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Soil organic matter (SOM) mapping in salinized areas is crucial for scientific guidance on soil salinization. However, accurately mapping SOM is challenging due to the intricate interplay between soil moisture content (SMC) and soil salt content (SSC), which significantly influences soil spectra. Unlike prior research that has separately examined the impacts of moisture or salinity, this study delves into the combined effects of these factors on SOM spectra. The objective of this study is to develop and validate several spectral optimization algorithms at both the laboratory and satellite levels. In October 2020, a study was conducted using 291 ground-truth data to examine the impact of various moisture-salt stages (seven moisture stages, five salt stages) on hyperspectral data. Spectrum mechanism responsive for soil moisture and salinity were analyzed through spectral curves, correlation, and analysis of variance (Anova, AOV), and spectrum mechanism responsive for soil moisture and salinity model were built. Following that, the spectra were optimized using piecewise direct normalization (PDS)-AOV, non-negative matrix factorization (NMF)-AOV, and orthogonal signal correction (OSC)-AOV. The SOM prediction models were then built by integrating these optimized spectra with Stacking ensemble machine learning algorithms (RF, GBM, ANN). Eventually, the lab-optimized spectra were merged with satellite multispectral images to create new image (named REC) for SOM mapping. The results indicated varying impacts of SMC and SSC on spectra, particularly between 1400 nm to 2000 nm, revealing the influence of moisture-salt interaction; the best optimization algorithm (OSC-AOV) with Stacking mitigated the effect of moisture-salt coexistence on spectra (the R2 and RPD of the best models elevated by 0.005–0.267, 0.020–0.374 respectively, RMSE reduced by 0.137–1.817 g/kg); implementing this algorithm on REC significantly improved the accuracy of SOM mapping (R2 elevated by 0.185–0.259, RMSE reduced by 2.615–3.203 g/kg). This study extensively investigated the effects of moisture and salinity on spectra, spanning from laboratory to satellite, offering a novel approach to understanding and addressing the complexities in SOM mapping in salinized environments. • Development and validation of a novel spectral optimization algorithm. • Effects of moisture and salinity on soil spectra, from laboratory to satellite scales. • The stacking integration algorithm improves model robustness. • Substantial improvement in SOM mapping accuracy through the developed algorithm. [ABSTRACT FROM AUTHOR]

Highlights • Multiple-units SCADA data is used to comparatively investigate the energy characteristics. • A newly improved algorithm for calculating power coefficient is proposed; • A fast and operable data dividing method is presented; • Understanding of mountain wind farm has been enriched and deepened. Abstract The energy output characteristics of mountain wind farms are more complex and unpredictable than that of flat wind farms because of complex mountainous terrain. Some questions about the energy output characteristics of mountain wind farms are still unclear. For example, the effect of local wind resource characteristics on the energy output of mountain wind farms. In another scenario, single-unit data from Supervisory Control and Data Acquisition (SCADA) system are used to investigate the performance of wind turbines in some cases. But, single-unit SCADA data may not be very comprehensive. To fill this gap, multiple-units SCADA data of 2 MW wind turbines are used to comparatively investigate the energy characteristics of wind turbines in a mountain wind farm. The main contribution of this paper is to obtain a new and comprehensive understanding of the actual characteristics of the energy output of mountain wind farms, rather than staying in the design stage or general theoretical analysis. Specifically, the following issues, including (1) influence of wind speed on energy output, (2) influence of wind direction on energy output, (3) relationship between rotor speed and energy output, (4) power coefficient, have been discussed in depth. To overcome the influence of inherent deviation in wind speed measurement and the inertia of the wind rotor, a newly improved algorithm for calculating power coefficient is proposed. Creatively, a fast and operable data dividing method is presented, and the power coefficients of different operating regions are given. Through the investigation, the influence of local wind resource characteristics on the energy output is obtained. It is found that in the same mountain wind farm, the energy output curves of different wind turbines are different. For instance, energy outputs for four wind turbines in December 2015 are 434 MW, 614 MW, 205 MW, 255 MW, respectively. [ABSTRACT FROM AUTHOR]

