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Copyright of Landscape Architecture (1673-1530) is the property of Beijing Landscape Architecture Journal Periodical Office Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective］ To investigate the spatial and temporal variations of land surface temperature (LST) in central Yunnan urban agglomeration and its relationship with land use/land cover (LULC) types. Methods］ Based on Google Earth Engine (GEE) platform, LST was inverted using Landsat satellite image data of central Yunnan urban agglomeration region from 199() to 2020, and was analyzed using Sen-MK trend analysis, mean-stand a rd deviation method, urban thermal field variance index (UTFVT) and correlation analysis were used to quantitatively analyze the spatial and temporal variations of LST and its response to LULC types in central Yunnan urban agglomeration from 199() to 202(). Results］ (1) There were significant spatial and temporal differences in interannual temperature classes in central Yunnan urban agglomeration. The mesothermal area always occupied an absolutely dominant position (40%), but its area share decreased year by year. (2) From 1 99() to 2020, the areas with UTFVT () showed an increasing trend year by year, and the areas with UTFVT〉0.02 showed a decreasing trend, and the ecological and thermal environments were gradually improved. (3) There were response differences in LST for different LULC types. Among them, LST was significantly positively correlated with the proportion of built-up area (r > 0,70). LST was always the lowest in the water area, with a multi-year average temperature of 17.38 °C, while the highest temperature was found in the area covered by built-up land and cropland, with multi-year average temperatures above 21 °C. (4) The rapid expansion of cities in the central Yunnan urban agglomeration had resulted in significant changes in LULQ The built-up land area had increased by more than 1 ()0.29%, and the rapid development of cities had increased LST from 199() to 2020. Conclusion] LULC is an important factor affecting the LST, and reasonable configuration of LULC structure can improve the thermal environment of urban agglomeration. The study's results can provide important insights for rational development planning and improvement of ecological conditions in the urban agglomeration of central Yunnan. [ABSTRACT FROM AUTHOR]

Copyright of Arid Land Geography is the property of Chinese Academy of Sciences, Xinjiang Institute of Ecology & Geography and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Bulletin of Soil & Water Conservation is the property of Bulletin of Soil & Water Conservation Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[Objective] To investigate the spatial and temporal variations of land surface temperature (LST) in central Yunnan urban agglomeration and its relationship with land use/land cover (LULC) types. [Methods] Based on Google Earth Engine (GEE) platform, LST was inverted using Landsat satellite image data of central Yunnan urban agglomeration region from 1990 to 2020, and was analyzed using Sen-MK trend analysis, mean-standard deviation method, urban thermal field variance index (UTFVI) and correlation analysis were used to quantitatively analyze the spatial and temporal variations of LST and its response to LULC types in central Yunnan urban agglomeration from 1990 to 2020. [Results] (1) There were significant spatial and temporal differences in interannual temperature classes in central Yunnan urban agglomeration. The mesothermal area always occupied an absolutely dominant position (40%), but its area share decreased year by year. (2) From 1990 to 2020, the areas with UTFVI 0 showed an increasing trend year by year, and the areas with UTFVI > 0.02 showed a decreasing trend, and the ecological and thermal environments were gradually improved. (3) There were response differences in LST for different LULC types. Among them, LST was significantly positively correlated with the proportion of built-up area (r > 0.70). LST was always the lowest in the water area, with a multi-year average temperature of 17.38 ℃, while the highest temperature was found in the area covered by built-up land and cropland, with multi-year average temperatures above 21 ℃. (4) The rapid expansion of cities in the central Yunnan urban agglomeration had resulted in significant changes in LULC. The built-up land area had increased by more than 100.29%, and the rapid development of cities had increased LST from 1990 to 2020. [Conclusion] LULC is an important factor affecting the LST, and reasonable configuration of LULC structure can improve the thermal environment of urban agglomeration. The study's results can provide important insights for rational development planning and improvement of ecological conditions in the urban agglomeration of central Yunnan.

