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Dexing City is an important mining city in China, abounding in copper ore, lead ore, zinc ore, and other metal resources, and there are two large open-pit mines in its territory, Dexing Copper Mine and Yinshan Mine. The two open-pit mines have been expanding their mining production scale since 2005, with frequent mining activities; and the expansion of the pits and the discharge of solid waste will undoubtedly increase the land use and cause the destruction of vegetation. Therefore, we plan to visualize the change in vegetation cover in Dexing City from 2005 to 2020 and the expansion of the two open-pit mines by calculating changes of the Fractional Vegetation Cover (FVC) in the mining area using remote sensing technology. In this study, we calculated the FVC of Dexing City in 2005, 2010, 2015 and 2020 using data from NASA Landsat Database via ENVI image analysis software, plotted the FVC reclassified maps via ArcGIS, and conducted field investigations in the mining areas of Dexing City. In this way, we can visualize the spatial and temporal changes of vegetation cover in Dexing City from 2005 to 2020, and appreciate the situation of mining expansion and its solid waste discharge in Dexing City. The results of this study showed that the vegetation cover of Dexing City remained stable from 2005 to 2020, as the expansion of mining scale and mine pits was accompanied by active environmental management and land reclamation, setting a positive example for other mining cities. • The change in FVC from 2005 to 2020 was used to visualize the impact of mining expansion in Dexing City, China. • The paper presented Dexing City as an example of a mining city that has achieved "healthy" development. • This paper described the environmental hazards associated with open-pit mining. [ABSTRACT FROM AUTHOR]

Resource-based cities generally face the dual pressures of ecological damage and high carbon emissions. Taking the National Sustainable Development Plan for Resource-Based Cities (NSDP) issued in China as a quasi-natural experiment, this paper for the first time, uses the difference-in-difference method to test the coordinated effect of green expansion and carbon reduction brought by NSDP at the county level using rich remote sensing data. The results show that NSDP has significantly promoted the ecological quality improvement and CO 2 emission mitigation in resource-based cities, and the above coordinated benefits mainly occur in energy, mature, declining, northeastern, and municipal resource-based cities. Through the mechanism analysis, we find that NSDP mainly works by promoting the transformation of industry sector to agriculture, animal husbandry, accommodation and catering, finance, and environment management sectors, which are reflected in output, employment, product yield, land type and land transaction area. Further discussion on the tertiary industry suggests that developing tourism, easing financing constraint and strengthening environment regulation, are the key channels to borne fruit in green expansion and carbon reduction. • Analysis of placed-based policy on resource at the county-level. • Coordinated effects of green expansion and carbon reduction are tested. • Key areas of policy effects are identified. • Industrial structure transformation and its related mechanisms are discussed. [ABSTRACT FROM AUTHOR]

The paper examines the role of mountain runoff in the seasonal growth of oasis vegetation in the hyper-arid region of northwest China. Central to this examination is the development of a simple hydrologic model that relates hydrologic inflows and outflows estimated from remote sensing data (e.g., evapotranspiration, precipitation, snow accumulation, and snowmelt) to the calculation of runoff over a ten-year period (2000–2009). Modeled runoff is shown to reproduce the seasonal trends in hydrometric data fairly well, yielding R 2 's of 0.75 and 0.66 for stations in the upper reaches of the Shiyang and Hei River systems. Greater than 90% of the runoff from the Qilian Mountains to the oases occurs during the May–September period. Considerable discrepancy between modeled and observed runoff exists in the lower reaches of the rivers, where significant amounts of river water (>45%) are routinely extracted for cropland irrigation. Along the river systems, where water extraction and inflow of glacial meltwater are minor, model calculations replicate observed water yields much more closely. Analysis of seasonal trends in the contribution of snowmelt and rainfall to the return flow, reveals snowmelt as having the greatest influence in initiating the oasis growing period during the March-to-May period of each year. [ABSTRACT FROM AUTHOR]

Surface water provides a basic resource for life and is vital for maintaining climate stability, hydrological cycles, and natural ecosystems. Lakes, which are an important part of surface water, play a crucial role in sustaining the hydrological balance and maintaining a healthy environment. The onset of climate change and anthropogenic disturbance on lakes and the environment, however, have drastically changed the morphological characteristics of lakes, leading to undesirable negative effects on the ecological environment. In recent years, many lakes around the world are undergoing phenomenal changes due to climate change and human activities. These changes have greatly affected the availability of freshwater resources, leading to a series of regional ecological and environmental problems, which constrain the regional sustainable development. Changes in lake parameters often indicate environmental change. Therefore, it is of great practical significance to extract lake water information to reflect the water quantity changes in the lake, understand its implications, and come up with timely solutions. This paper summarizes the existing research methods of lake water estimation in China and abroad and draws the following conclusions: (1) In the study of lake water volume estimation, it can be roughly divided into with topographic data and without topographic data. The most representative methods are the estimation methods based on topographic parameters and the Triangle Irregular Network (TIN) model method. In the actual research process, most of the scarce data that are difficult to obtain are lake topographic data, prompting researchers to constantly put forward new methods; (2) From the perspective of research methods, estimation of lake water volume can be divided into remote sensing-based and non-remote sensing-based research. With the continuous improvement of scientific and technological levels, current geographical researchers tend to use remote sensing to monitor lake water volume; (3) Although researchers have proposed a series of models for lake water volume estimation for the long-time process. However, many models are based on the ideal static estimation process, which poses challenges for researchers to make a long-term dynamic estimation of lake storage capacity in the future. At the same time, the accuracy of the model can be a problem that researchers need to work out solutions in the future. Finally, combined with the current development of remote sensing technology, the application potential and the future prospect of remote sensing in lake monitoring are briefly discussed. • Lake water volume estimation, it can roughly be divided into with topographic data and without topographic data. • Estimation of lake water volume can be divided into remote sensing-based and non-remote sensing research. • The application potential and the future prospect of remote sensing in lake monitoring are briefly discussed. [ABSTRACT FROM AUTHOR]