Wind resource characteristics in a mountain wind farm is more complicated and changeable than that in a flat wind farm due to the influence of complex mountain terrain-topography on air flow. Although there have been a large number of investigations on wind resource characteristics, the local wind resource characteristics in a mountain wind farm are still a lack. To fill this knowledge gap, high-resolution wind data are extracted for investigating from multiple-unit SCADA data, that is, wind data are collected from four selected wind turbines with SCADA system, which means wind data are from four different locations. For each location, more than thirty million sets of wind data are extracted for investigation in 2015. Then, several kinds of probability density functions (PDFs) are compared, and one-dimensional and multidimensional kernel density estimation method is selected for the investigation of the frequency distribution. Data preprocessing methods of both wind speed and wind direction are also presented. Finally, the results of this investigation reveal the specific change characteristics of wind speed and wind direction in the investigated mountain wind farm. Yearly mean wind speeds for four different locations are 3.8 m/s, 5.1 m/s, 5.4 m/s and 5.6 m/s, respectively. [ABSTRACT FROM AUTHOR]

Due to the time-varying wind direction, yaw operation of wind turbines is a common state. Under yaw, the aerodynamic behavior of wind turbines is complicated, and also causes complex energy capture performance. To clarify some vague knowledge, a detailed investigation of yaw effect based on SCADA data is carried out. Firstly, a yaw coefficient definition and its specific calculation method are presented. Furthermore, to analyze the energy capture mechanism, the loss factor of energy capture (power coefficient) is subdivided into aerodynamic loss factor and inertia loss factor, which means both the effects of aerodynamic characteristic and mechanical inertia on energy capture are considered. According to this understanding, power coefficient is expanded as a function of four factors. Then, a single-valued processing method is employed to investigate the relationship between wind speed and output power. Subsequently, relationship between yaw coefficient and output power is investigated. Comparative investigations are carried out by two different methods, that is, least square fitting (LSF) method and kernel density estimation (KDE) method. Also, characteristics of power coefficient and rotor torque under yaw are investigated. Effect laws of yaw coefficient on wind turbine power, power coefficient, and rotor torque are obtained. Finally, the relationship between the internal control mechanism and external output of wind turbines is discussed. [ABSTRACT FROM AUTHOR]

• Vegetation cover has different effects on topsoil salinity mapping. • SYSI from bare soil pixels could help improve the accuracy of soil salinity mapping. • Models for different soil types achieve better regional mapping than those of the whole. • Stacking algorithm is robust for soil salinity mapping. Optical remote sensing satellites provide rapid access to regional topsoil salinization mapping. However, mapping topsoil salinization based on spectral reflectance is always affected by background material like vegetation cover, straw mulching and soil types. In light of these challenges, this study investigates the potential of image fusion, where images of original and bare soil pixels were combined, to minimize the impact of vegetation cover on topsoil salinity mapping. A case study was presented for the typical vegetation cover area using synchronized Sentinel-2 MSI image (named original image) and 255 ground-truth data collected in October 2020, aligning with periods of vegetation cover and salt return. Furthermore, to obtain novel bare soil pixels, multi-temporal Sentinel-2 MSI images were acquired during two distinct intervals: March to May and September to November, spanning the years from 2018 to 2021. The synthetic soil image (SYSI) was obtained by extracting bare soil pixels from multi-temporal images. Two images (original, SYSI) were fused with non-negative matrix factorization (NMF) method, named SYSI fused. Then, the stacking machine algorithm was used for soil salinity mapping under different soil types, with evaluating the impact of SYSI fused on the accuracy of soil salinity prediction. The results showed the SYSI fused outperformed the original image (the R2 of the best models increased by 0.054–0.242, RMSE and MAE decreased by 0.049–0.780 and 0.012–0.546, respectively). Based on the SYSI fused , the order of the effect of soil types was coastal bog solonchaks > alluvial soil > cinnamon soil > coral saline soil > overall samples, and their roles in improving the R2 of the model were 0.141, 0.085, 0.022, 0.012, respectively. Besides, stacking models with the SYSI fused provided the best prediction performances (R2 = 0.742, RMSE = 0.377, MAE = 0.362). This study introduces the concept of merging original images with SYSI, resulting in a significant improvement in the accuracy of soil salinity mapping in areas covered by vegetation. [ABSTRACT FROM AUTHOR]