With global warming, extreme heat events have occurred more frequently, posing significant threats to human health, and social and economic development in many regions. This study comprehensively utilizes remote sensing data and socio-economic statistics to develop a heat vulnerability evaluation index system based on the "exposure-sensitivity-adaptability" framework. This approach quantifies heat vulnerability and reveals its spatiotemporal characteristics in the Guanzhong region of Shaanxi Province, China, from 2005 to 2020. The results indicate that: (1) The high-temperature zones of summer land surface temperature in the Guanzhong region expanded from 2005 to 2020. The spatial pattern of land surface temperature remained relatively consistent, with sub-high-temperature and high-temperature zones distributed sporadically in the central area, while sub-low-temperature and low-temperature zones were concentrated in the southern Qinling Mountains. (2) Heat vulnerability in the Guanzhong region exhibited significant spatial clustering, mirroring the distribution pattern of surface temperature. Higher vulnerability zones were predominantly located in the central plains, whereas lower and low vulnerability zones were mainly found in the southern Qinling Mountains. (3) The heat vulnerability index of the Guanzhong region showed a decreasing trend from 2005 to 2020, with the proportion of higher and high vulnerability areas declining from 48.20% in 2005 to 37.49% in 2020. (4) From 2005 to 2010, changes in heat vulnerability levels were relatively minor, whereas from 2010 to 2020, the extent of changes increased substantially, with most regions experiencing a decrease in vulnerability levels. This change was primarily characterized by medium vulnerability shifting to lower vulnerability, higher vulnerability reducing to medium vulnerability, and high vulnerability decreasing to higher vulnerability. The findings offer valuable insights for enhancing heat adaptability and mitigating heat vulnerability. [ABSTRACT FROM AUTHOR]

Abstract: The spatiotemporal variations of water use efficiency (WUE) and its relationship with drought and land surface temperature (LST) on the Loess Plateau are crucial for assessing the maximum vegetation carrying capacity in this region, known as the most severely eroded area globally and the largest greening area in China. This study employed Theil-Sen trend analysis and the first-order differencing relative contribution method to examine the spatiotemporal changes in WUE across different seasons on the Loess Plateau from 2001 to 2021 and to evaluate the contributions of drought and LST to these changes. The results reveal that: (1) The average WUE values in spring and autumn are below 2.0 g C·m-2·mm-1, while in summer, the average WUE exceeds 2.0 g C·m-2·mm-1. In spring and autumn, WUE is higher in cultivated land and forest areas compared to grassland areas, whereas in summer, WUE is lowest in cultivated land, followed by forest areas, and highest in grassland. (2) WUE remains stable in spring and summer, displaying a spatial distribution of "reduction in the central part, stability in the western and eastern parts." The rate of decline in WUE is greater in grassland areas than in forest and cultivated land areas. In autumn, WUE shows an increasing trend, with a higher rate of increase observed in grassland areas than in forest and cultivated land areas, exhibiting a spatial pattern of "increase in the northwest, decrease in the southeast." (3) In spring and summer, LST positively contributes to WUE changes, with the most significant impacts in grassland areas. In autumn, LST negatively affects WUE in grassland and forest areas but positively influences WUE in cultivated land areas. Drought positively contributes to WUE changes in spring and autumn, while it negatively affects WUE in summer. These findings enhance the understanding of the interactions between drought, LST, and water resources in the context of climate change and ecological restoration efforts on the Loess Plateau. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Drought is one of the most serious natural disasters with the widest distribution, the highest frequency, and the greatest economic losses in the world. A grave threat has been posed to agricultural production, food security, and socioeconomic development. It is of great significance to timely and accurately monitor the drought at the regional scale. In this study, the NDVI-LST feature space was constructed using the MOD11A2 surface temperature 8-d synthetic product and MOD13A1 vegetation index 16-d synthetic product. The data was collected from the land surface temperature (LST) and normalized difference vegetation index (NDVI). Temperature vegetation dryness index (TVDI) was captured from the Guanzhong areas in a 20-year time series from 2001 to 2020. The spatial and temporal distribution of drought was analyzed to determine the causes and future trends of natural disasters. The results show that: 1) It was feasible to carry out drought monitoring on a regional scale using the TVDI remote sensing index. The average TVDI was 0.57 over the past 20 years. The drought type was in a normal state in general. The highest annual average TVDI was found in 2013 and the lowest in 2006. The average TVDI value fluctuated greatly during the 20-year period. Drought also shared strong regional characteristics, indicating a spatial distribution pattern of wet in the southwest, dry in the center and northeast, and normal in the north. 