Soil moisture plays an important role in crop yield estimation,irrigation management,etc.Remote sensing technology has potential for large-scale and high spatial soil moisture mapping.However,offline remote sensing da...Soil moisture plays an important role in crop yield estimation,irrigation management,etc.Remote sensing technology has potential for large-scale and high spatial soil moisture mapping.However,offline remote sensing data processing is time-consuming and resource-intensive,and significantly hampers the efficiency and timeliness of soil moisture mapping.Due to the high-speed computing capabilities of remote sensing cloud platforms,a High Spatial Resolution Soil Moisture Estimation Framework(HSRSMEF)based on the Google Earth Engine(GEE)platform was developed in this study.The functions of the HSRSMEF include research area and input datasets customization,radar speckle noise filtering,optical-radar image spatio-temporal matching,soil moisture retrieving,soil moisture visualization and exporting.This paper tested the performance of HSRSMEF by combining Sentinel-1,Sentinel-2 images and insitu soil moisture data in the central farmland area of Jilin Province,China.Reconstructed Normalized Difference Vegetation Index(NDVI)based on the Savitzky-Golay algorithm conforms to the crop growth cycle,and its correlation with the original NDVI is about 0.99(P<0.001).The soil moisture accuracy of the random forest model(R 2=0.942,RMSE=0.013 m3/m3)is better than that of the water cloud model(R 2=0.334,RMSE=0.091 m3/m3).HSRSMEF transfers time-consuming offline operations to cloud computing platforms,achieving rapid and simplified high spatial resolution soil moisture mapping.展开更多
The objective of this study is to quantify the values of greenhouse gases(GHGs) exchange in carbon equivalents of marshes and paddy fields in the Sanjiang Plain,Heilongjiang Province,China. We obtained the GHGs exchan...The objective of this study is to quantify the values of greenhouse gases(GHGs) exchange in carbon equivalents of marshes and paddy fields in the Sanjiang Plain,Heilongjiang Province,China. We obtained the GHGs exchange values based on comparable price by calculating the carbon sequestration values and the GHGs emission values of marshes and paddy fields respectively in four periods of 1982,1995,2000 and 2005. It is noted that the GHGs emission values are always negative. In this study,the marshes areas decreased from 1438977.0 to 775,132.2ha and the paddy fields areas increased from 417195.8 to 934205.0ha. The values of GHGs exchange of marshes varied from 135877.156×106 to 136882.534×106 yuan(RMB) and those of paddy fields varied from 1006.256×106 to 2767.645×106 yuan. The GHGs exchange values of marshes decreased from 1982 to 2005 on the whole,reversely,those of paddy fields increased,but those in 2005 were lower than those in 2000. In different periods,the GHGs exchange values were always higher in marshes than in paddy fields. The contribution rate of GHGs exchange values per unit area of marshes was also very high in different periods,and the maximum was up to 98.35% in 2005. As far as the whole wetland ecosystem(including marshes and paddy fields) ,assuming a linear change in GHGs exchange values,it represented a cumulative increase of 20926.757×106 yuan from 1982 to 2005. By adding GHGs exchange values increased during those four periods,we obtained a cumulative net increase values of GHGs exchange of wetland ecosystem of 18200.860×106 yuan. The results will be useful for understanding the indirect services provided by marshes and paddy fields.展开更多
基金Under the auspices of National Key Research and Development Project of China(No.2021YFD1500103)Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDA28100500)+2 种基金National Natural Science Foundation of China(No.4197132)Science and Technology Development Plan Project of Jilin Province(No.20210201044GX)Land Observation Satellite Supporting Platform of National Civil Space Infrastructure Project(No.CASPLOS-CCSI)。
文摘Soil moisture plays an important role in crop yield estimation,irrigation management,etc.Remote sensing technology has potential for large-scale and high spatial soil moisture mapping.However,offline remote sensing data processing is time-consuming and resource-intensive,and significantly hampers the efficiency and timeliness of soil moisture mapping.Due to the high-speed computing capabilities of remote sensing cloud platforms,a High Spatial Resolution Soil Moisture Estimation Framework(HSRSMEF)based on the Google Earth Engine(GEE)platform was developed in this study.The functions of the HSRSMEF include research area and input datasets customization,radar speckle noise filtering,optical-radar image spatio-temporal matching,soil moisture retrieving,soil moisture visualization and exporting.This paper tested the performance of HSRSMEF by combining Sentinel-1,Sentinel-2 images and insitu soil moisture data in the central farmland area of Jilin Province,China.Reconstructed Normalized Difference Vegetation Index(NDVI)based on the Savitzky-Golay algorithm conforms to the crop growth cycle,and its correlation with the original NDVI is about 0.99(P<0.001).The soil moisture accuracy of the random forest model(R 2=0.942,RMSE=0.013 m3/m3)is better than that of the water cloud model(R 2=0.334,RMSE=0.091 m3/m3).HSRSMEF transfers time-consuming offline operations to cloud computing platforms,achieving rapid and simplified high spatial resolution soil moisture mapping.
基金Under the auspices of National Natural Science Foundation of China (No. 40830535)Frontier of Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences (No. KZCX3-SW-NA3-01)
文摘The objective of this study is to quantify the values of greenhouse gases(GHGs) exchange in carbon equivalents of marshes and paddy fields in the Sanjiang Plain,Heilongjiang Province,China. We obtained the GHGs exchange values based on comparable price by calculating the carbon sequestration values and the GHGs emission values of marshes and paddy fields respectively in four periods of 1982,1995,2000 and 2005. It is noted that the GHGs emission values are always negative. In this study,the marshes areas decreased from 1438977.0 to 775,132.2ha and the paddy fields areas increased from 417195.8 to 934205.0ha. The values of GHGs exchange of marshes varied from 135877.156×106 to 136882.534×106 yuan(RMB) and those of paddy fields varied from 1006.256×106 to 2767.645×106 yuan. The GHGs exchange values of marshes decreased from 1982 to 2005 on the whole,reversely,those of paddy fields increased,but those in 2005 were lower than those in 2000. In different periods,the GHGs exchange values were always higher in marshes than in paddy fields. The contribution rate of GHGs exchange values per unit area of marshes was also very high in different periods,and the maximum was up to 98.35% in 2005. As far as the whole wetland ecosystem(including marshes and paddy fields) ,assuming a linear change in GHGs exchange values,it represented a cumulative increase of 20926.757×106 yuan from 1982 to 2005. By adding GHGs exchange values increased during those four periods,we obtained a cumulative net increase values of GHGs exchange of wetland ecosystem of 18200.860×106 yuan. The results will be useful for understanding the indirect services provided by marshes and paddy fields.
