遥感GPP模型在中亚干旱区4个典型生态系统的适用性评价

总初级生产力（GPP）是全球生态系统碳循环的重要组成部分，对全球气候变化有重要影响。目前有多种遥感模型可以模拟总初级生产力，比较不同遥感模型在中亚干旱区上的适用性对推进全球干旱区碳收支估算具有重要意义。基于涡度协相关技术观测的四个地面站数据验证MOD17、VODCA2、VPM、TG、SANIRv五种模型的模拟精度。结果表明：（1）基于光能利用率理论的MOD17、VPM模型模拟咸海荒漠植被和阜康荒漠植被GPP的精度最高（R²分别为0.52和0.80），但在模拟草地、农田生态系统生产力时存在较明显的低估（RE>20%）；基于植被指数的遥感模型TG模型、SANIRv模型模拟巴尔喀什湖草地生态系统和乌兰乌苏农田生态系统GPP的精度最高（R²分别为0.91和0.81），同时模拟值与实测值的相对误差也较低；基于微波的VODCA2模型模拟各生态系统生产力的效果最差。（2）水分亏缺是限制植被GPP的主要因素，因此是否合理考虑水分胁迫是影响GPP模型在中亚干旱区适用性的重要因素。研究揭示了遥感GPP模型在中亚干旱区的应用潜力，为推进全球植被碳通量的准确估算提供参考。

Evaluation of the applicability of remote sensing GPP models in four typical ecosystems in the arid zone of Central Asia

Gross primary productivity (GPP) is an important component of the global ecosystem carbon cycle and has a significant impact on global climate change. Accurate estimation of GPP helps to understand the carbon cycle processes in atmospheric and terrestrial ecosystems. a variety of remote sensing models are available to simulate GPP, but few studies have assessed the applicability of remote sensing GPP models for arid and semi-arid regions, and semi-arid ecosystems dominate the trend and inter-annual variability in the land CO₂ sink, it is important to compare the applicability of different remote sensing models in the arid zone of Central Asia to advance the estimation of carbon balance in the global arid zone. In this study, the data from four ground stations observed based on the eddy covariance technique are used as validation data to verify the simulation accuracy of five models, MOD17, VODCA2, VPM, TG, and SANIRv. The four ground stations are China Fukang Desert Ecological Observation and Research Station (CN-Fuk), China Ulan-Usu Oasis Farmland Ecology and Agrometeorological Experiment Station (CN-Wul), Kazakhstan Desert Ecological Observatory (KZ-Ara), and Kazakhstan Lake Balkhash Grassland Ecological Observatory (KZ-Bal). The remote sensing model is mainly driven by moderate-resolution imaging spectroradiometer (Modis) remote sensing data and ERA5-land meteorological reanalysis data. The results indicated that:(1) MOD17 and VPM models based on light energy utilization theory simulated the highest accuracy of GPP in Aral Sea and Fukang desert vegetation (R² 0.52 and 0.80, respectively), but there was more obvious underestimation (RE>20%) in simulating the productivity of grassland and farmland ecosystems. The remote sensing models based on vegetation index, TG model, SANIRv model, simulating Grassland ecosystem of Lake Balkhash and farmland ecosystem of Ulan-Usu had the highest accuracy (R² 0.91 and 0.81, respectively), and the relative error between simulated and measured values was low. The microwave-based VODCA2 model simulated the productivity of each ecosystem with the worst effect, also the VODCA2 model performs the worst in tracking the seasonal dynamics of GPP in four ecosystems, and the other four remote sensing models show excellent performance. (2) Water deficit was the main factor limiting vegetation GPP, so whether water stress is reasonably considered is an important factor affecting the applicability of GPP models in the arid zone of Central Asia. This study initially reveals the application potential of remote sensing GPP models in the arid zone of Central Asia, which can help to advance the accurate estimation of global vegetation carbon fluxes.