1）在山区，由于复杂的地形地质背景条件，在降雨、融雪、地震和人类工程活动等外界因子触发下，极易发生滑坡，导致生命财产损失和自然环境的破坏。为了满足工程场地建设的安全性、土地利用规划的合理性和灾害减缓的迫切性需求，需要展开区域滑坡敏感性评价。当利用多种不同的方法得到多个不同评价结果时，如何有效的将这些结果进行组合以得到最优的预测是当前仍未很难解决的一个技术难题，在确定某个区域滑坡敏感性评价的最优策略和最佳方法的操作执行方面仍然十分欠缺。2）利用传统经典的多元分类技术，通过对模型结果评估和误差量化，将最优评价模型进行组合，快速实现区域滑坡敏感性高质量评价。源代码基于R语言软件平台编写，用户需要单独准备一个本地文件夹，用来读取和储存软件运行结果，用户需要记住文件夹储存路径并在软件源代码中进行相应的设置。3）源代码设计了两种不同的模式来展示模型运行结果，以文本和图形格式的标准格式分析结果输出和需要空间数据并以标准地理格式展示的地理空间模式，4）适用于所有对滑坡风险评价工作感兴趣的人群。该软件能够为大专院校经验丰富的科研人员高效使用，也可以被国土环境规划、管理领域的政府人员和公益组织方便快捷、正确可靠的获取滑坡敏感性分级结果。可服务于地区土地利用规划，灾害风险评价与管理，极端诱发事件（地震或降雨等）下的灾害应急，以及对滑坡监测设备的遴选和预警网络的合理有效布置和运行具有重大的现实指导意义，在滑坡发育严重的地区都可以推广应用
杨仲康
1）数据内容：本数据集为青藏高原东南三江流域滑坡灾害数据；2）数据来源及加工方法：本数据集系北京工业大学戴福初利用谷歌地球独立解译完成；采用遥感解译-现场验证-再解译-再验证等方法，经过7次系统解译最终形成本数据文件，累计对超过5000处滑坡开展了现场验证，具有较高的精度；4）本数据对青藏高原东南三江流域水能资源开发、交通工程建设、地质灾害评价等方面具有广阔的应用前景。
戴福初
Accurate estimation of the gross primary production (GPP) of terrestrial vegetation is vital for understanding the global carbon cycle and predicting future climate change. Multiple GPP products are currently available based on different methods, but their performances vary substantially when validated against GPP estimates from eddy covariance data. This paper provides a new GPP dataset at moderate spatial (500 m) and temporal (8-day) resolutions over the entire globe for 2000–2016. This GPP dataset is based on an improved light use efficiency theory and is driven by satellite data from MODIS and climate data from NCEP Reanalysis II. It also employs a state-of-the-art vegetation index (VI) gap-filling and smoothing algorithm and a separate treatment for C3/C4 photosynthesis pathways. All these improvements aim to solve several critical problems existing in current GPP products. With a satisfactory performance when validated against in situ GPP estimates, this dataset offers an alternative GPP estimate for regional to global carbon cycle studies.
张尧
Satellite-retrieved solar-induced chlorophyll fluorescence (SIF) has shown great potential to monitor the photosynthetic activity of terrestrial ecosystems. However, several issues, including low spatial and temporal resolution of the gridded datasets and high uncertainty of the individual retrievals, limit the applications of SIF. In addition, inconsistency in measurement footprints also hinders the direct comparison between gross primary production (GPP) from eddy covariance (EC) flux towers and satellite-retrieved SIF. In this study, by training a neural network (NN) with surface reflectance from the MODerate-resolution Imaging Spectroradiometer (MODIS) and SIF from Orbiting Carbon Observatory-2 (OCO-2), we generated two global spatially contiguous SIF (CSIF) datasets at moderate spatiotemporal (0.05∘ 4-day) resolutions during the MODIS era, one for clear-sky conditions (2000–2017) and the other one in all-sky conditions (2000–2016). The clear-sky instantaneous CSIF (CSIFclear-inst) shows high accuracy against the clear-sky OCO-2 SIF and little bias across biome types. The all-sky daily average CSIF (CSIFall-daily) dataset exhibits strong spatial, seasonal and interannual dynamics that are consistent with daily SIF from OCO-2 and the Global Ozone Monitoring Experiment-2 (GOME-2). An increasing trend (0.39 %) of annual average CSIFall-daily is also found, confirming the greening of Earth in most regions. Since the difference between satellite-observed SIF and CSIF is mostly caused by the environmental down-regulation on SIFyield, the ratio between OCO-2 SIF and CSIFclear-inst can be an effective indicator of drought stress that is more sensitive than the normalized difference vegetation index and enhanced vegetation index. By comparing CSIFall-daily with GPP estimates from 40 EC flux towers across the globe, we find a large cross-site variation (c.v. = 0.36) of the GPP–SIF relationship with the highest regression slopes for evergreen needleleaf forest. However, the cross-biome variation is relatively limited (c.v. = 0.15). These two contiguous SIF datasets and the derived GPP–SIF relationship enable a better understanding of the spatial and temporal variations of the GPP across biomes and climate.
张尧
该数据集是基于16个动态全球植被模式（TRENDY v8）在S2情景下（CO2+Climate）模拟的GPP，表征生态系统总初级生产力。数据来源于Le Quéré et al. (2019)，具体信息和方法参见文章。源数据范围为全球，本数据集选取了青藏高原区域，空间上用最近邻方法插值到0.5度，时间上保持了原有的月尺度。该数据集是标准的模型输出数据，常被用作评定总初级生产力的时间和空间格局，且与其它遥感观测、通量观测等数据进行比较和参考，具有实际意义和理论价值。
Stephen Sitch
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