湿地是地球上重要的“碳库”之一，针对湿地净初级生产力（Net Primary Productivity, NPP）模拟中时空分辨率不高和估算精度不稳定等方面的问题，本文提出了一种修正的CASA（Carnegie-Ames-Stanford Approach）模型。首先采用遥感云计算下的时空融合算法快速、准确地获得了时间序列的Landsat 8多光谱影像，解决湿地NPP估算中高时空分辨率影像缺失问题。然后，利用Landsat 8数据集（光谱波段、陆表水体指数、归一化植被指数等）与自适应Stacking算法得到高精度的植被分类图，并结合植被分类图确定每个植被像元理想条件下最大光能利用率εmax。同时，利用时序陆表水体指数及降水数据计算获得NPP估算中所需的水分胁迫因子。最后，基于归一化植被指数、水分胁迫因子、εmax及气象数据等多种参数，驱动CASA模型对洞庭湖湿地NPP进行估测。研究结果显示，与其他模型相比，本文修正CASA模型估算的NPP与实测的NPP具有最高的相关系数（R2 = 0.85）和最低的RMSE（20.16 g C/m2），表明该方法能有效、准确地模拟区域湿地生态系统NPP。洞庭湖区主要湿地植被类型芦苇与苔草的NPP均值分别为424.26 g C/m2和357.50g C/m2。

Wetland, an important carbon pool on the earth, is of great significance for human beings and the environment, and an accurate estimation of wetland carbon storage and its temporal and spatial changes is conducive to understanding the sustainable development of wetland ecosystems. Net primary productivity (NPP) is the net accumulation of organic matter fixed by photosynthesis per unit time and per unit area of green vegetation, and is an important indicator to characterize the status of carbon flux. Therefore, accurate estimation of the spatial patterns and temporal dynamics of wetland NPP at a regional scale is of crucial significance to improve our understanding of the carbon dynamics and sustainable development of terrestrial ecosystems. In China, similar studies have mapped wetlands or estimated wetland NPP using optical data. However, few studies have used dense high-spatio-temporal-resolution multispectral images for wetland mapping and considered the accuracy of the maximum light-use efficiency (εmax) of wetland vegetation types for NPP estimation. In this study, we proposed an improved Carnegie–Ames–Stanford Approach (CASA) model to generate wetland NPP with high-spatio-temporal-resolution. First, spatio-temporal fusion algorithm process under remote sensing cloud computing was utilized to produce a dense Landsat 8 reflectance images based on Landsat 8 and MOD09Q1 images. Then, we explored the potential of Landsat 8 dataset for vegetation type mapping in a subtropical wetland ecosystem using the adaptive Stacking algorithm. Subsequently, using the vegetation classification map to determine the final prior specification of a maximum ε (εmax) of each vegetation pixel. Finally, estimating wetland NPP with CASA using the Normalized Difference Vegetation Index (NDVI), LSWI and wetland vegetation map. Visually, spatio-temporal fusion algorithm process under remote sensing cloud computing showed good performance for downscaling MODIS at low spatial resolution to high spatial resolution, except for some minor flaws that did not affect the overall product. For the fused image, the STNLFFM produced an R2 value larger than 0.88, RMSE less than 0.05, and SAM less than 3, which indicated that the fused image was nearly consistent with original Landsat spectrally and spatially. Therefore, STNLFFM is suitable for image fusion in areas experiencing rapid change, such as wetlands and city suburbs. The overall accuracy of wetland map was above 88%, which indicates the potential of the improved Stacking algorithm for delineating different land cover types. Additionally, the user and producer accuracies of vegetation types varied within 85%–92% and 83%–91%, respectively. The classification accuracy associated with the proposed method are notably higher than those of the classical methods (e.g., SVM, RF, kNN), indicating the superiority of the adaptive Stacking for discriminating land cover in a wetland with complex conditions. The measured NPP values derived from field aboveground biomass data were used to validate the accuracy of simulated NPP. The high correlation coefficient (R2 = 0.85) and low RMSE (20.16 g C/m2) between the estimated and measured NPP demonstrated a significant linear relationship, and thus the estimated NPP based on Landsat data using the CASA model with the input parameters described above is creditable. The average NPP of sedges and reed wetland were 357.50 g C/m2 and 424.26g C/m2, respectively. The mean NPP values of wetlands (reed and tussock) estimated by the modified CASA model in this paper were also closer to those estimated by other models. The NPP estimation method in this paper is expected to provide scientific data support for quantitative research on regional wetland carbon reserves and sustainable development.