Exploring the variations in the water use efficiency (WUE) is helpful in gaining an in-depth understanding of the regional carbon and water cycles on the Chinese Loess Plateau (CLP). Here, we employed the spatial variations in the WUE and the quantitative contributions of the influencing factors, including the precipitation (P), temperature (Temp), vapor pressure deficit (VPD), sunshine duration (SD), and leaf area index (LAI), with the drought index varying over the last two decades. Results showed that the multiyear average WUE decreased significantly as the drought index increased for all of the vegetation types. Per-pixel interannual variability of WUE trend was 0.024 gC·m−2·mm−1·yr−1. As the drought index increased, the WUE initially increased and then decreased for the forests, grassland, and shrubland, and their peaks occurred at drought index values of 2.60–3.10. Among the influencing factors, the WUE was predominantly controlled by the LAI, with an impact and relative contribution of 0.014 gC·m−2·mm−1·yr−1 and 58.3%, respectively. The P and SD contributed the least to the trend in WUE, and impact and relative contribution of both were 0.001 gC·m−2·mm−1·yr−1 and 4.17%. Our study also demonstrated that the LAI was the dominant factor affecting the WUE trends for grassland and the Yan River due to the structural parameters and geographical location. In addition, the impact and relative contribution of the residual factors on the WUE trend were 0.004 gC·m−2·mm−1·yr−1 and 16.7%. Our findings suggested that comprehensive effects such as micro-geomorphic changes and nitrogen deposition could not be ignored except for vegetation and climate change. This study will clarify the spatial and temporal evolution of WUE and its influence mechanism.
Sparse mixed forest with trees, shrubs, and green herbaceous vegetation is a typical landscape in the afforestation areas in northwestern China. It is a great challenge to accurately estimate the woody aboveground biomass (AGB) of a sparse mixed forest with heterogeneous woody vegetation types and background types. In this study, a novel woody AGB estimation methodology (VI-AGB model stratified based on herbaceous vegetation coverage) using a combination of Landsat-8, GaoFen-2, and unmanned aerial vehicle (UAV) images was developed. The results show the following: (1) the woody and herbaceous canopy can be accurately identified using the object-based support vector machine (SVM) classification method based on UAV red-green-blue (RGB) images, with an average overall accuracy and kappa coefficient of 93.44% and 0.91, respectively; (2) compared with the estimation uncertainties of the woody coverage-AGB models without considering the woody vegetation types (RMSE = 14.98 t∙ha−1 and rRMSE = 96.31%), the woody coverage-AGB models stratified based on five woody species (RMSE = 5.82 t∙ha−1 and rRMSE = 37.46%) were 61.1% lower; (3) of the six VIs used in this study, the near-infrared reflectance of pure vegetation (NIRv)-AGB model performed best (RMSE = 7.91 t∙ha−1 and rRMSE = 50.89%), but its performance was still seriously affected by the heterogeneity of the green herbaceous coverage. The normalized difference moisture index (NDMI)-AGB model was the least sensitive to the background. The stratification-based VI-AGB models considering the herbaceous vegetation coverage derived from GaoFen-2 and UAV images can significantly improve the accuracy of the woody AGB estimated using only Landsat VIs, with the RMSE and rRMSE of 6.6 t∙ha−1 and 42.43% for the stratification-based NIRv-AGB models. High spatial resolution information derived from UAV and satellite images has a great potential for improving the woody AGB estimated using only Landsat images in sparsely vegetated areas. This study presents a practical method of estimating woody AGB in sparse mixed forest in dryland areas.
The estimation of potato biomass and yield can optimize the planting pattern and tap the production potential. Based on partial least square (PLSR), multiple linear regression (MLR), support vector machine (SVM), random forest (RF), BP neural network and other machine learning algorithms, the biomass estimation model of potato in different growth stages is constructed by using single variables such as original spectrum, first-order differential spectrum, combined spectrum index and vegetation index (VI) and their coupled combination variables. The accuracy of the models is compared and analyzed, and the best modeling method of biomass in different growth stages is selected. Based on the optimized modeling method, the biomass of each growth stage is estimated, and the yield estimation model of different growth stages is constructed based on the estimation results and the linear regression analysis method, and the accuracy of the model is verified. The results showed that in tuber formation stage, starch accumulation stage and maturity stage, the biomass estimation accuracy based on combination variable was the highest, the best modeling method was MLR and SVM, in tuber growth stage, the best modeling method was MLR, the effect of yield estimation is good. It provides a reference for the algorithm selection of crop biomass and yield models based on machine learning.
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