The Songnen Plain (SNP) is an important grain production base, and is designated as an ecological red-line as a protected area in China. Natural ecosystems such as the ecological protection barrier play an important role in maintaining the productivity and sustainability of farmland. Carbon use efficiency (CUE), defined as the ratio of net primary productivity (NPP) to gross primary productivity (GPP), represents the ecosystem capacity of transferring carbon from the atmosphere to terrestrial biomass. The understanding of the CUE of natural ecosystems in protected farmland areas is vital to predicting the impact of global change and human disturbances on carbon budgets and evaluating ecosystem functions. To date, the changes in CUE at different time scales and their relationships with climatic factors have yet to be fully understood. CUE and the response to land surface phenology are also deserving attention. In this study, variations in ecosystem CUE in the SNP during 2001–2015 were investigated using Moderate-Resolution Imaging Spectroradiometer (MODIS) GPP and NPP data products estimated using the Carnegie-Ames-Stanford approach (CASA) model. The relationships between CUE and phenological and climate factors were explored. The results showed that ecosystem CUE fluctuated over time in the SNP. The lowest and highest CUE values mainly occurred in May and October, respectively. At seasonal scale, average CUE followed a descending order of Autumn > Summer > Spring. The CUE of mixed forest was greater than that of other ecosystems at both monthly and seasonal scales. Land surface phenology plays an important role in the regulation of CUE. The earlier start (SOS), the later end (EOS) and longer length (LOS) of the growing season would contribute increasing of CUE. Precipitation and temperature affected CUE positively in most areas of the SNP. These findings help explain the CUE of natural ecosystems in the protected farmland areas and improve our understanding of ecosystem carbon allocation dynamics in temperate semi-humid to semi-arid transitional region under climate and phenological fluctuations.

Leaf area index (LAI) is an important parameter for monitoring the physical and biological processes of vegetation canopy. Due to the constraints of cloud contamination, snowfall, and instrument conditions, most of the current satellite remote sensing LAI products have lower resolution that cannot satisfy the needs of vegetation remote sensing application in areas of high heterogeneity. We proposed a new model to generate high resolution LAI, by combining linear pixel unmixing and the Flexible Spatiotemporal Data Fusion (FSDAF) method. This method derived the input data of FSDAF by downscaling the MODIS (Moderate Resolution Imaging Spectroradiometer) data with a linear spectral mixture model. Through the improved input parameters of the algorithm, the fusion of MODIS LAI and LAI at Landsat spatial resolution estimated by Support Vector Regression model was realized. The fusion accuracy of generated LAI data was validated based on Sentinel-2 LAI products. The results showed that strong correlation between predicted LAI and Sentinel-2 LAI in sample sites was observed with higher correlation coefficients and lower Root Mean Square Error. Compared to the simulation results of FSDAF, the modified FSDAF model showed higher accuracy and reflected more spatial details in the boundary areas of different land cover types.

Soil water use efficiency (SWUE) was proposed as an effective proxy of ecosystem water use efficiency (WUE), which reflects the coupling of the carbon–water cycle and function of terrestrial ecosystems. The changes of ecosystem SWUE at the regional scale and their relationships with the environmental and biotic factors are yet to be adequately understood. Here, we aim to estimate SWUE over northeast China using time-series Moderate Resolution Imaging Spectroradiometer (MODIS) gross primary productivity data and European Space Agency climate change initiative (ESA CCI) soil moisture product during 2007–2015. The spatio-temporal variations in SWUE and their linkages to multiple factors, especially the phenological metrics, were investigated using trend and correlation analysis. The results showed that the spatial heterogeneity of ecosystem SWUE in northeast China was obvious. SWUE distribution varied among vegetation types, soil types, and elevation. Forests might produce higher photosynthetic productivity by utilizing unit soil moisture. The seasonal variations of SWUE were consistent with the vegetation growth cycle. Changes in normalized difference vegetation index (NDVI), land surface temperature, and precipitation exerted positive effects on SWUE variations. The earlier start (SOS) and later end (EOS) of the growing season would contribute to the increase in SWUE. Our results help complement the knowledge of SWUE variations and their driving forces.