古尔班通古特沙漠SMAP土壤水分产品降尺度分析

• 作者： 薛智暄 ^1,2 张 丽 ^1,2 王新军 ^1,2 李永康 ^1,2 张冠宏 ^1,2 李沛尧 ^1,2
• 作者单位：
  1.  新疆农业大学资源与环境学院，新疆乌鲁木齐830052

  2.  新疆土壤与植物生态 过程实验室，新疆 乌鲁木齐 830052
• 提交时间：2023-05-30

摘要: SMAP(Soil Moisture Active Passive，SMAP)产品空间分辨率低的特征限制了在地表高异质性的干旱区沙漠稀疏植被区的适用性。考虑到干旱区沙漠植被区特殊的环境特征，在地表温度(Land Surface Temperature，LST)、归一化植被指数(Normalized Difference Vegetation Index，NDVI)、数字高程模型(Digital Elevation Model，DEM)等传统降尺度因子的基础上，增加了与荒漠地表土壤水分关联性更强的增强型修改土壤植被指数(Enhanced Modified Soil-Adjusted Vegetation Index，EMSAVI)与比值沙地亮度指数(Ratio Sand Brightness Index，RSBI)分别作为反映研究区植被盖度和裸沙分布状况的降尺度因子，利用随机森林算法(Random Forest，RF)，构建了干旱区土壤水分降尺度模型。结果表明：（1）由相关性分析可知，EMSAVI（r 干=－0.37，r 湿=－0.34）、RSBI（r 干=－0.42，r 湿=－0.25）对荒漠土壤水分均有较好的指示作用且效果优于NDVI（r 干=－0.21，r 湿=0.08）；（2）EMSAVI 和NDVI 重要性分别为18.7%、13.2%，EMSAVI在构建降尺度模型时贡献度更高。（3）构建的干、湿季干旱区土壤水分降尺度模型得到的结果与SMAP产品的R2分别达到了0.916，0.910，RMSE分别达到了0.0075 cm3cm-3、0.0063 cm3cm-3，较传统模型的RMSE均降低了0.0013 cm3cm-3。（4）通过计算LBP(Local Binary Patterns)的差值（LBPC）对空间一致性评价，新构建降尺度模型的结果（0.0585）优于传统降尺度（0.0645）。研究结果将短波红外波段引入到植被指数建立的EMSAVI，可较好地应用于干旱区沙漠稀疏植被区土壤水分降尺度研究。

Abstract: The low spatial resolution of SMAP products limits its applicability to sparsely vegetated arid regions and deserts with high surface heterogeneity. Considering the special environmental characteristics of sparsely vegetated desert areas in arid regions, traditional downscaling methods such as land surface temperature (LST), normalized difference vegetation index (NDVI), and digital elevation model (DEM) have been used, among others. Based on the scale factor, the enhanced modified soil-adjusted vegetation index (Enhanced Modified Soil- Adjusted Vegetation Index, EMSAVI) and the ratio sand brightness index (RSBI), which are more correlated with the desert surface soil moisture, were added to reflect the study area. For the downscaling factors of vegetation coverage and bare sand distribution, the random forest (RF) algorithm was used to build a soil moisture downscaling model in arid areas. The results showed the following: (1) Correlation analysis showed that EMSAVI (rdry = -0.37, rwet = -0.34) and RSBI (rdry = -0.42, rwet = -0.25) were good indicators of desert soil moisture, being superior to NDVI (rdry = -0.21, rwet = 0.08). (2) The importance of EMSAVI and NDVI was 18.7% and 13.2%, respectively, and EMSAVI contributed more to the construction of the downscaling model. (3) The results obtained from the soil moisture downscaling model in dry and wet season arid regions and R2 of the SMAP product reached 0.916 and 0.910, and the RMSE reached 0.0075 cm3cm-3 and 0.0063 cm3cm-3, respectively, which are lower than the RMSE of the traditional model of 0.0013 cm3cm-3. (4) By calculating the difference (LBPC) of LBP (local binary patterns) to evaluate the spatial consistency, the result of the newly constructed downscaling model (0.0585) was better than that of traditional downscaling (0.0645). This research shows that introduction of the short-wave infrared band into the EMSAVI established by the vegetation index enables its better application to the study of soil moisture downscaling in sparsely vegetated desert areas in arid regions.

• 分类： 农、林、牧、渔 >> 土壤学
• 引用： ChinaXiv:202305.00267 (或此版本 ChinaXiv:202305.00267V1)
  doi:10.12074/202305.00267
  CSTR:32003.36.ChinaXiv.202305.00267.V1
• 推荐引用方式： 薛智暄,张 丽,王新军,李永康,张冠宏,李沛尧.(2023).古尔班通古特沙漠SMAP土壤水分产品降尺度分析.干旱区研究.[ChinaXiv:202305.00267] (点此复制)