基于1公里分辨率降尺度被动微波土壤湿度产品的中国东北地区土壤湿度长期时空变化

Meng Xiangjin, Mao Kebiao, Meng Fei, Shen Xinyi, Xu Tongren, Cao Mengmeng

School of Surveying and Geo-Informatics, Shandong Jianzhu University, Shandong 250100, China.

School of Geography, South China Normal University, Guangzhou 510631, China.

Sensors (Basel). 2019 Aug 12;19(16):3527. doi: 10.3390/s19163527.

It is very important to analyze and monitor agricultural drought to obtain high temporal-spatial resolution soil moisture products. To overcome the deficiencies of passive microwave soil moisture products with low resolution, we construct a spatial fusion downscaling model (SFDM) using Moderate Resolution Imaging Spectroradiometer (MODIS) data. To eliminate the inconsistencies in soil depth and time among different microwave soil moisture products (Advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR-E) and its successor (AMSR2) and the Soil Moisture Ocean Salinity (SMOS)), a time series reconstruction of the difference decomposition (TSRDD) method is developed to create long-term multisensor soil moisture datasets. Overall, the downscaled soil moisture (SM) products were consistent with the in situ measurements (R > 0.78) and exhibited a low root mean square error (RMSE < 0.10 m/m), which indicates good accuracy throughout the time series. The downscaled SM data at a 1-km spatial resolution were used to analyze the spatiotemporal patterns and monitor abnormal conditions in the soil water content across North East China (NEC) between 2002 and 2018. The results showed that droughts frequently appeared in western North East China and southwest of the Greater Khingan Range, while drought centers appeared in central North East China. Waterlogging commonly appeared in low-terrain areas, such as the Songnen Plain. Seasonal precipitation and temperature exhibited distinct interdecadal characteristics that were closely related to the occurrence of extreme climatic events. Abnormal SM levels were often accompanied by large meteorological and natural disasters (e.g., the droughts of 2008, 2015, and 2018 and the flooding events of 2003 and 2013). The spatial distribution of drought in this region during the growing season shows that the drought-affected area is larger in the west than in the east and that the semiarid boundary extends eastward and southward.

分析和监测农业干旱以获取高时空分辨率的土壤湿度产品非常重要。为了克服被动微波土壤湿度产品分辨率低的缺陷，我们利用中分辨率成像光谱仪（MODIS）数据构建了一个空间融合降尺度模型（SFDM）。为了消除不同微波土壤湿度产品（地球观测系统先进微波扫描辐射计（AMSR-E）及其后续产品（AMSR2）和土壤湿度海洋盐度仪（SMOS））之间土壤深度和时间上的不一致性，开发了一种差分分解时间序列重建（TSRDD）方法来创建长期多传感器土壤湿度数据集。总体而言，降尺度后的土壤湿度（SM）产品与原位测量结果一致（R>0.78），且均方根误差较低（RMSE<0.10 m/m），这表明在整个时间序列中精度良好。利用1公里空间分辨率的降尺度SM数据，分析了2002年至2018年中国东北地区（NEC）土壤水分含量的时空格局并监测异常情况。结果表明，干旱频繁出现在中国东北西部和大兴安岭西南部，而干旱中心出现在中国东北中部。涝灾常见于低地形区域，如松嫩平原。季节性降水和温度呈现出明显的年代际特征，这与极端气候事件的发生密切相关。异常的SM水平往往伴随着重大气象和自然灾害（如2008年、2015年和2018年的干旱以及2003年和2013年的洪水事件）。该地区生长季干旱的空间分布表明，西部地区受干旱影响的面积大于东部地区，半干旱边界向东和向南延伸。