青藏高原冬季枯草生物量遥感估算

草地生物量是高寒草甸生态系统功能状态与生产效益的基础指标。然而，青藏高原冬半年非生育期，包括生物量在内的牧草要素观测全面中止，使得冬季成为牧草观测的一个空白期。通过2020年8月—2021年4月在青海海北高寒草地逐月牧草参数与高光谱野外同步观测试验，进行了牧草不同月份、不同衰减状态下生物量、表观状态、光谱特征的观测及其动态变化过程分析。结果表明，高寒冬季牧草生物量总体呈迅速下降和相对稳定两个阶段。8—10月牧草生物量处于迅速衰减下降阶段，牧草生物量由8月的9225 kg/hm²急剧下降至10月的3536 kg/hm²,降幅近160%,11月—次年4月则进入总体稳定阶段。利用衰减过程牧草生物量与反射光谱间关系，提出了一种修订的归一化枯草植被指数(R-DGVI),该指数在低覆盖与高覆盖植被区均表现出较好的枯草识别能力，具有相比NDVI更强的枯草识别能力与更宽的阈值范围。在此基础上，建立的中分辨率成像光谱仪(MODIS)卫星冬季枯草生物量遥感估算模型R²达到0.5627(P<0.01),进一步，通过给出5个等级枯草生物量...

Estimation of wilted grass biomass by satellite remote sensing data in winter on Qinghai-Tibet Plateau

Grassland biomass is a basic index of ecosystem function and production efficiency of alpine meadow ecosystem. However, during non-growing season of the half of the year in winter takes a gap of grass observations period on Qinghai-Tibet Plateau, the work of grass observation which is completely suspended. This study based on monthly synchronous field observation on grass parameters and hyperspectral in alpine grassland of Haibei, Qinghai, from August 2020 to April 2021, observations of biomass, apparent state and spectral characteristics of grasses in different months and at different states of attenuation, and analysis of their dynamic processes. The results showed that wilted grass biomass in winter is generally in two stages:rapid decline and relative stability, a rapid decline period from August to October with the biomass was decreased sharply from 9225 kg/hm² in August to 3536 kg/hm² in October, there is a distinct decrement of nearly 160% compare to the biomass in August, and then, a gently and less variation period performed on the time of November to April of the next year. A Revised Dead Grass Vegetation Index (R-DGVI) was proposed based on the relationship between ground observed grass biomass and reflectance spectrum, which showed a good ability for wilted grass identification especially in area of lower or higher vegetation cover, compare to Normalized Difference Vegetation Index, it represented the stronger responsive and wider threshold range on wilted grass biomass monitoring. Furthermore, a remote sensing estimation model for winter wilted grass biomass using MODIS satellite data was established with R² reached at 0.5627 (P<0.01), then, a five-level classified system related to R-DGVI value at different grades grassland was provided, which was suitable for remote sensing monitoring on wilted grass biomass in winter. The results will provide a further information to grass change pattern in winter and some basic methods and technical for biomass monitoring.