中文摘要：

利用2015-2016年8月采集的黄河源区草地生物量数据和MODIS卫星遥感资料,结合农业多光谱相机（agricultural digital camera,ADC）获取的植被指数数据,比较分析3种_（ADC）植被指数（NDVI_（ADC）、SAVI_（ADC）和GNDVI_（ADC））与野外实测草地地上生物量（above-ground biomass,AGB）数据的相关性,筛选出适合构建草地AGB反演模型的_（ADC）植被指数;结合MODIS NDVI（记作NDVIMOD）构建草地地上生物量反演模型,采用留一法交叉验证方法评价各模型精度,确立适宜模拟研究区草地AGB的最优模型;并利用NDVI_（ADC）校正NDVI_（MOD）,获得高分辨率、高精度的草地AGB遥感监测改进模型。结果表明,1）基于_（ADC）获取的3种植被指数中,NDVI_（ADC）与高寒草地地上生物量关系最为密切,其次为SAVI_（ADC）,拟合效果最差的是GNDVI_（ADC）;2）基于NDVI_（ADC）建立的草地AGB监测模型的精度（RMSEP介于383.55~393.18kg DW/hm2;r范围为0.65~0.66）远高于NDVI_（MOD）的模型精度（RMSEP介于421.08~427.00kg DW/hm~2;r范围为0.55~0.58）,NDVI_（ADC）反演得到的草地AGB更接近于黄河源区草地实际生物量,且相较于NDVI_（ADC）,NDVI_（MOD）的样本值整体偏高;3）在NDVI_（ADC）构建的4类模型中,线性和乘幂模型模拟研究区草地AGB的能力较好,但线性模型精度更高（y=3248.93×NDVI_（ADC）-305.59,RMSEP=383.55kg DW/hm~2,r=0.66）,该模型为黄河源区草地生物量的估测提供了一个新型且易操作的方法;4）NDVI_（ADC）与NDVIMOD相关性较高,利用NDVI_（ADC）校正NDVI_（MOD）可以改进草地AGB遥感反演模型,优化模型为y=2455.54×NDVI_（MOD）-301.69。该模型可在大尺度范围内估测黄河源区的草地生物量,且模型精度接近于地表测量法的监测精度。

英文摘要：

We collected grassland biomass and _（MOD）IS satellite remote sensing data,and calculated vegetation indices（VIs）from data obtained by an agricultural digital camera（ADC）in the Yellow River Headwater Region（YRHR）in August of 2015-2016.We explored the correlations between each of three ADC vegetation indices（NDVI_（ADC）,SAVI_（ADC）,and GNDVI_（ADC））and field-measured grassland above-ground biomass（AGB）,and selected the optimal ADC vegetation index to construct an AGB inversion model.Grassland AGB inversion models based on ADC vegetation indices and MODIS NDVI（denoted as NDVI_（MOD））were constructed,and the accuracy of each model was evaluated by leave-one-out cross validation（LOOCV）to identify the optimal grassland AGB monitoring model.The NDVI_（ADC） was used to correct the NDVI_（MOD）to obtain the optimized grassland AGB model with high resolution and accuracy.The results showed that：1）among the three VIs-based ADC indices,the NDVI_（ADC）was most closely related to the AGB of alpine grassland,followed by SAVI_（ADC）and GNDVI_（ADC）.2）The NDVI_（ADC）-based AGB monitoring model（RMSEP：383.55-393.18 kg DW/ha;r：0.65-0.66）was more accurate than the NDVI_（MOD） model（RMSEP：421.08-427.00 kg DW/ha;r：0.55-0.58）.Therefore,the grassland AGB inversion value from the NDVI_（ADC）-based model was much closer to the actual grassland AGB in YRHR,and the sampling values of NDVI_（MOD） were higher than those of NDVI_（ADC）as a whole.3）Among the four models based on NDVI_（ADC）,the linear and power models showed better performance in grassland AGB simulations.The linear model（y=3248.93×NDVI_（ADC）-305.59,RMSEP=383.55 kg DW/ha,r=0.66）was more accurate than the power model,and the linear model provided a novel and simple method to estimate grassland biomass in the study area.4）There was a strong correlation between NDVI_（ADC）and NDVI_（MOD）;therefore,we could obtain an optimized grassland AGB model by using NDVI_（ADC?