Recent Changes in Terrestrial Gross Primary Productivity in Asia from 1982 to 2011

A novel multi-model fusion framework diagnoses the complex variation characteristics of ecological indicators and quantitatively reveals their driving mechanism

Systematic analysis of the change law and driving mechanism of ecological indicators (GPP, ET, WUE), as well as the study of maximum threshold of water resources benefit changing with ecological benefit, are important prerequisites for realizing the scientific allocation and efficient utilization of water resources in desert riparian forests. However, previous studies have defects in the detailed description of the change characteristics of ecological indicators. How to accurately diagnose the characteristics of a site, mutation year, pattern (linear, exponential, logarithmic, etc.), duration of change, future change trends of ecological indicators in a desert riparian environment, as well as quantitatively revealing their driving mechanisms, are major scientific problems that need to be solved urgently. In this regard, an ensemble function coupling a logistic function and an asymmetric Gaussian function was creatively adopted, a novel framework was created to integrate the time-series trajectory fitting method and the sensitivity analysis method, and the arid and ecologically fragile Tarim River Basin was taken as a typical area. The results showed that with enhanced water resource management in the Tarim River Basin, GPP, ET, and WUE all showed patterns of increasing change and could be expected to continue to rise or to remain at a high-level stable state. The longest continuous period of GPP change was 15 years, showing that ecological restoration is a long-term process. The years of GPP mutation were consistent with the implementation periods of major measures in the Tarim River Basin (1990, 2001, and 2011), indicating the reliability of this framework. More importantly, when GPP increased to 216.44 gCm^-2, the maximum WUE threshold of 0.93 gCm^-2mm^-1 occurred. This threshold can be used as a reference criterion for efficient utilization of ecological water in the basin. Among the ecological indicators studied, GPP was the most sensitive to environmental change, but GPP, with 80.60% of pixel area, showed a weak memory effect（α < 0.4). Besides, GPP was the most sensitive to the leaf area index (LAI) and had the strongest correlation with it (p < 0.001). Therefore, LAI can be used as the main control factor for judging plant growth. This research can provide important scientific guidance and reference for the analysis of ecological indicator changes and the sustainable utilization of water resources in arid areas.

Effect of Permafrost Thawing on Discharge of the Kolyma River, Northeastern Siberia

With permafrost warming, the observed discharge of the Kolyma River in northeastern Siberia decreased between 1930s and 2000; however, the underlying mechanism is not well understood. To understand the hydrological changes in the Kolyma River, it is important to analyze the long-term hydrometeorological features, along with the changes in the active layer thickness. A coupled hydrological and biogeochemical model was used to analyze the hydrological changes due to permafrost warming during 1979–2012, and the simulated results were validated with satellite-based products and in situ observational records. The increase in the active layer thickness by permafrost warming suppressed the summer discharge contrary to the increased summer precipitation. This suggests that the increased terrestrial water storage anomaly (TWSA) contributed to increased evapotranspiration, which likely reduced soil water stress to plants. As soil freeze–thaw processes in permafrost areas serve as factors of climate memory, we identified a two-year lag between precipitation and evapotranspiration via TWSA. The present results will expand our understanding of future Arctic changes and can be applied to Arctic adaptation measures.

Prediction of Soil Organic Carbon based on Landsat 8 Monthly NDVI Data for the Jianghan Plain in Hubei Province, China

High-precision maps of soil organic carbon (SOC) are beneficial for managing soil fertility and understanding the global carbon cycle. Digital soil mapping plays an important role in efficiently obtaining the spatial distribution of SOC, which contributes to precision agriculture. However, traditional soil-forming factors (i.e., terrain or climatic factors) have weak variability in low-relief areas, such as plains, and cannot reflect the spatial variation of soil attributes. Meanwhile, vegetation cover hinders the acquisition of the direct information of farmland soil. Thus, useful environmental variables should be utilized for SOC prediction and the digital mapping of such areas. SOC has an important effect on crop growth status, and remote sensing data can record the apparent spectral characteristics of crops. The normalized difference vegetation index (NDVI) is an important index reflecting crop growth and biomass. This study used NDVI time series data rather than traditional soil-forming factors to map SOC. Honghu City, located in the middle of the Jianghan Plain, was selected as the study region, and the NDVI time series data extracted from Landsat 8 were used as the auxiliary variables. SOC maps were estimated through stepwise linear regression (SLR), partial least squares regression (PLSR), support vector machine (SVM), and artificial neural network (ANN). Ordinary kriging (OK) was used as the reference model, while root mean square error of prediction (RMSEP) and coefficient of determination of prediction (R2P) were used to evaluate the model performance. Results showed that SOC had a significant positive correlation in July and August (0.17, 0.29) and a significant negative correlation in January, April, and December (−0.23, −0.27, and −0.23) with NDVI time series data. The best model for SOC prediction was generated by ANN, with the lowest RMSEP of 3.718 and highest R2P of 0.391, followed by SVM (RMSEP = 3.753, R2P = 0.361) and PLSR (RMSEP = 4.087, R2P = 0.283). The SLR model was the worst model, with the lowest R2P of 0.281 and highest RMSEP of 3.930. ANN and SVM were better than OK (RMSEP = 3.727, R2P = 0.372), whereas PLSR and SLR were worse than OK. Moreover, the prediction results using single-data NDVI or short time series NDVI showed low accuracy. The effect of the terrain factor on SOC prediction represented unsatisfactory results. All these results indicated that the NDVI time series data can be used for SOC mapping in plain areas and that the ANN model can maximally extract additional associated information between NDVI time series data and SOC. This study presented an effective method to overcome the selection of auxiliary variables for digital soil mapping in plain areas when the soil was covered with vegetation. This finding indicated that the time series characteristics of NDVI were conducive for predicting SOC in plains.

