全球陆地表面物候的微波极化和梯度比(MPGR)

M. Boori, R. Ferraro
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引用次数: 18

摘要

卫星生成的亮度温度在很大程度上受土壤湿度和植被覆盖的影响。微波极化梯度比(MPGR)是利用EOS先进微波扫描辐射计(AMSR-E)等传感器表征地表物候特征的有效指标。MPGR结合微波梯度比和极化比来确定地表特征(即裸露土壤/发育、冰和水),以及在云覆盖条件下,这些信息无法通过光学遥感数据获得。本研究使用HDF Explorer、Matlab和ArcGIS软件对AMSR-E影像中的像素纬度、经度和BT信息进行处理。本文利用AMSR-E BT在6.9、10.7、18.7、23.8、36.5和89.0GHz波段的极化和梯度比识别了17种土地覆盖类型。MPGR越小,植被越密集,混合植被、退化植被、裸土/发达植被和冰水植被的MPGR逐渐增大。这些信息可以帮助改善用于天气预报应用的陆地表面物候特征,即使在多云和降水条件下也会干扰其他传感器。
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Microwave Polarization and Gradient Ratio (MPGR) for Global Land Surface Phenology
Satellite-generated brightness temperatures (BT) are largely influenced by soil moisture and vegetation cover. Microwave polarization and gradient ratio (MPGR) is an effective indicator for characterizing the land surface phenology from sensors like EOS Advanced Microwave Scanning Radiometer (AMSR-E).The MPGR combines the microwave gradient ratio with polarization ratio to determine surface characteristics (i.e., bare soil/developed, ice, and water) and under cloud covered conditions when this information cannot be obtained using optical remote sensing data. This investigation uses the HDF Explorer, Matlab, and ArcGIS software to process the pixel latitude, longitude, and BT information from the AMSR-E imagery. This paper uses the polarization and gradient ratio from AMSR-E BT for 6.9, 10.7, 18.7, 23.8, 36.5, and 89.0GHz to identify seventeen land cover types. A smaller MPGR indicates dense vegetation, with the MPGR increasing progressively for mixed vegetation, degraded vegetation, bare soil/developed, and ice and water. This information can help improve the characterization of land surface phenology for use in weather forecasting applications, even during cloudy and precipitation conditions which often interferes with other sensors.
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