Solar photovoltaic program helps turn deserts green in China: Evidence from satellite monitoring.

Solar energy is considered one of the key solutions to the growing demand for energy and to reducing greenhouse gas emissions. Thanks to the relatively low cost of land use for solar energy and high power generation potential, a large number of photovoltaic (PV) power stations have been established in desert areas around the world. Despite the contribution to easing the energy crisis and combating climate change, large-scale construction and operation of PV power stations can change the land cover and affect the environment. However, few studies have focused on these special land cover changes, especially vegetation cover changes, which hinders understanding the effects of the extensive development of solar energy. Here, we used Continuous Change Detection and Classification - Spectral Mixture Analysis (CCDC-SMA) based on Landsat images to monitor changes in vegetation abundance before and after the PV power stations deployment. To reduce the interference of PV shading on vegetation abundance estimation, we improved the vegetation (VG) fraction from SMA and developed the Photovoltaics-Adjusted Vegetation (PAVG) fraction for vegetation abundance measurements in PV power stations. Results show that PV power stations in China's 12 biggest deserts expanded from 0 to 102.56 km2 from 2011 to 2018, mainly distributed in the central part of north China. The desert vegetation in the deployment area of PV power stations presented a significant greening trend. Compared to 2010, the greening area reached 30.80 km2, accounting for 30% of the total area of PV power stations. Overall, the large-scale deployment of PV power stations has promoted desert greening, primarily due to government-led Photovoltaic Desert Control Projects and favorable climatic change. This study shows the great benefits of PV power stations in combating desertification and improving people's welfare, which bring sustainable economic, ecological and social prosperity in sandy ecosystems.

Estimation of heavy metal concentrations in reclaimed mining soils using reflectance spectroscopy.

A selection of soil samples from reclaimed mining areas were prepared to establish the quantitative inversion models of the soil heavy metal (As, Zn, Cu, Cr, and Pb) concentrations. The concentrations of the soil heavy metals and the visible and near-infrared spectra of the soil samples were obtained in a darkroom. Firstly, smoothing processing was used to smooth the noise in the original spectra, and the spectral transformation techniques of first derivative (FD), continuum removal (CR), and standard normal variate (SNV) were used to promote the model stability and the accuracy of the prediction. Through correlation analysis, the feature bands of the different transformed spectra were extracted. Finally, three different inversion models were adopted and compared, i. e., traditional multiple linear regression (MLR), partial least squares regression (PLSR), and least squares support vector machines (LS-SVM) modeling. The results indicated that: (1) the stability and accuracy of the inversion models established by the different transformed spectra was high, in which LS-SVM was better than PLSR, and PLSR was better than MLR (except for a few cases); and (2) the spectral features extracted from the different transformed spectra had a certain influence on the inversion model, in which the results based on CR transformation and SNV transformation were better than the FD transformation. Therefore, the quantitative estimation of heavy metal concentrations by the use of reflectance spectroscopy is feasible, and the pre-processing is essential to improve the accuracy of the model.

Change in water column total chlorophyll-a in the Mediterranean revealed by satellite observation.

Chlorophyll-a (Chl-a) is a crucial pigment in algae and macrophytes, which makes the concentration of total Chl-a in the water column (total Chl-a) an essential indicator for estimating the primary productivity and carbon cycle of the ocean. Integrating the Chl-a concentration at different depths (Chl-a profile) is an important way to obtain the total Chl-a. However, due to limited cost and technology, it is difficult to measure Chl-a profiles directly in a spatially continuous and high-resolution way. In this study, we proposed an integrated strategy model that combines three different machine learning methods (PSO-BP, random forest and gradient boosting) to predict the Chl-a profile in the Mediterranean by using several sea surface variables (photosynthetically active radiation, spectral irradiance, sea surface temperature, wind speed, euphotic depth and KD490) and subsurface variables (mixed layer depth) observed by or estimated from satellite and BGC-Argo float observations. After accuracy estimation, the integrated model was utilized to generate the time series total Chl-a in the Mediterranean from 2003 to 2021. By analysing the time series results, it was found that seasonal fluctuation contributed the most to the variation in total Chl-a. In addition, there was an overall decreasing trend in the Mediterranean phytoplankton biomass, with the total Chl- decreasing at a rate of 0.048 mg/m2 per year, which was inferred to be related to global warming and precipitation reduction based on comprehensive analysis with sea surface temperature and precipitation data.

Error-Reduced Digital Elevation Model of the Qinghai-Tibet Plateau using ICESat-2 and Fusion Model.