2) The spatial and temporal trends of drought showed that 82.00% of the areas had relatively stable droughts, and only 8.05% of the areas were with improved droughts. As such, there was a relatively small variation in the drought condition; Combined with the Hurst index, the future trend of drought was characterized by a relatively small amount of change. There was an anti-sustainable trend of drought in the future; Combined Hurst index and TVDI trend, 7.71% of the study area was continuously wet in the future, and 36.24% of the area was changed from drought to wet. 3) The TVDI value was positively correlated with the air temperature, while negatively correlated with the precipitation. There was a similar spatial distribution pattern of the 20-year average TVDI and the annual average air temperature. The air temperature and precipitation shared a similar distribution in the spatial pattern. The degree of drought depended mainly on the air temperature and precipitation. Meanwhile, the degree of aridity was negatively correlated with both elevation and slope. The arid and more arid types were mainly distributed in the central plains with low elevations and small slopes. While the moist and more moist types occupied a larger area in the mountainous areas south of the Qinling Mountains with high elevation. The TVDI values varied with months in the different land use types in the study area. The cropland and construction land presented the highest TVDI values, with the annual mean values of 0.76 and 0.75, respectively, whereas, the woodland was the smallest TVDI value, with an annual mean value of 0.45. Therefore, the woodland and grassland cover can be expected to positively mitigate the drought. The TVDI can be used to quantify the drought conditions from the regional scale. Remote sensing drought monitoring can also provide a theoretical foundation and scientific basis for the formulation of drought countermeasures in Guanzhong areas. [ABSTRACT FROM AUTHOR]

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Remote Sensing for Natural Resources is the property of Remote Sensing for Natural Resources Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Soil salinization is an important factor that jeopardizes agricultural production and ecological environment. Rapid and accurate acquisition of soil salinity information in farmland is instructive for sustainable agricultural development and land resource management. In order to improve the accuracy of soil salinity prediction under vegetation cover conditions by satellite remote sensing, the eighth Agricultural Division of Xinjiang Production and Construction Corps was taken as the study area in this study. The soil surface (0-20 cm) samples were collected under high fractional vegetation cover conditions in July and August 2023, respectively, and synchronized satellite images were acquired. Sentinel-1, Sentinel-2 and environment variables provide 3 different types of explanatory variables. The dataset A (polarization indices, spectral indices), dataset B (polarization indices, environment variables), dataset C (spectral indices, environment variables), and dataset D (polarization indices, spectral indices, environment variables) were constructed separately from different combinations of Sentinel-1 radar information, Sentinel-2 multispectral information and environment variables. Then, three integrated machine learning algorithms, namely adaptive boosting (AdaBoost), gradient boost regression Tree (GBRT) and eXtreme gradient boosting tree (XGBoost), were applied to construct soil salinity inversion models based on different datasets. The results showed that Models constructed from dataset B (polarization indices and environmental variables) and C (spectral indices and environmental variables) achieved higher prediction accuracies compared to dataset A (polarization indices and spectral indices). It is shown that when environmental variables are involved in the prediction of soil salinity, the model effect is more effective than the model constructed by polarization and spectral indices suggesting that the model effects are more effective than those constructed from polarization and spectral indices. When environmental variables were applied to dataset D together with polarization indices and spectral indices, the prediction accuracy of all models constructed based on dataset D are generally higher than those constructed on dataset A, B, and C, and that the synergy of environmental variables with radar data and multispectral data can effectively improve the model accuracy. Radar information, spectral information and environmental variables are complementary in soil salinity prediction. Based on the correlation analysis, it can be seen that radar information, spectral information and environmental variables can be used as effective characteristic variables for soil salinity prediction in the study area. It was worth noting that the correlation between topographic factors and land surface temperature with soil salinity is relatively high, with the highest correlation between elevation and surface soil salinity (r = 0.52). Considering the spatial characteristics of soil salinity distribution in the study area can provide effective characteristic variables for soil salinity prediction under vegetation cover condition. In all datasets, the XGBoost had the best performance, followed by GBRT, and the AdaBoost had a large