张桂欣, 郝振纯, 祝善友, 周楚炫, 华俊玮. AMSR2缺失数据重建及其土壤湿度反演精度评价[J]. 农业工程学报, 2016, 32(20): 137-143. DOI: 10.11975/j.issn.1002-6819.2016.20.018
    引用本文: 张桂欣, 郝振纯, 祝善友, 周楚炫, 华俊玮. AMSR2缺失数据重建及其土壤湿度反演精度评价[J]. 农业工程学报, 2016, 32(20): 137-143. DOI: 10.11975/j.issn.1002-6819.2016.20.018
    Zhang Guixin, Hao Zhenchun, Zhu Shanyou, Zhou Chuxuan, Hua Junwei. Missing data reconstruction and evaluation of retrieval precision for AMSR2 soil moisture[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2016, 32(20): 137-143. DOI: 10.11975/j.issn.1002-6819.2016.20.018
    Citation: Zhang Guixin, Hao Zhenchun, Zhu Shanyou, Zhou Chuxuan, Hua Junwei. Missing data reconstruction and evaluation of retrieval precision for AMSR2 soil moisture[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2016, 32(20): 137-143. DOI: 10.11975/j.issn.1002-6819.2016.20.018

    AMSR2缺失数据重建及其土壤湿度反演精度评价

    Missing data reconstruction and evaluation of retrieval precision for AMSR2 soil moisture

    • 摘要: 新型传感器土壤湿度产品在水分循环、农业管理等领域应用之前,需要评价其在不同区域的反演精度与数据质量。该文针对目前全球空间分辨率最高的AMSR2(advanced microwave scanning radiometer 2)3级微波土壤湿度产品,实现了缺失数据重建及其反演精度评价目的。论文研究引入离散余弦函数表达的惩罚最小二乘回归(discrete cosine transformation-partial least square,DCT-PLS)方法,重建因卫星轨道等原因造成的缺失与异常值,以构建时空分布连续的土壤湿度数据;进而以山西省为例,采用1 km分辨率的MODIS光学波段数据对AMSR2微波产品进行降尺度处理,通过实测土壤湿度、温度植被干旱指数(temperature vegetation dryness index,TVDI)验证评价这2种不同尺度的微波土壤湿度数据质量。结果表明:DCT-PLS方法能够充分利用三维时空信息对缺失数据进行重建,重建后土壤湿度具有较高质量;10、1 km 2种尺度的土壤湿度与实测土壤湿度在空间分布上较为吻合,2个示例日期中降尺度土壤湿度与TVDI之间的相关性提高了0.352、0.4264,能够更为准确地反映土壤湿度空间分布细节;通过实测数据、TVDI指数的校验,降尺度前后的AMSR2土壤湿度数据表现出了较高的质量与可靠性。

       

      Abstract: Abstract: Quality and precision of soil moisture data derived from a newly launched sensor, advanced microwave scanning radiometer 2 (AMSR2), needs to be evaluated before its quantitatively application in such fields as hydrological and energy cycle, agricultural management and so on. AMSR2 level 3 soil moisture data estimated based on the Land Parameter Retrieval Model (LPRM) has the highest spatial resolution of 10 km. The purpose of the paper was to reconstruct missing data and evaluate its retrieval precision of AMSR2 level 3. The missing data reconstruction was based on a penalized least square regression with three-dimensional discrete cosine transform (DCT-PLS) method. In order to evaluate the feasibility of the method, AMSR2 data in 2013 from 2 randomly selected locations (Yugan county of Jiangxi Province and Pingdu of Shandong Province) with continuous data originally were used and 20% pixels were given NaN for DCT-PLS-based missing data reconstruction. The reconstructed result was compared with the original one to evaluate the DCT-PLS method. Meanwhile, the AMSR2 data for the whole China on June 1, 2013 was used for the method validation in reconstructing missing data (eg. Anhui, Shanxi, Taiwan, and East China). Moreover, MODIS products within Shanxi area including land surface temperature (MOD11A2), vegetation index (MOD13A2) and surface albedo (MCD43B3) were combined to downscale AMSR2 level 3 soil moistures obtained on Jun. 1, 2013 and Nov.1, 2012 by using a statistical regression method. The retrieved soil moisture with the spatial resolution of 10 and 1 km were evaluated based on the field measurement of 38 stations as well as temperature vegetation drought index (TVDI) computed based on MODIS products. The results showed that: 1) the correlation coefficient (r) of reconstructed and original data was 0.9834 and 0.9557 (P<0.001) in both locations. In spatial distribution, the reconstructed data for the whole China had high correlation with the original data (r=0.9255, P<0.001). The reconstructed data could reveal a reasonable continuous distribution. Hence, the DCT-PLS method was reliable for missing data reconstruction; 2) Though the spatial distribution of AMSR2 soil moisture at the two different spatial scales was consistent with that of the field measurements, the correlations were low on Nov. 1, 2012 and Jun 1, 2013 respectively and they were improved by downscaling. There was a negative correlation between the AMSR2 soil moisture and TVDI at 10 km resolution. Compared with 10 km, the 1 km resolution showed reasonable spatial distribution of soil moisture. The correlation between the soil moisture and TVDI at 10 km resolution was -0.1869, -0.4720 on Jun 1, 2013 and Nov. 1, 2012 respectively, and it increased to -0.5389, -0.8984 at 1 km resolution. Moreover, the downscaled data at 1 km resolution could reflect more spatio-temporal distribution details of soil moisture, which could reduce unreliable estimation for higher soil moisture at 10 km resolution. Therefore, the 1 km downscaled data was more reliable. In sum, the DCT-PLS reconstruction combined with downscale method is good for soil moisture retrieval with a higher spatial resolution for AMSR2 level 3 data.

       

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