The Qinghai-Tibet Plateau (QTP) holds significance for investigating Earth's surface processes. However, due to rugged terrain, forest canopy, and snow accumulation, open-access Digital Elevation Models (DEMs) exhibit considerable noise, resulting in low accuracy and pronounced data inconsistency. Furthermore, the glacier regions within the QTP undergo substantial changes, necessitating updates. This study employs a fusion of open-access DEMs and high-accuracy photons from the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2). Additionally, snow cover and canopy heights are considered, and an ensemble learning fusion model is presented to harness the complementary information in the multi-sensor elevation observations. This innovative approach results in the creation of HQTP30, the most accurate representation of the 2021 QTP terrain. Comparative analysis with high-resolution imagery, UAV-derived DEMs, control points, and ICESat-2 highlights the advantages of HQTP30. Notably, in non-glacier regions, HQTP30 achieved a Mean Absolute Error (MAE) of 0.71 m, while in glacier regions, it reduced the MAE by 4.35 m compared to the state-of-the-art Copernicus DEM (COPDEM), demonstrating its versatile applicability.

Adaptive affinity propagation with spectral angle mapper for semi-supervised hyperspectral band selection.

Band selection is a commonly used approach for dimensionality reduction in hyperspectral imagery. Affinity propagation (AP), a new clustering algorithm, is addressed in many fields, and it can be used for hyperspectral band selection. However, this algorithm cannot get a fixed number of exemplars during the message-passing procedure, which limits its uses to a great extent. This paper proposes an adaptive AP (AAP) algorithm for semi-supervised hyperspectral band selection and investigates the effectiveness of distance metrics for improving band selection. Specifically, the exemplar number determination algorithm and bisection method are addressed to improve AP procedure, and the relations between selected exemplar numbers and preferences are established. Experiments are conducted to evaluate the proposed AAP-based band selection algorithm, and the results demonstrate that the proposed method outperforms other popular methods, with lower computational cost and robust results.

Multiple classifier system for remote sensing image classification: a review.

Over the last two decades, multiple classifier system (MCS) or classifier ensemble has shown great potential to improve the accuracy and reliability of remote sensing image classification. Although there are lots of literatures covering the MCS approaches, there is a lack of a comprehensive literature review which presents an overall architecture of the basic principles and trends behind the design of remote sensing classifier ensemble. Therefore, in order to give a reference point for MCS approaches, this paper attempts to explicitly review the remote sensing implementations of MCS and proposes some modified approaches. The effectiveness of existing and improved algorithms are analyzed and evaluated by multi-source remotely sensed images, including high spatial resolution image (QuickBird), hyperspectral image (OMISII) and multi-spectral image (Landsat ETM+). Experimental results demonstrate that MCS can effectively improve the accuracy and stability of remote sensing image classification, and diversity measures play an active role for the combination of multiple classifiers. Furthermore, this survey provides a roadmap to guide future research, algorithm enhancement and facilitate knowledge accumulation of MCS in remote sensing community.

A global record of annual terrestrial Human Footprint dataset from 2000 to 2018.

Human Footprint, the pressure imposed on the eco-environment by changing ecological processes and natural landscapes, is raising worldwide concerns on biodiversity and ecological conservation. Due to the lack of spatiotemporally consistent datasets of Human Footprint over a long temporal span, many relevant studies on this topic have been limited. Here, we mapped the annual dynamics of the global Human Footprint from 2000 to 2018 using eight variables that reflect different aspects of human pressures. The accuracy assessment revealed a good agreement between our mapped results and the previously developed datasets in different years. We found more than two million km2 of wilderness (i.e., regions with Human Footprint values below one) were lost over the past two decades. The biome dominated by mangroves experienced the most significant loss (i.e., above 5%) of wilderness, likely attributed to intensified human activities in coastal areas. The derived annual and spatiotemporally consistent global Human Footprint can be a fundamental dataset for many relevant studies about human activities and natural resources.

Estimating the distribution trend of soil heavy metals in mining area from HyMap airborne hyperspectral imagery based on ensemble learning.

The problem of heavy metal pollution of soils in China is severe. The traditional spectral methods for soil heavy metal monitoring and assessment cannot meet the needs for large-scale areas. Therefore, in this study, we used HyMap-C airborne hyperspectral imagery to explore the estimation of soil heavy metal concentration. Ninety five soil samples were collected synchronously with airborne image acquisition in the mining area of Yitong County, China. The pre-processed spectrum of airborne images at the sampling point was then selected by the competitive adaptive reweighted sampling (CARS) method. The selected spectral features and the heavy metal data of soil samples were inverted to establish the inversion model. An ensemble learning method based on a stacking strategy is proposed for the inversion modeling of soil samples and image data. The experimental results show that this CARS-Stacking method can better predict the four heavy metals in the study area than other methods. For arsenic (As), chromium (Cr), lead (Pb), and zinc (Zn), the determination coefficients of the test data set (RP2) are 0.73, 0.63, 0.60, and 0.71, respectively. It was found that the estimated results and the distribution trend of heavy metals are almost the same as in actual ground measurements.