validation error. The D-XGBoost model having the highest accuracy with a validation set R² of 0.72, an RMSE of 2.40 g/kg, and an MAE of 1.29 g/kg. The integrated learning algorithms based on the combination of multiple source variables has a strong nonlinear fitting ability. XGBoost can better model the complex nonlinear relationship between soil salinity content and remote sensing information, environmental factors, and obtain ideal fitting results. The joint application of multi-source remote sensing data and integrated learning algorithms can obtain the ideal soil salinity inversion accuracy under vegetation cover conditions. This study provides an effective technical means for real-time dynamic monitoring of soil salinity by satellite remote sensing in farmland to optimize irrigation strategies and manage saline soils comprehensively in Xinjiang. [ABSTRACT FROM AUTHOR]

In order to investigate the risk of low temperature freezing damage in winter for thermophilic vegetables in greenhouse in Fujian province, this study utilized the meteorological monitoring data from representative plastic greenhouses in Fuqing and Qingliu from December 2020 to February 2023, as well as assimilated land surface minimum temperature data during the same period. Regression models were developed for the minimum temperature inside the greenhouse under different weather conditions, such as sunny, cloudy, overcast and rainy. Based on data from the 66 meteorological stations in Fujian province from 1991 to 2020, daily minimum temperature inside greenhouse were inferred from December to February in previous years. Based on the low-temperature data inside the greenhouses, authors constructed a risk classification index system for freezing damage of thermophilic vegetables in greenhouses in Fujian. The weights of the indicators were determined by using the subjective-objective comprehensive weight method, and a risk assessment model for freezing damage was established to carry out the risk zoning of freezing damage in greenhouse for thermophilic vegetables in Fujian province. The results showed that: (1) the regression models for the minimum temperature in the greenhouse during winter under different weather conditions, including sunny, cloudy, overcast, and rainy, had R² greater than 0.75, and the absolute errors of the model validation were 0.6°C, 1.4°C, 1.0°C, and 0.5°C, respectively. (2) The low-risk area for cold and freezing damage of thermophilic vegetables in greenhouse mainly distributed in the coastal counties and cities south of Fuzhou with elevations below 200m. The medium-risk area mainly concentrated in the coastal areas of the north and the inland areas of the south with elevations between 200m and 500m. The moderate-risk area was mainly distributed in the middle and high altitude mountainous areas with elevations between 500m and 1000m in the southern inland and the coastal areas with elevations between 200m and 500m in the north, basically surrounding the areas with severe freezing damage and extending to lower elevations. The high-risk area mainly distributed in the areas above 1000m in Wuyi mountains, Jiufeng mountains, Daiyun mountains, Boping ridge, and Daimao mountain, where significant losses occurred and were not suitable for greenhouse thermophilic vegetable cultivation. [ABSTRACT FROM AUTHOR]

Copyright of South China Fisheries Science is the property of South China Fisheries Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Environmental Science & Technology (10036504) is the property of Editorial Board of Environmental Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Arid Zone Research / Ganhanqu Yanjiu is the property of Arid Zone Research Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of South China Normal University (Natural Science Edition) / Huanan Shifan Daxue Xuebao (Ziran Kexue Ban) is the property of Journal of South China Normal University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[Objective] The aim of this study is to explore relations between different temporal scales TVDI and soil moisture and determine the best temporal scale inversing soil moisture. It is important to accurately acquire the soil moisture information for the monitoring and the response to drought disaster. [Methods] There are many methods to monitor drought using remotely sensed data. An index in combination with land surface temperature and vegetation index is frequently utilized. In this research, the remotely sensed data including reflectance and land surface temperature on three temporal scales (8-day, 16-day, monthly) were used to calculate temperature vegetation dryness index (TVDI), to analyze relationships between TVDI and soil moisture, and to determine the preferential temporal scale to monitor soil moisture. [Results] The vegetation index was linearly related to max and min land surface temperature at three temporal scales. At different temporal scales, there was a significant relationship between TVDI and soil moisture. However, 8-day TVDI demonstrated a closer relationship with soil moisture and with increasing temporal scales, the relationship between them became weak. In Shandong Province, the drought distribution was in accordance with precipitation. [Conclusion] It is suggested that TVDI can indicate soil moisture and should be used preferentially at the short temporal scale, and precipitation has important influence on wheat drought in Shandong Province. [ABSTRACT FROM AUTHOR]

