Downscaling AMSR-2 Soil Moisture Data With Geographically Weighted Area-to-Area Regression Kriging
Downscaling AMSR-2 Soil Moisture Data With Geographically Weighted Area-to-Area Regression Kriging
复制标题
使用地理加权区域间回归克里金法缩小 AMSR-2 土壤湿度数据
DOI：
10.1109/tgrs.2017.2778420
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发表时间：
2018-04
影响因子：
8.2
通讯作者：
Atkinson Peter M.
中科院分区：
文献类型：
--
作者：
Jin Yan;Ge Yong;Wang Jianghao;Chen Yuehong;Heuvelink Gerard B. M.;Atkinson Peter M.
土壤水分在地表能量平衡和水分循环中起着重要作用。微波遥感已被广泛应用于SM的估算。然而，由于空间分辨率较低，这类数据的应用受到了普遍的限制。降尺度方法已被应用于从原始数据中预测高分辨率的SM。通常，SM在空间上是高度可变的，因此，这种局部空间异质性应该在降尺度过程中被考虑。本文提出了一种结合地理加权回归和面积-面积克立格法的混合地统计学方法，用于微波SM产品的降尺度。地理加权面积-面积回归克里金法(GWATARK)结合了精细空间分辨率的光学遥感数据和粗空间分辨率的被动微波遥感数据，因为两者的结合在绘制精细空间分辨率的近地表SM方面具有很大的潜力。GWATARK方法是通过从每天25公里分辨率的AMSR-2 SM产品中产生1公里分辨率的缩小SM来进行评估的。GWATARK方法和两种基准方法对三组协变量的缩尺预测与现场观测的比较表明，GWATARK方法比两种基准方法更准确。平均而言，均方根误差值下降了20%。使用更多的协变量进一步提高了缩小尺度预测的准确性，特别是在使用地形校正的地表温度和植被-温度条件指数协变量时。
Soil moisture (SM) plays an important role in the land surface energy balance and water cycle. Microwave remote sensing has been applied widely to estimate SM. However, the application of such data is generally restricted because of their coarse spatial resolution. Downscaling methods have been applied to predict fine-resolution SM from original data with coarse spatial resolution. Commonly, SM is highly spatially variable and, consequently, such local spatial heterogeneity should be considered in a downscaling process. Here, a hybrid geostatistical approach, which integrates geographically weighted regression and area-to-area kriging, is proposed for downscaling microwave SM products. The proposed geographically weighted area-to-area regression kriging (GWATARK) method combines fine-spatial-resolution optical remote sensing data and coarse-spatial-resolution passive microwave remote sensing data, because the combination of both information sources has great potential for mapping fine-spatial-resolution near-surface SM. The GWATARK method was evaluated by producing downscaled SM at 1-km resolution from the 25-km-resolution daily AMSR-2 SM product. Comparison of the downscaled predictions from the GWATARK method and two benchmark methods on three sets of covariates with in situ observations showed that the GWATARK method is more accurate than the two benchmarks. On average, the root-mean-square error value decreased by 20%. The use of additional covariates further increased the accuracy of the downscaled predictions, particularly when using topography-corrected land surface temperature and vegetation–temperature condition index covariates.
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影响因子：
2.6
作者：
Harris, P.;Fotheringham, A. S.;Charlton, M.
通讯作者：
Charlton, M.
影响因子：
3.4
作者：
Chauhan, NS;Miller, S;Ardanuy, P
通讯作者：
Ardanuy, P
影响因子：
4.3
作者：
P. Srivastava;Dawei Han;M. Ramirez;T. Islam
通讯作者：
P. Srivastava;Dawei Han;M. Ramirez;T. Islam
影响因子：
6.1
作者：
Kerry R;Goovaerts P;Rawlins BG;Marchant BP
通讯作者：
Marchant BP
影响因子：
8.2
作者：
Qunming Wang;W. Shi;P. Atkinson;E. Pardo‐Igúzquiza
通讯作者：
Qunming Wang;W. Shi;P. Atkinson;E. Pardo‐Igúzquiza