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Published： 2018-1 ，
Accepted： 25 July 2017
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张猛, 曾永年. 2018. 融合高时空分辨率数据估算植被净初级生产力. 遥感学报, 22(1): 143–152
Zhang M and Zeng Y N. 2018. Net primary production estimation by using fusion remote sensing data with high spatial and temporal resolution. Journal of Remote Sensing, 22(1): 143–152
植被净初级生产力NPP(Net Primary Production)遥感估算与分析，有赖于高时空分辨率的遥感数据，但目前中高分辨率的遥感数据受卫星回访周期及天气的影响，在中国南方地区难以获取连续时间序列的数据，从而影响了高精度的区域植被净初级生产力的遥感估算。为此，提出一种基于多源遥感数据时空融合技术与CASA模型估算高时空分辨率NPP的方法。首先，利用多源遥感数据，即Landsat 8 OLI数据与MODIS13Q1数据，采用遥感数据时空融合方法，获得了时间序列的Landsat 8 OLI融合数据；然后，基于Landsat 8 OLI时空融合数据，并采用CASA模型，以长株潭城市群核心区为例，进行区域植被NPP的遥感估算。研究结果表明，基于时间序列Landsat融合数据估算的30 m分辨率的NPP具有良好的空间细节信息，且估算值与实测值的相关系数达0.825，与实测NPP数据保持了较好的一致性。
High-precision and rapidly changing Net Primary Production (NPP) monitoring in regions depends on remote sensing data with high spatial and temporal resolution. However
high-quality remote sensing data with high spatial and temporal resolution are difficult to acquire with a single remote sensor. To solve the problem of missing data
we used the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm in blending MODIS and Landsat data. We obtained high-frequency temporal information from the MODIS data and high-resolution spatial information from the Landsat8 OLI data to predict NPP with high spatial and temporal resolution. Using the STARFM model
we obtained the time series data with 30 m spatial resolution and 16 day temporal resolution. Then
the fused time series NDVI data combined with meteorological data were used as inputs to the Carnegie–Ames–Stanford approach model to estimate the NPP in the Chang–Zhu–Tan urban agglomerations. An area with 500×500 pixels was randomly selected from true Landsat images and fused Landsat images. The correlation coefficient and RMSE between the true Landsat8 NDVI and fused Landsat NDVI image were above 0.7 and 0.08
respectively. This result indicates that the true data and fused data maintained good consistency. The NPP estimated with the fused Landsat NDVI data showed better detailed spatial information than that obtained with the MODIS data. The fused NPP data showed distinct boundaries between water
road
and building
whereas the NPP simulated from the MODIS did not. The mean values of the NPP data obtained with the fused Landsat NDVI and MODIS NDVI data were 323.01 and 260.88 gC·m
–2
·a
–1
respectively. The mean value of the NPP from the fused Landsat NDVI data was higher than that of the NPP from the MODIS data due to the spectral difference between the MODIS and Landsat images. To further validate the fused NPP data
we employed the measured value of NPP. The correlation coefficients
RMSEs
and relative errors of the fused NPP and measured NPP were 0.89
4.83 gC·m
–2
·a
–1
and 9.82%
respectively
which indicated that the fused NPP had a good consistency with the measured data. The fused NPP was consistent with the NPP obtained with the MODIS data in terms of pixels; the vegetation coverage accounted for more than 80% of the mixed pixels. Meanwhile
the fused NPP was significantly higher than the NPP estimated from the MODIS data in terms of pixels; the vegetation coverage accounted for less than 20% of the mixed pixels. At the same time
the fused NPP data retained the time features of the time series MODIS data. With the rapid development of regions
remote sensing data from a single date cannot meet the requirements of the monitoring of dynamic vegetation growth in urban areas. A spatial–temporal fusion technique is an effective way to blend images from different sensors for applications that require high resolutions in both time and space. This technique should be able to support time series remote data with high spatial and temporal resolution for NPP monitoring in regional areas. However
due to the rapid expansion of cities and unreasonable planning of regions
vegetation patches become increasingly fragmented with high spatial heterogeneity. Therefore
on the one hand
we should improve the spatial resolution of remote sensing data. On the other hand
we have to solve spatial heterogeneity
which is the focus of our follow-up research.
Landsat 8 OLIMODIS时空融合高时空分辨率净初级生产力遥感估算
Landsat 8 OLIMODISdata fusionhigh spatial and temporalnet primary productionremote sensing estimation
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