Revealing the globally multiscale controls of environmental factors on carbon use efficiency.

The carbon use efficiency (CUE), which is the ratio of net primary production to gross primary production, is an essential element for detecting the terrestrial carbon cycle and ecosystem function. The spatial variation of CUE is controlled by environmental factors independently or interactively with different intensity. However, previous studies have mainly focused on the effect of climate on the local CUE at the sampling scale, while neglecting the effects of topography or soil on the global CUE, and even its spatially predictive model. In the study, the relative contributions of potentially influencing factors (i.e., climatic, topographic, and edaphic factors), and their interactions on the global CUE were analyzed using the combined methods of curvelet transform and geographical detector model, and the spatial model of CUE were established based on its relationships with influencing factors. The results showed that CUE values at the sampling scale were generally greater in the mid- and high-latitude regions than those in the low-latitude region, which was characterized by its spatial pattern at the large scale. Climate had the greater effects on CUE variations at the large scale, while topography was the main factor controlling CUE at the small or medium scale. However, the explanatory power of the interaction among factors on CUE was greater than any single factor, among which the interaction between climatic and topographical factors was the strongest at all scales. The CUE predication based on scale-dependent effects was more accurate than that based on the sampling scale especially in the high-latitude, and temperature and elevation was the main predictors. Based on the model, the spatial patterns of CUE under future scenarios with any climatic changes could be extracted. This study can further advance our understanding on spatial variation of CUE, and provide a unique insight for CUE prediction responding to climate changes.

Rapid Identification of Main Vegetation Types in the Lingkong Mountain Nature Reserve Based on Multi-Temporal Modified Vegetation Indices.

Nature reserves are among the most bio-diverse regions worldwide, and rapid and accurate identification is a requisite for their management. Based on the multi-temporal Sentinel-2 dataset, this study presents three multi-temporal modified vegetation indices (the multi-temporal modified normalized difference Quercus wutaishanica index (MTM-NDQI), the multi-temporal modified difference scrub grass index (MTM-DSI), and the multi-temporal modified ratio shaw index (MTM-RSI)) to improve the classification accuracy of the remote sensing of vegetation in the Lingkong Mountain Nature Reserve of China (LMNR). These three indices integrate the advantages of both the typical vegetation indices and the multi-temporal remote sensing data. By using the proposed indices with a uni-temporal modified vegetation index (the uni-temporal modified difference pine-oak mixed forest index (UTM-DMI)) and typical vegetation indices (e.g., the ratio vegetation index (RVI), the difference vegetation index (DVI), and the normalized difference vegetation index (NDVI)), an optimal feature set is obtained that includes the NDVI of December, the NDVI of April, and the UTM-DMI, MTM-NDQI, MTM-DSI, and MTM-RSI. The overall accuracy (OA) of the random forest classification (98.41%) and Kappa coefficient of the optimal feature set (0.98) were higher than those of the time series NDVI (OA = 96.03%, Kappa = 0.95), the time series RVI (OA = 95.56%, Kappa = 0.95), and the time series DVI (OA = 91.27%, Kappa = 0.90). The OAs of the rapid classification and the Kappa coefficient of the knowledge decision tree based on the optimal feature set were 95.56% and 0.95, respectively. Meanwhile, only three of the seven vegetation types were omitted or misclassified slightly. Overall, the proposed vegetation indices have advantages in identifying the vegetation types in protected areas.

Spatial and temporal characteristics of surface soil moisture in a disturbed coal mining area of Chinese Loess Plateau.

Soil water content is an important variable in hydrology and many related disciplines. It affects runoff from precipitation, groundwater recharge, and evapotranspiration. This research used the coal mining area of the Changhe River Basin in the Loess Plateau as a study and using SAR (Synthetic Aperture Radar) data, the surface soil water in 24 days (From Jan 25, 2018 to Dec 10, 2019) was estimated using a radar signal change detection algorithm. The temporal and spatial variation characteristics of surface soil water inside and outside the disturbed area were compared and analyzed. An empirical orthogonal function (EOF) analysis method was used to analyze the potential temporal and spatial variation of surface soil water, and to detect the regional soil water variation under coal mining disturbances to better understand the different potential modes of spatial variation of soil water in the unobserved time. The results showed that the average surface soil water content in the study area changed with season, showing a dry-wet-dry variation. Moreover, it was significantly affected by precipitation factors, and its response to precipitation had a hysteresis effect. From the perspective of spatial variation, the influence of coal mining disturbance on surface soil moisture was not obvious. From the perspective of time series change, moving from wet to dry conditions, the soil in the disturbed area dried faster than the soil in the undisturbed area after soil wetted. When moving from drying to wetting, the soil in the disturbed area was quickly wetted. The EOF analysis showed that most observed spatial variability of soil moisture was stable in time. The study was conducted in a disturbed area and an undisturbed area for single EOF analysis, and the results showed that the EOF mode of the disturbed area was closer to that of the whole study area. By comparing the two subregions and the entire study area, it was found that the changes of correlation values were related to soil texture, bulk density, altitude and slope, indicating that the soil texture of the two subregions may be different at different elevations, and may also be related to the change of the original soil structure in the disturbed area. Overall, the EOF mode of the disturbed area determined the EOF mode of the entire study area.