Estimation of the spatial distribution of heavy metal in agricultural soils using airborne hyperspectral imaging and random forest.

Hyperspectral imaging, with the hundreds of bands and high spectral resolution, offers a promising approach for estimation of heavy metal concentration in agricultural soils. Using airborne imagery over a large-scale area for fast retrieval is of great importance for environmental monitoring and further decision support. However, few studies have focused on the estimation of soil heavy metal concentration by airborne hyperspectral imaging. In this study, we utilized the airborne hyperspectral data in LiuXin Mine of China obtained from HySpex VNIR-1600 and HySpex SWIR-384 sensor to establish the spectral-analysis-based model for retrieval of heavy metals concentration. Firstly, sixty soil samples were collected in situ, and their heavy metal concentrations (Cr, Cu, Pb) were determined by inductively coupled plasma-mass spectrometry analysis. Due to mixed pixels widespread in airborne hyperspectral images, spectral unmixing was conducted to obtain purer spectra of the soil and to improve the estimation accuracy. Ten of estimated models, including four different random forest models (RF)-standard random forest (SRF), regularized random forest (RRF), guided random forest (GRF), and guided regularized random forest (GRRF)-were introduced for hyperspectral estimated model in this paper. Compared with the estimation results, the best accuracy for Cr, Cu, and Pb is obtained by RF. It shows that RF can predict the three heavy metals better than other models in this area. For Cr, Cu, Pb, the best model of RF yields Rp2 values of 0.75,0.68 and 0.74 respectively, and the values of RMSEp are 5.62, 8.24, and 2.81 (mg/kg), respectively. The experiments show the average estimated values are close to the truth condition and the high estimated values concentrated near several industries, valifating the effectiveness of the presented method.

[Hyperspectral remote sensing image classification based on radical basis function neural network].

Based on the radial basis function neural network (RBFNN) theory and the specialty of hyperspectral remote sensing data, the effective feature extraction model was designed, and those extracted features were connected to the input layer of RBFNN, finally the classifier based on radial basis function neural network was constructed. The hyperspectral image with 64 bands of OMIS II made by Chinese was experimented, and the case study area was zhongguancun in Beijing. Minimum noise fraction (MNF) was conducted, and the former 20 components were extracted for further processing. The original data (20 dimension) of extraction by MNF, the texture transformation data (20 dimension) extracted from the former 20 components after MNF, and the principal component analysis data (20 dimension) of extraction were combined to 60 dimension. For classification by RBFNN, the sizes of training samples were less than 6.13% of the whole image. That classifier has a simple structure and fast convergence capacity, and can be easily trained. The classification precision of radial basis function neural network classifier is up to 69.27% in contrast with the 51.20% of back propagation neural network (BPNN) and 40. 88% of traditional minimum distance classification (MDC), so RBFNN classifier performs better than the other three classifiers. It proves that RBFNN is of validity in hyperspectral remote sensing classification.

Assessing the Spatiotemporal Variation and Impact Factors of Net Primary Productivity in China.

In this study, the net primary productivity (NPP) in China from 2001 to 2012 was estimated based on the Carnegie-Ames-Stanford Approach (CASA) model using Moderate Resolution Imaging Spectroradiometer (MODIS) and meteorological datasets, and the accuracy was verified by a ChinaFLUX dataset. It was found that the spatiotemporal variations in NPP present a downward trend with the increase of latitude and longitude. Moreover, the influence of climate change on the evolution of NPP shows that NPP has had different impact factors in different regions and periods over the 12 years. The eastern region has shown the largest increase in gross regional product (GRP) and a significant fluctuation in NPP over the 12 years. Meanwhile, NPP in the eastern and central regions is significantly positively correlated with annual solar radiation, while NPP in these two regions is significantly negatively correlated with the growth rate of GRP. It is concluded that both the development of the economy and climate change have influenced NPP evolution in China. In addition, NPP has shown a steadily rising trend over the 12 years as a result of the great importance attributed to ecological issues when developing the economy.

Spatiotemporal Pattern of PM2.5 Concentrations in Mainland China and Analysis of Its Influencing Factors using Geographically Weighted Regression.

