Watershed scale vegetation net primary productivity remote sensing fine inversion method

Abstract

The invention discloses a watershed scale vegetation net primary productivity remote sensing fine inversion method. The method comprises the following steps: acquiring a vegetation type distribution diagram, remote sensing image data and site meteorological data of a measured area; interpreting the vegetation type distribution diagram; calculating the remote sensing image data to obtain normalized vegetation index time sequence data; carrying out spatial interpolation on the station meteorological data to obtain raster data with the same resolution as the normalized vegetation index time sequence data; improving the resolution of the data by using a fully constrained least square mixed pixel decomposition and sub-pixel spatial gravitation model; configuring static parameters; and using the improved NPP estimation model to invert the vegetation net primary productivity NPP. On the basis of existing low-resolution remote sensing data, the method for improving the product resolution by means of sub-pixel mapping is provided, the vegetation net primary productivity is accurately estimated, the estimation precision of the net primary productivity is improved, and remote sensing fine inversion of the vegetation net primary productivity in the watershed scale is achieved.

Description

technical field [0001] The invention relates to the technical field of remote sensing, in particular to a fine inversion method for remote sensing of net primary productivity of watershed vegetation. Background technique [0002] Vegetation net primary productivity (NPP) not only directly reflects the production capacity of vegetation communities under natural environmental conditions, but also is the main factor for determining ecosystem carbon sources and carbon sinks and regulating ecological processes. Vegetation net primary productivity has been widely used in land use assessment, regional ecological planning, vegetation growth monitoring, crop yield estimation, soil and water erosion assessment, ecological benefit assessment, etc. [0003] The existing net primary productivity estimation models are roughly divided into four categories: climate productivity models, physiological and ecological process models, ecological remote sensing coupling models, and light energy u...

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Problems solved by technology

However, the area of ​​river basins is generally small, and more refined NPP inversion is required. However, most of the existing net primary productivity products have low resolution, and it is impossible to conduct fine analysis on the basin scale. At the same time, there is a lack of NPP remote sensing using existing remote sensing data. Therefore, there is an urgent need for a remote sensing fine inversion method of vegetation net primary productivity applicable to the watershed scale

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