基于GF-1/WFV数据的三江源草地月度NPP反演研究

准确认识三江源植被生产力月度尺度的时空格局变化,对三江源畜牧业生产以及生态保护政策制定具有重要意义,可稳定获取的重访周期为4 d的16 m分辨率GF-1/WFV数据使中等空间分辨率的月度NPP产品生产成为可能。本文建立了一套以GF-1/WFV为基本数据源的中等空间分辨率草地月度NPP估算技术方法,并评估了其在三江源地区应用的可行性。在黄河源区玛多县的实验表明以GF-1/WFV为基础,以MODIS13Q1数据为补充,可以获得覆盖全区的中等空间分辨率月度NDVI数据,据其反演得到的草地NPP,地面验证精度在70%以上,优于MODIS NPP产品精度,且能更为详细地反映草地生产力变化的空间差异,在青海三江源地区利用GF-1/WFV数据生产中等空间分辨率的草地月度NPP产品是可行的。

Monthly Net Primary Production Estimation of Grassland in the Three-River Headwater Region Using GF-1/WFV Data

This paper presented a method of monthly net primary production (NPP) estimation of grassland in the Three-River Headwater Region (TRH) based on GF-1/WFV data. First, a preprocessing of radiometric calibration and atmospheric correction is applied on GF-1/WFV 1A data by ENVI software. Secondly, geocoding is processed by Rational Function Model (RFM) with GF-1/WFV RPC (Rational Polynomial Coefficient) and the orthophoto images with high georeferenced accuracy are conducted after block adjustment. The processed GF-1/WFV data is comparable in space and time. Then, cloud and cloud shadow per scene are detected using Multi-feature Combined method; NDVI is retrieved based on GF-1/WFV image and monthly NDVI is generated by Maximum Value Composite (MVC) method. The values of pixels still affected by cloud or cloud shadow cover in monthly NDVI mosaic are extrapolated using linear regression using least square method based on MODIS 13Q1 NDVI. Finally monthly NPP of grassland is calculated based on Carnegie-Ames-Stanford Approach (CASA) with monthly NDVI and other variables including monthly total precipitation, monthly averaged temperature and monthly total solar radiation. A case study was conducted in Maduo country and results showed that: (1) reliable monthly NDVI data at medium spatial resolution can be obtained based on GF-1/WFV under the support of MODIS 13Q1 product; (2) The accuracy of estimated grassland NPP based on GF-1/WFV was over 70% based on field data validation, which is better than MODIS 17A3 NPP production and the former can occupied more detailed NPP spatial variation. Monthly NPP can be successfully estimated based on GF-1/WFV under the support of MODIS 13Q1 product in TRH. However, some details need to be improved for further study: (1) more area of cloud and cloud shadow in images, lower precision of the extrapolated NDVI and the error of simulated NPP may be greater; (2) in low temperature, NPP is 0 in CASA, which overestimates the grassland NPP because underground root of grassland is still alive in TRH in winter and NPP should be negative; (3) monthly NDVI generated by MVC represents the best growth situation of vegetation in the period, not the average one, which may overestimates NPP. Besides, mapping accuracy of vegetation type will also affect the simulated NPP result precision; (4) field data collection is difficulty due to the study area is in remote area of high altitude, so the current ground data is not enough to cover all months in growth season and the uncertainty of this method remains to be further tested.