Based on annual average PM2.5 gridded dataset, this study first analyzed the spatiotemporal pattern of PM2.5 across Mainland China during 1998-2012. Then facilitated with meteorological site data, land cover data, population and Gross Domestic Product (GDP) data, etc., the contributions of latent geographic factors, including socioeconomic factors (e.g., road, agriculture, population, industry) and natural geographical factors (e.g., topography, climate, vegetation) to PM2.5 were explored through Geographically Weighted Regression (GWR) model. The results revealed that PM2.5 concentrations increased while the spatial pattern remained stable, and the proportion of areas with PM2.5 concentrations greater than 35 µg/m3 significantly increased from 23.08% to 29.89%. Moreover, road, agriculture, population and vegetation showed the most significant impacts on PM2.5. Additionally, the Moran's I for the residuals of GWR was 0.025 (not significant at a 0.01 level), indicating that the GWR model was properly specified. The local coefficient estimates of GDP in some cities were negative, suggesting the existence of the inverted-U shaped Environmental Kuznets Curve (EKC) for PM2.5 in Mainland China. The effects of each latent factor on PM2.5 in various regions were different. Therefore, regional measures and strategies for controlling PM2.5 should be formulated in terms of the local impacts of specific factors.

An Adaptive Weighting Algorithm for Interpolating the Soil Potassium Content.

The concept of spatial interpolation is important in the soil sciences. However, the use of a single global interpolation model is often limited by certain conditions (e.g., terrain complexity), which leads to distorted interpolation results. Here we present a method of adaptive weighting combined environmental variables for soil properties interpolation (AW-SP) to improve accuracy. Using various environmental variables, AW-SP was used to interpolate soil potassium content in Qinghai Lake Basin. To evaluate AW-SP performance, we compared it with that of inverse distance weighting (IDW), ordinary kriging, and OK combined with different environmental variables. The experimental results showed that the methods combined with environmental variables did not always improve prediction accuracy even if there was a strong correlation between the soil properties and environmental variables. However, compared with IDW, OK, and OK combined with different environmental variables, AW-SP is more stable and has lower mean absolute and root mean square errors. Furthermore, the AW-SP maps provided improved details of soil potassium content and provided clearer boundaries to its spatial distribution. In conclusion, AW-SP can not only reduce prediction errors, it also accounts for the distribution and contributions of environmental variables, making the spatial interpolation of soil potassium content more reasonable.

[Spectral feature-based hyperspectral RS image retrieval].

Oriented to the demands of vast RS information management for RS image retrieval, the applications of spectral features are discussed by taking hyperspectral RS image as an example. It is proposed that spectral features-based retrieval includes two modes: retrieval based on point mask an dpolygon mask. The most key issues in retrieval are spectral features extraction andsimilarity measure. The spectral vector can be used to retrieval directly, and the spectral angle and spectral information divergence (SID) are effective in similarity measure. The local maximum and minimum in reflectance spectral curve, corresponding to reflectance apex and absorption apex, can be used to retrieval also, but effective matching strategy should be adopted. The quantitative indexes for spectral curves such as moment, fractal and entropy are not suitable to retrieval because of poor similarity measure performance.

[The analysis of error sources for SAM and its improvement algorithms].

Based on the analysis of the error sources for spectral anglemapping (SAM), several key elements are pointed out, i.e. the change of wave band location, the change of the attribution ratio, the random change of attribution, and the whole translation of wave band. After the above-mentioned four error sources are analyzed, the authors present several improvement algorithms, viz. calculating the spectral angle with grouping, normalization and intersection. The grouping method can resolve the pseudo-similar problem, because it considers both spectral global features and local features. Calculating spectral angle with normalization restrains those random errors in original data by normalizing the spectral vectors. The intersection method can eliminate the error elicited by the whole wave translation. Therefore, it can be employed to correctly identify spectral class. Experiments show that those improvement algorithms are effective and can be used to process spectral data with errors.

Ensemble learning for spatial interpolation of soil potassium content based on environmental information.

One important method to obtain the continuous surfaces of soil properties from point samples is spatial interpolation. In this paper, we propose a method that combines ensemble learning with ancillary environmental information for improved interpolation of soil properties (hereafter, EL-SP). First, we calculated the trend value for soil potassium contents at the Qinghai Lake region in China based on measured values. Then, based on soil types, geology types, land use types, and slope data, the remaining residual was simulated with the ensemble learning model. Next, the EL-SP method was applied to interpolate soil potassium contents at the study site. To evaluate the utility of the EL-SP method, we compared its performance with other interpolation methods including universal kriging, inverse distance weighting, ordinary kriging, and ordinary kriging combined geographic information. Results show that EL-SP had a lower mean absolute error and root mean square error than the data produced by the other models tested in this paper. Notably, the EL-SP maps can describe more locally detailed information and more accurate spatial patterns for soil potassium content than the other methods because of the combined use of different types of environmental information; these maps are capable of showing abrupt boundary information for soil potassium content. Furthermore, the EL-SP method not only reduces prediction errors, but it also compliments other environmental information, which makes the spatial interpolation of soil potassium content more reasonable and useful.