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Received：03 December 2021，
Published：07 June 2023
移动端阅览
湿地是地球上重要的“碳库”之一，针对湿地净初级生产力NPP（Net Primary Productivity）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
ε
max
。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、
ε
max
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（
R
2
=0.85）和最低的RMSE（20.16 g C/m
2
），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/m
2
和357.50g C/m
2
。
Wetlands
which are an important carbon pool on Earth
is crucial for human beings and the environment. An accurate estimation of wetland carbon storage and its temporal and spatial changes are 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 crucial to improving 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
only a few studies have used dense high-spatiotemporal-resolution multispectral images for wetland mapping and considered the accuracy of the light-use efficiency (
ε
) 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 spatiotemporal resolution. First
spatiotemporal fusion algorithm process under remote sensing cloud computing was utilized to produce dense Landsat 8 reflectance images based on Landsat 8 and MOD09A1 images. Then
we explored the potential of the Landsat 8 dataset for vegetation type mapping in a subtropical wetland ecosystem using the adaptive stacking algorithm. Subsequently
the vegetation classification map was used to determine the final prior specification of a maximum
ε
(
ε
max
) of each vegetation pixel. Finally
wetland NPP with CASA was estimated using the normalized difference vegetation index
LSWI and wetland vegetation map.
Visually
the SpatioTemporal Fusion Algorithm (STFA) process based on Google Earth Engine (GEE) showed good performance in downscaling MODIS at low to high spatial resolutions
except for some minor flaws that did not affect the overall product. For the fused image
the STFA based on GEE produced an
R
2
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
STFA based on GEE is suitable for image fusion in areas experiencing rapid change
such as wetlands and city suburbs. The overall accuracy of the 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
and kNN)
indicating the superiority of the adaptive stacking algorithm 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 (
R
2
=
0.85) and low
RMSE
(20.16 g C/m
2
) 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 and 424.26 g C/m
2
respectively. The mean NPP values of wetlands (reed and tussock) estimated by the modified CASA model in this study were also closer to those estimated by other models.
In this study
time-series Landsat data were obtained on the basis of the STFA based on GEE
and the modified CASA model estimated the NPP of the Dongting Lake wetlands with high spatiotemporal resolution. The NPP estimation method in this study is expected to provide scientific data support for quantitative research on regional wetland carbon reserves and sustainable development.
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