Remote estimation of K-d (PAR) using MODIS and Landsat imagery for turbid inland waters in Northeast China
Remote estimation of K-d (PAR) using MODIS and Landsat imagery for turbid inland waters in Northeast China
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DOI：
10.1016/j.isprsjprs.2016.11.010
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发表时间：
2017
影响因子：
12.7
通讯作者：
K. Song;Jianhang Ma;Z. Wen;C. Fang;Y. Shang;Ying Zhao;Ming Wang;Jia Du
中科院分区：
文献类型：
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作者：
K. Song;Jianhang Ma;Z. Wen;C. Fang;Y. Shang;Ying Zhao;Ming Wang;Jia Du
光合有效辐射（PAR）的光能利用率是影响水生生态系统光合作用的主要因素之一。PAR域（Kd（PAR））中的光衰减的常规测量仅代表小面积的水体。根据水体离体辐亮度与Kd（PAR）的关系，利用光学遥感图像可以监测大面积水体的Kd（PAR）。本研究于2015年4月至9月对东北地区20多个湖泊（或水库）进行了6次实地调查。利用Landsat/TM/ETM+/OLI和MODIS逐日地表反射率数据（MOD 09 GA ~ 500 m，波段1-7）建立了区域尺度的Kd（PAR）经验反演模型。通过多元逐步回归分析，将波段差（红-蓝）和波段比（NIR/Red）用于Landsat影像建模，将波段差（红-蓝）和波段比（红/蓝）用于MODIS影像建模。通过10次10倍交叉验证评价两种模型的准确性。结果表明，该模型表现良好的Landsat（R2= 0.83，RMSE = 0.95，MRE = 0.33），和中分辨率成像光谱仪（R2= 0.86，RMSE = 0.91，MRE = 0.19）图像。然而，由MODIS得到的Kd（PAR）略高于Landsat估算的Kd（斜率= 1.203，R2= 0.972）。通过Kd（PAR）估计和回归分析（斜率= 1.044，R2= 0.966）验证了MODIS逐日（MYD 09 G A）和8天复合反射率（MYD 09 A1）数据之间模型性能的一致性。最后，中国东北地区的Kd（PAR）的时空分布表明，特定的地理特征以及气象变化会影响Kd（PAR）的校准。具体而言，我们发现，风速和藻类水华是主要的决定因素的Kd（PAR）在呼伦湖（2050 km 2）和兴凯湖（4412 km 2）。
Light availability for photosynthetically active radiation (PAR) is one of the major factors governing photosynthesis in aquatic ecosystems. Conventional measurements of light attenuation in the PAR domain (Kd(PAR)) is representative for only small areas of water body. Remotely sensed optical imagery can be utilized to monitor Kd(PAR) in large areas of water bodies, based on the relationship between water leaving radiance and Kd(PAR). In this study, six field surveys were conducted over 20 lakes (or reservoirs) across Northeast China from April to September 2015. In order to derive the Kd(PAR) at regional scale, the Landsat/TM/ETM+/OLI and the MODIS daily surface reflectance data (MOD09GA ∼500 m, Bands 1–7) were used to establish empirical inversion models. Through multiple stepwise regression analysis, the band difference (Red-Blue) and band ratio (NIR/Red) were used in Landsat imagery modeling, and the band difference (Red-Blue) and ratio (Red/Blue) were used in MODIS imagery modeling. The accuracy of the two models was evaluated by 10-fold cross-validation in ten times. The results indicate that the models performed well for both Landsat (R2= 0.83, RMSE = 0.95, and MRE = 0.33), and MODIS (R2= 0.86, RMSE = 0.91, and MRE = 0.19) imagery. However, the Kd(PAR) derived by MODIS is slightly higher than that estimated by Landsat (slope = 1.203 and R2= 0.972). Consistency of model performance between the MODIS daily (MYD09G A) and the 8-Day composite reflectance (MYD09A1) data was verified by Kd(PAR) estimations and regression analysis (slope = 1.044 and R2= 0.966). Finally, the spatial and temporal distribution of Kd(PAR) in Northeast China indicated that specific geographical characteristics as well as meteorological alterations can influence Kd(PAR) calibrations. Specifically, we have revealed that the wind speed and algal bloom are the major determinants of Kd(PAR) in Lake Hulun (2050 km2) and Xingkai (4412 km2).