青藏高原地区高分遥感雪面反照率的模拟与验证

Snow albedo is a critical geographical parameter that is widely used in studies of land-surface process models, hydrological models, and general circulation models, and its temporal and spatial variation is closely related to the global climate change and regional weather system due to the albedo important positive and negative feedback mechanism. The snow broadband albedo is defined as the ratio of the surface upwelling to the downward flux of shortwave solar radiation over the upward semi-hemispherical space. In this project, we will study on the inversion and validation of the snow broadband albedo using domestic GF remote sensing on the Qinghai-Tibet plateau in China, and relying on field stations of the Chinese Academy of Sciences and meteorological stations, we will carry out the field observations synchronized with remote sensing, and intend to reduce noise using the low-pass filter method and posterior distributions to get the true value of snow reflectance and albedo. By means of the high precision DEM (eg. 1 arc resolution of SRTM) and the radiative transfer equation including BRDF properties using MODTRAN model, we will retrieve the snow narrowband albedo using the GF remote sensing in the complex mountain area. The narrow-to-broadband conversions and BRDF angular modeling focus on band and angular integrations of the surface spectral BRF, respectively. The narrow-to-broadband conversion is a fundamental algorithm for estimating surface broadband albedo from spectral albedo or BRF, which is widely applied with the single-angular observed platform when assuming lambertian surface. Based on this idea of narrow-to-broadband conversions, we will apply the Romberg numerical integral and the Boolean formula method to approximate the snow narrowband albedo from observations and retrieval, respectively. The initial value of the snow broadband albedo contains the estimated uncertain error, and jointing the true value from ground observed, the optimal simulated snow broadband albedo and its error envelopes will be iterated and controlled by coupling the PID control system. And we will validate the precision and estimate the uncertainty error to the retrieved snow broadband albedo of GF remote sensing in complex mountainous areas. Then, we will design the field temporal and spatial sampling on the Bayesian maximum entropy method by snow surface heterogeneity, which will be expressed by the snow surface roughness. Overall, this project will develop the subject of snow and ice science, promote the application of GF remote sensing in complex mountainous terrain, and provide theoretical evidence for domestic production of high-precision quantitative remote sensing products.

雪面反照率对全球气候变化有重要的正负反馈调节作用，是陆面过程模型、气候模型与水文模型的关键参数之一。以我国青藏高原地区为研究区，依托野外台站与气象台站开展野外观测与遥感同步观测试验，拟以低通滤波去噪方法和后验分布获得雪面观测反射率与反照率的真实值；借助高精度DEM与MODTRAN BRDF辐射传输方程反演复杂山区高分遥感雪面窄波段反照率；重点研究以龙贝格积分与布尔公式等数值积分分别逼近雪面观测与反演的窄波段反照率，获得雪面宽波段反照率的初值，进一步耦合PID控制方法迭代并控制模拟值的误差估计，得到雪面宽波段反照率的最优模拟值和误差边界；同时将以雪面粗糙度来表征雪面异质性的方式设计贝叶斯最大熵地统计学的空间采样，实现高分遥感雪面反照率在复杂山区的真实性检验与不确定性估计。本项目的实施将为推动国产高分遥感在雪冰科学中的应用、复杂山区地形的应用以及高精度定量化遥感产品的生产提供基础性研究依据。

雪面反照率对全球气候变化有重要的正负反馈调节作用，是陆面过程模型、气候模型与水文模型的关键参数之一。以我国青藏高原地区为研究区，依托野外台站与气象台站开展野外观测与遥感同步观测试验，以低通滤波去噪方法和后验分布获得雪面观测反射率与反照率的真实值。（1）瑞利散射是生活中重要而又常见的自然现象之一，瑞利光学厚度是衡量瑞利散射强度的重要指标。本项目总结了现有瑞利光学厚度两类模拟模型的优缺点。随着全球气候变化中CO2浓度已突破400ppm，近似数值模型因受到大气温度和CO2浓度为300ppm的背景条件的限制会导致部分模型误差的增加。通过对不同高度和纬度的九个试验地点，以理论离散模型为基础，模拟特定大气条件下(P0=1atm, T=15℃, CO2=400ppm)的瑞利光学厚度。通过拟合分析得出，瑞利散射强度与波长的4.529次方成反比，且在紫外-蓝波段CO2浓度对瑞利光学厚度的贡献在10-4~10-3数量级之间。以理论离散模型为主要算法模拟瑞利光学厚度将能更好的提高模型的自适应性并减少模型本身带来的误差；并通过该模拟结果可进一步获得该大气条件下的计算简单方便的数值模拟模型。（2）地形是影响不同尺度空间太阳辐射格局的关键参数，太阳天顶角的逐日变化与季节性变化成为了影响太阳辐射变化的直接原因。由于地形的调制，特别是在复杂地形中对入射的空间太阳辐射进行精确的量化。随着太阳天顶角的增加，我们从长期的野外积雪观测试验中也证实了雪面反照率在所有波长都会增加。根据大气质量数限定了有效太阳天顶角在75度以内的值域，也进一步探讨了有效太阳天顶角对太阳辐射通量和遥感观测的影响，也讨论了雪面宽波段反照率在复杂山区地形下的最优模拟值和误差边界，构建了逐日最小太阳天顶角随日数变化的函数。本项目的实施推动了国产高分遥感在雪冰科学中的应用、复杂山区地形的应用以及高精度定量化遥感产品的生产提供基础性研究依据。