Potential of VIS-NIR-SWIR Spectroscopy from the Chinese Soil Spectral Library for Assessment of Nitrogen Fertilization Rates in the Paddy-Rice Region, China

To meet growing food demand with limited land and reduced environmental impact, soil testing and formulated fertilization methods have been widely adopted around the world. However, conventional technology for investigating nitrogen fertilization rates (NFR) is time consuming and expensive. Here, we evaluated the use of visible near-infrared shortwave-infrared (VIS-NIR-SWIR: 400–2500 nm) spectroscopy for the assessment of NFR to provide necessary information for fast, cost-effective and precise fertilization rating. Over 2000 samples were collected from paddy-rice fields in 10 Chinese provinces; samples were added to the Chinese Soil Spectral Library (CSSL). Two kinds of modeling strategies for NFR, quantitative estimation of soil N prior to classification and qualitative by classification, were employed using partial least squares regression (PLSR), locally weighted regression (LWR), and support vector machine discriminant analogy (SVMDA). Overall, both LWR and SVMDA had moderate accuracies with Cohen’s kappa coefficients of 0.47 and 0.48, respectively, while PLSR had fair accuracy (0.37). We conclude that VIS-NIR-SWIR spectroscopy coupled with the CSSL appears to be a viable, rapid means for the assessment of NFR in paddy-rice soil. Based on qualitative classification of soil spectral data only, it is recommended that the SVMDA be adopted for rapid implementation

Revealing the scale- and location-specific controlling factors of soil organic carbon in Tibet

International audienceSoil organic carbon (SOC) leads to a significant impact on global carbon (C) cycling and soil quality. Variations in SOC are controlled by vegetation, geomorphic, geological and climatic factors, but the dominant environmental differs. In the Qinghai -Tibet Plateau, which contains large amount of low-latitude permafrost, the impact of environmental factors for the variations of SOC may be different due to the unique and complicated geographical condition. In this study, the two-dimension empirical mode decomposition (2D-EMD) is applied to examine the variations of SOC at different scales and locations, and the correlations between SOC and environmental factors are explained. The spatial distribution of SOC in Tibet was decomposed into three intrinsic mode functions (IMFs) under different scales, with spatial variation scales of approximately 7 km, 109 km and 338 km, which represented the small, medium and large scale, respectively. The remaining residual represented the variation trend of SOC across Tibet. The correlations between SOC and environmental factors (elevation, radiation, evapotranspiration and temperature) are distinguished by the physiographic zone at small and medium scales. Temperature is weekly or nonsignificantly correlated to SOC in cold-dry western Tibet at large scale. Normalized difference vegetation index (NDVI) and precipitation influenced SOC mainly at small scales, while the effects of precipitation and evapotranspiration on the distribution of SOC were due to geomorphology and type of permafrost. The combined effect of climate on SOC was larger than other factors at large scale while factors refer to DEM, evapotranspiration, water erosion and NDVI accounted for more contribution at small scale. The results indicated that the environmental factors influence SOC under a combination of scale and location effect. These findings are of great significance for future studies in SOC dynamic modelling under the influence of natural changes and human activities

Fine-Resolution Mapping of Soil Total Nitrogen across China Based on Weighted Model Averaging

Accurate estimates of the spatial distribution of total nitrogen (TN) in soil are fundamental for soil quality assessment, decision making in land management, and global nitrogen cycle modeling. In China, current maps are limited to individual regions or are of coarse resolution. In this study, we compiled a new 90-m resolution map of soil TN in China by the weighted summation of random forest and extreme gradient boosting. After harmonizing soil data from 4022 soil profiles into a fixed soil depth (0–20 cm) by equal area spline, 18 environmental covariates were employed to characterize the spatial pattern of soil TN in topsoil across China. The accuracy assessments from independent validation data showed that the weighted model averaging gave the best predictions with an acceptable R2 (0.41). The prediction map showed that high-value areas of soil TN were mainly distributed in the eastern Tibetan Plateau, central Qilian Mountains and the north of the Greater Khingan Range. Climate factors had a considerable influence on the variation of the soil TN, and land-use types played a pivotal part in each climate zone. This high-resolution and high-quality soil TN data set in China can be very useful for future inventories of soil nitrogen, assessments of soil nutrient status, and management of arable land

Climate change-induced greening on the Tibetan Plateau modulated by mountainous characteristics

International audienceGlobal terrestrial vegetation is greening, particularly in mountain areas, providing strong feedbacks to a series of ecosystem processes. This greening has been primarily attributed to climate change. However, the spatial variability and magnitude of such greening do not synchronize with those of climate change in mountain areas. By integrating two data sets of satellite-derived normalized difference vegetation index (NDVI) values, which are indicators of vegetation greenness, in the period 1982-2015 across the Tibetan Plateau (TP), we test the hypothesis that climate-changeinduced greening is regulated by terrain, baseline climate and soil properties. We find a widespread greening trend over 91% of the TP vegetated areas, with an average greening rate (i.e. increase in NDVI) of 0.011 per decade. The linear mixed-effects model suggests that climate change alone can explain only 26% of the variation in the observed greening. Additionally, 58% of the variability can be explained by the combination of the mountainous characteristics of terrain, baseline climate and soil properties, and 32% of this variability was explained by terrain. Path analysis identified the interconnections of climate change, terrain, baseline climate and soil in determining greening. Our results demonstrate the important role of mountainous effects in greening in response to climate change