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These data represent the research findings from our initial manuscript submitted to the journal CATENA, which includes plant information extraction, identification of key factors influencing plant distribution, as well as the simulation and future prediction of plant NDVI. Our paper is well-suited for the specific subfield of landscape ecology, as it addresses the evolutionary characteristics of early-spring ephemeral plants in the unique desert landscape and their responses to future climate change. We also believe that the study and its findings will be of interest to the readers of your journal. The highlights of the paper are as follows:(1) This study examines the correlation between high temporal resolution remote sensing monitoring and plant phenology, establishing a distribution range identification and NDVI extraction model for early-spring ephemeral plants based on a life-cycle approach, and validating the model using species distribution point data and high spatial resolution Landsat series data.(2) In developing the NDVI simulation model for ephemeral plants, we integrated multi-source environmental data, incorporating GIS spatial analysis and fully utilizing the factor contribution module of the RF (Random Forest) algorithm and the prediction module of the CNN (Convolutional Neural Network) model, thereby perceiving and extracting local features between ephemeral plants and climate variables.(3) We conducted priority planning for the biodiversity conservation of the unique plant landscape of early-spring ephemeral plants in the Gurbantünggüt Desert.The research findings of this study indicate that "it is speculated that these plants will migrate northwestward in the 2050s (to higher altitude and higher latitude areas), forming a second region in the center of the desert that is conducive to their survival and reproduction," which aligns with the inference that "due to global warming, biodiversity will migrate to higher altitudes and higher latitudes."This study comprehensively considers the relationship between remote sensing and plant phenology in the remote sensing extraction method for the unique plant landscape of the desert (early-spring ephemeral plants). It also employs a combination model of RF and CNN to predict future plant distribution and trends. This approach can provide new ideas and suggestions for related research on plant identification and conservation globally.

这些数据代表了我们提交给CATENA期刊的初始手稿中的研究成果，包括植物信息提取、影响植物分布关键因素的识别，以及植物NDVI的模拟与未来预测。我们的论文非常适合景观生态学的特定子领域，因为它探讨了独特荒漠景观中早春短命植物的演化特征及其对未来气候变化的响应。我们也相信本研究及其成果将引起贵刊读者的兴趣。论文的亮点如下：(1) 本研究探讨了高时间分辨率遥感监测与植物物候之间的相关性，基于生命周期方法建立了早春短命植物的分布范围识别与NDVI提取模型，并利用物种分布点数据和高空间分辨率Landsat系列数据对模型进行了验证。(2) 在构建短命植物NDVI模拟模型时，我们整合了多源环境数据，结合了GIS空间分析，并充分利用了随机森林（Random Forest）算法的因子贡献模块和卷积神经网络（Convolutional Neural Network）模型的预测模块，从而感知并提取了短命植物与气候变量之间的局部特征。(3) 我们对古尔班通古特沙漠早春短命植物这一独特植物景观的生物多样性保护进行了优先规划。本研究的结果表明：'推测这些植物将在2050年代向西北迁移（至更高海拔和更高纬度地区），在沙漠中心形成第二个利于其生存繁殖的区域'，这与'由于全球变暖，生物多样性将向更高海拔和更高纬度迁移'的推论一致。本研究在荒漠独特植物景观（早春短命植物）的遥感提取方法中，综合考虑了遥感与植物物候之间的关系；同时采用RF与CNN的组合模型预测未来植物分布及趋势。该方法可为全球范围内植物识别与保护的相关研究提供新思路与建议。