Time-series Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data have been widely used for large area crop mapping.However,the temporal crop signatures generated from these data were always accompanied by noise.In this study,a denoising method combined with Time series Inverse Distance Weighted (T-IDW) interpolating and Discrete Wavelet Transform (DWT) was presented.The detail crop planting patterns in Hebei Plain,China were classified using denoised time-series MODIS NDVI data at 250 m resolution.The denoising approach improved original MODIS NDVI product significantly in several periods,which may affect the accuracy of classification.The MODIS NDVI-derived crop map of the Hebei Plain achieved satisfactory classification accuracies through validation with field observation,statistical data and high resolution image.The field investigation accuracy was 85% at pixel level.At county-level,for winter wheat,there is relatively more significant correlation between the estimated area derived from satellite data with noise reduction and the statistical area (R2 = 0.814,p < 0.01).Moreover,the MODIS-derived crop patterns were highly consistent with the map generated by high resolution Landsat image in the same period.The overall accuracy achieved 91.01%.The results indicate that the method combining T-IDW and DWT can provide a gain in time-series MODIS NDVI data noise reduction and crop classification.
本文提出利用中国第1颗可操作性静止气象卫星风云2号C星(FY-2C)数据结合中等分辨率航天成像光谱仪MODIS产品估算河北灌溉农田实际蒸散量(ET)的方法,其中FY-2C的第1、2波段用于反演区域地表温度,再结合16 d MODIS合成的植被指数产品(MOD13),得到地表温度与植被指数的三角空间分布图(Ts-NDVI)。通过Ts-NDVI空间分布的关系,利用改良三角算法得到区域的蒸发比(EF)。最后结合MODIS地表反射率产品MCD43估算得到的日净辐射量,根据能量平衡计算得到该地区的日实际蒸散量。模型结果与地表Lysimeter观测数据比较,显示该模型估算得到的蒸发比和日蒸散量结果较为合理,误差在可接受范围。此外,FY-2C用于估算地表ET,其时间分辨率具有较强的优势,从而为获得多幅无云蒸散图提供了有利条件。
