and accurate acquisition of soil organic matter(SOM)information in cultivated land is important for sustainable agricultural development and carbon balance management.This study proposed a novel approach to predict SOM with high accuracy using multiyear synthetic remote sensing variables on a monthly scale.We obtained 12 monthly synthetic Sentinel-2 images covering the study area from 2016 to 2021 through the Google Earth Engine(GEE)platform,and reflectance bands and vegetation indices were extracted from these composite images.Then the random forest(RF),support vector machine(SVM)and gradient boosting regression tree(GBRT)models were tested to investigate the difference in SOM prediction accuracy under different combinations of monthly synthetic variables.Results showed that firstly,all monthly synthetic spectral bands of Sentinel-2 showed a significant correlation with SOM(P<0.05)for the months of January,March,April,October,and November.Secondly,in terms of single-monthly composite variables,the prediction accuracy was relatively poor,with the highest R^(2)value of 0.36 being observed in January.When monthly synthetic environmental variables were grouped in accordance with the four quarters of the year,the first quarter and the fourth quarter showed good performance,and any combination of three quarters was similar in estimation accuracy.The overall best performance was observed when all monthly synthetic variables were incorporated into the models.Thirdly,among the three models compared,the RF model was consistently more accurate than the SVM and GBRT models,achieving an R^(2)value of 0.56.Except for band 12 in December,the importance of the remaining bands did not exhibit significant differences.This research offers a new attempt to map SOM with high accuracy and fine spatial resolution based on monthly synthetic Sentinel-2 images.
文摘应用地统计学与地理信息系统相结合的方法,研究了杭州湾南岸慈溪市域内不同土层(0～20、20～40、40～60、60～80、80～100、0～100 cm)的土壤有机碳含量空间变异特征。结果表明:研究区各土层土壤有机碳平均含量变化范围为3.49～7.95 g kg-1,变异系数介于54.51%～67.34%之间,属中等程度变异;地统计分析得出块金效应变化范围为0.141～0.372,表现为较强空间自相关性;自表层至底层最优半方差模型依次为高斯、指数、指数、高斯和球状模型;Kriging插值结果显示各土层土壤有机碳含量自滩涂向内陆呈递增趋势,其中0～20 cm土层土壤有机碳含量呈平行于海岸线的带状分布;土壤有机碳含量随剖面深度增加呈递减规律;不同土地利用方式和不同围垦时期均增加了土壤有机碳的空间变异性。从研究结果看,慈溪市土壤有机碳空间异质性主要由结构性因素引起,研究结果可为了解杭州湾南岸土壤有机碳分布特征提供参考。