Influence of Landscape Heterogeneity and Spatial Resolution in Multi-Temporal In Situ and MODIS NDVI Data Proxies for Seasonal GPP Dynamics

The objective of this paper was to evaluate the use of in situ normalized difference vegetation index (NDVIis) and Moderate Resolution Imaging Spectroradiometer NDVI (NDVIMD) time series data as proxies for ecosystem gross primary productivity (GPP) to improve GPP upscaling. We used GPP flux data from 21 global FLUXNET sites across main global biomes (forest, grassland, and cropland) and derived MODIS NDVI at contrasting spatial resolutions (between 0.5 × 0.5 km and 3.5 × 3.5 km) centered at flux tower location. The goodness of the relationship between NDVIis and NDVIMD varied across biomes, sites, and MODIS spatial resolutions. We found a strong relationship with a low variability across sites and within year variability in deciduous broadleaf forests and a poor correlation in evergreen forests. Best performances were obtained for the highest spatial resolution at 0.5 × 0.5 km). Both NDVIis and NDVIMD elicited roughly three weeks later the starting of the growing season compared to GPP data. Our results confirm that to improve the accuracy of upscaling in situ data of site GPP seasonal responses, in situ radiation measurement biomes should use larger field of view to sense an area, or more sensors should be placed in the flux footprint area to allow optimal match with satellite sensor pixel size.

Estimation of Vegetation Productivity Using a Landsat 8 Time Series in a Heavily Urbanized Area, Central China

Estimating the net primary production (NPP) of vegetation is essential for eco-environment conservation and carbon cycle research. Remote sensing techniques, combined with algorithm models, have been proven to be promising methods for NPP estimation. High-precision and real-time NPP monitoring in heterogeneous areas requires high spatio-temporal resolution remote sensing data, which are not easy to acquire by single remote sensors, especially in cloudy weather. This study proposes to fuse images of different sensors to provide high spatio-temporal resolution data for NPP estimation in cloud-prone areas. Firstly, the time series Normalized Difference Vegetation Index (NDVI) with a spatial resolution of 30 m and a temporal resolution of 16 days, are obtained by the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). Then, the time series NDVI data, combined with meteorological data are input into an improved Carnegie–Ames–Stanford Approach (CASA) model for NPP estimation. This method is validated by a case study of a heavily urbanized area, in the middle reaches of the Yangtze River in China. The results indicate that the NPP estimated by the fused NDVI data has more detailed spatial information than by using the MODIS data. The results show a strong correlation between the actual Landsat8 NDVI and the fused NDVI images, which means that the accuracy of synthetic NDVI images (a 16 day interval and a 30 m resolution) is reliable, and it can provide superior inputs for accurate estimations of a NPP time series. The correlation coefficient (R) and root mean square error between the NPP, based on the fused NDVI and the measured NPP, are 0.66 and 14.280 g C/(m2·yr), respectively, indicating a good consistency. The small discrepancy is caused by the uncertainties of fused NDVI, measurement errors, conversion errors, and other factors in the CASA model. In this study, we achieved NPP with high spatial and temporal resolutions, which can provide higher accuracies of NPP data for analyzing the carbon cycling heavily urbanized areas, compared with similar studies using mono-temporal NPP data. The spatio-temporal fusion technique is an effective way of generating high spatio-temporal resolution images from different sensors, thereby providing enough data for NPP monitoring in urbanized areas.