This paper explores how human disturbance and hydrologic elements affect the spatial distribution pattern of plant diversity in the watershed, taking Shamu watershed in the World Natural Heritage Site as a case study. Spatial analysis of multisource remote sensing and plant diversity plots data were conducted using linear mixed effects models and structural equation models. Results revealed that the distribution of plant diversity in the watershed is mainly affected by human disturbance. However, under similar human disturbance levels, hydrologic elements also affect the plant diversity within the watershed. The topographic undulation and surface runoff significantly promote plant diversity, while the river network density, the watershed shape factor, the river longitudinal gradient do not. The influence of topographic undulation is more obvious than that of runoff on plant diversity, but the effect of topographic undulation and runoff on plant diversity is getting weaker from upstream to downstream within the watershed. In addition, the impact of hydrologic elements on plant diversity is mainly regulated by environmental factors Pre and Tem. The findings clarify how human disturbance and hydrologic elements affect plant diversity distribution within the watershed, optimizing the conservation theory of plant diversity resources and scientifically guiding the region's sustainable development. • Human disturbance contributes most to plant diversity. • Surface runoff and topographic undulation promote plant diversity. • Precipitation and temperature regulate the hydrological impact on plant diversity. • Weaker restrictions of hydrologic elements on plant diversity in downstream. [ABSTRACT FROM AUTHOR]

Understanding the impact of climate change on terrestrial wet and dry changes and the relationship between the two is of great significance to the sustainable development of terrestrial ecosystems. The farming-pastoral ecotone of northern China (FPENC) is an area that is sensitive to climate change, suffering from perennial drought and a clear aridification trend. Unlike previous single-factor, single-timescale studies, we identified aridification in the region based on a dataset established by remote sensing and ground-based monitoring stations from a combination of two perspectives: climate and soil. The results show that, in terms of climate, the period from 2000 to 2019 was the driest in the region during the last 120 years , and the summer drought was the most severe and shifted from a summer to spring drought; in terms of soil, the soil aridification trend in the region was severe, with 16.1% of the areas becoming significantly drier (P < 0.1) among the years and 41.6% in spring, respectively. Similar to climate change, soils exhibited recessive aridification due to the counterbalancing effect of the dry and wet seasons within the year. Then the coupling relationship between climate change and soil aridification was established in time and space. Moreover, the spatiotemporal response patterns of both were obtained. The results showed that the frequency of soil drought under meteorological drought conditions showed an increasing trend and that the sensitivity of soil drought occurrence increased. Among them, the effect of precipitation on relative soil moisture (RSM) was immediate, and the effect of prolonged warming on RSM is greater. The area of soil aridification that was caused by climatic aridification in spring accounted for 13.7% of the entire area. The regional aridification research mode proposed in this paper can provide ideas for subsequent studies. [Display omitted] • A comprehensive judgment mode of the regional aridification combining climate elements, soil moisture was established. • Over the last 20 years, the central part of the FPENC showed overall warming and wetting of the climate. • Over the last 20 years, the trend of the soil aridification in the central part of the FPENC remained severe. • The counterbalancing effect of the dry and wet seasons leads to recessive aridification. • The sensitivity of the occurrences of soil drought to the climate drought anomalies continuously became stronger. [ABSTRACT FROM AUTHOR]

Understanding the impact of climate change on terrestrial wet and dry changes and the relationship between the two is of great significance to the sustainable development of terrestrial ecosystems. The farming-pastoral ecotone of northern China (FPENC) is an area that is sensitive to climate change, suffering from perennial drought and a clear aridification trend. Unlike previous single-factor, single-timescale studies, we identified aridification in the region based on a dataset established by remote sensing and ground-based monitoring stations from a combination of two perspectives: climate and soil. The results show that, in terms of climate, the period from 2000 to 2019 was the driest in the region during the last 120 years , and the summer drought was the most severe and shifted from a summer to spring drought; in terms of soil, the soil aridification trend in the region was severe, with 16.1% of the areas becoming significantly drier (P < 0.1) among the years and 41.6% in spring, respectively. Similar to climate change, soils exhibited recessive aridification due to the counterbalancing effect of the dry and wet seasons within the year. Then the coupling relationship between climate change and soil aridification was established in time and space. Moreover, the spatiotemporal response patterns of both were obtained. The results showed that the frequency of soil drought under meteorological drought conditions showed an increasing trend and that the sensitivity of soil drought occurrence increased. Among them, the effect of precipitation on relative soil moisture (RSM) was immediate, and the effect of prolonged warming on RSM is greater. The area of soil aridification that was caused by climatic aridification in spring accounted for 13.7% of the entire area. The regional aridification research mode proposed in this paper can provide ideas for subsequent studies. [Display omitted] • A comprehensive judgment mode of the regional aridification combining climate elements, soil moisture was established. • Over the last 20 years, the central part of the FPENC showed overall warming and wetting of the climate. • Over the last 20 years, the trend of the soil aridification in the central part of the FPENC remained severe. • The counterbalancing effect of the dry and wet seasons leads to recessive aridification. • The sensitivity of the occurrences of soil drought to the climate drought anomalies continuously became stronger. [ABSTRACT FROM AUTHOR]

Timely and accurate monitoring of the spatiotemporal changes in drought is very important for the reduction in the social losses caused by drought. The Optimized Meteorological Drought Index (OMDI), originally established in southwestern China, showed great potential for drought monitoring over large regions on a large scale. However, the applicability of the index requires further evaluation, especially when used throughout China, which has a different agricultural divisions, variable climatic conditions, complex terrain and diverse land cover. In addition, the OMDI model relies on training data to construct local parameters for the model. On a large scale, it is of great significance to use multisource remote sensing data sets to construct OMDI model parameters. In this paper, the constrained optimization method was used to establish weights for the MODIS-derived Vegetation Conditional Index (VCI), TRMM-derived Precipitation Condition Index (PCI), and GLDAS-derived Soil Moisture Condition Index (SMCI) and calculate the OMDI based on the Standard Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI) and weather stations. The accuracy of the OMDI model was evaluated by using the correlation coefficient. Moreover, the spatiotemporal changes in drought were also analyzed through trend analysis, Mann-Kendall (MK) statistics and the Hurst index on the monthly and annual scales. The results showed that (1) the highest positive correlation between the OMDI and the SPI was SPI-1, which was higher than that for any other month interval, such as 3 months, 6 months, 9 months and 12 months of the SPI. The results indicated that the OMDI was suitable to monitor meteorological drought. (2) In the nine agricultural subareas in China, the degree of drought in the Yangtze River (DYR) area had the most severe evolution and change frequency. This region was very sensitive to drought in the past two decades. (3) The area with OMDI variation coefficient less than 0.1 accounted for 94%, indicating that the degree of drought fluctuates little; The linear tendency rate is 0.0004, and the area greater than 0 reaches 66.44%, indicating that the drought is developing in a lightning trend. (4) The Hurst index value is mostly higher than 0.5 (the area ratio is 56.31%), and the area of "Positive-Consistent" and "Negative- Opposite" accounted for 54.02%, indicating that more than half of China's area drought changes will show a trend of mitigation in the future. [Display omitted] • OMDI model was improved based on multi-source remote sensing data sets. •OMDI model was improved based on constrained optimization theory. •Drought dynamics was monitored on large scale based on remote sensing technology. •Drought conditions in China were valuated both on month scale and annual scale. •Change trend in the future was analyzed using Linear trend and OMDI Hurst index. [ABSTRACT FROM AUTHOR]

Apparent color of seawater is a key variable that can be used to describe the optical properties of seawater and plays a crucial role in understanding surface marine ecosystems and processes. The apparent color of seawater traditionally determined from a hand-held Forel-Ule color comparator scale can now be derived from satellite ocean color end-product water leaving radiance or remote sensing reflectance. However, this retrieval algorithm has not been verified in China's offshore waters, which are some of the most turbid waters in the world. Thus, the precision of the satellite-derived Forel-Ule index (FUI satellite) is unknown in this area. In this paper, FUI satellite values is evaluated using in-situ FUI (FUI insitu) observations obtained in the first large-scale systematic survey on the marine environment in China, and the results show that there is a generally good agreement (r = 0.75, N = 2390, p = 0.00, MAD = 2.61, MUPD = 35.02%) between them. Uncertainties of satellite derivation along the coast of the East China Sea (ECS) and the Beibu Gulf are identified. Moreover, the probability of uncertainties of FUI satellite as a function of FUI insitu can be well fitted by Gaussian function (r2 = 0.99). Our study also demonstrates that the radial basis function, which uses linear function as the basis function, is the optimal method on the spatial interpolation of FUI insitu. The FUI satellite is still in good agreement with the interpolated FUI insitu field (r = 0.77, N = 265302, p = 0.00, MAD = 1.73, MUPD = 36.06%), which reveals that remote sensing techniques could be an integral tool for operational environmental monitoring of water quality in China's offshore waters. • Water color FU scale derived by remote-sensing reflectances in China's offshore waters are evaluated by in situ measurements. • Several sea areas of inaccurate satellite derivation have been identified. • Linear radial basis function interpolation is used to reconstruct the observed FU field, due to its good performance. [ABSTRACT FROM AUTHOR]