Copyright of Remote Sensing for Natural Resources is the property of Remote Sensing for Natural Resources Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Geodesy & Geodynamics (1671-5942) is the property of Editorial Board Journal of Geodesy & Geodynamics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Exploring the driving mechanism of urban Land Surface Temperature (LST) is of great significance to alleviating the Urban Heat Island (UHI) effect and building a healthy and comfortable urban living environment. Four Landsat remote sensing images representing the four seasons in 2020 and 2021 were used in the paper to invert the LST of Beijing, then the seasonal changes of the UHI effect were discussed. Then geographical detector model was used to analyze the interpretation degree of LST by four types of driving factors including land cover, topography, social economy, and meteorology. Furthermore, the Geographically Weighted Regression (GWR) model was used to analyze the spatial heterogeneity of LST’s driving factors. The results showed that: (1) the UHI effect in Beijing varied obviously with seasons. The heat island areas were concentrated in urban areas in summer and autumn, and most of the heat island areas were scattered in the whole study area in spring and winter. (2) The driving factors for LST varied with seasons. The main driving factors of LST in spring were ranked from high to low as normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI), digital elevation model (DEM)；the main driving factors of LST in summer were ranked from high to low as NDVI, DEM, MNDWI: the main driving factors of LST in autumn were ranked from high to low as DEM, nighttime light, air temperature: the main driving factors of LST in winter were ranked from high to low as air temperature, nighttime light, MNDWI (3) Took exploring the spatial heterogeneity of the driving factors for LST in summer as an example, the results showed that the relationships between NDVI, MNDWI, the proportion of impervious surface and LST had little spatial variation, the relationships between road density, nighttime light intensity, DEM, wind speed, humidity, air temperature and LST had great spatial variation. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Applied Ecology / Yingyong Shengtai Xuebao is the property of Chinese Journal of Applied Ecology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Progress in Earthquake Sciences is the property of China Earthquake Administration, Institute of Geophysics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of South Architecture / Nanfang Jianzhu is the property of South Architecture Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract: In order to explore the respiration of winter wheat farmland ecosystem in the North China Plain, carbon flux observation data from Zhengzhou agricultural meteorological experimental station in Henan Province from 2013 to 2017 were used to analyze the respiration of winter wheat in Zhengzhou area during the growing season. Based on MODIS data, the environmental factors of this ecosystem were discussed and the respiration of winter wheat was simulated. The results showed that there was a good correlation between observed winter wheat respiration and Enhanced Vegetation Index(EVI), Land Surface Moisture Index(LSWI), Land Surface Temperature (LST). The respiration model based on MODIS data could be used for respiration simulation of winter wheat in Zhengzhou area. The simulated value of respiration was greater during the day than at night. In the research stage(day of year was 73 to 169) of winter wheat from 2013 to 2017, the total accumulation of simulated respiration was 472.18, 477.02, 490.43, 482.90 and 487.37 g·m–2, respectively. The study shows that it is rational and feasible to simulate the respiration of winter wheat farmland ecosystem in Zhengzhou based on MODIS data, which can provide data basis and technical theoretical support for the study of regional carbon budget assessment in China. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Applied Ecology / Yingyong Shengtai Xuebao is the property of Chinese Journal of Applied Ecology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Arid Zone Research / Ganhanqu Yanjiu is the property of Arid Zone Research Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Arid Zone Research / Ganhanqu Yanjiu is the property of Arid Zone Research Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Arid Land Geography is the property of Chinese Academy of Sciences, Xinjiang Institute of Ecology & Geography and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Arid Land Geography is the property of Chinese Academy of Sciences, Xinjiang Institute of Ecology & Geography and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to improve the simulation performance of NCAR CLM4.5 （The Community Land Model Version 4.5） for land surface temperature （LST） in northern China， the ability of three commonly used soil thermal conductivity （STC） models were tested in this paper.The results show that these schemes can simulate the temporal and spatial characteristics of land surface temperature in northern China well.The LST in northern China has risen in recent 30 years， especially since 2003.The trend can be simulated by all these three schemes and shows a significant correlation with the observed LST， but the simulated warming amplitude is small than that of observation.The overall evaluation based on the taylor diagram and precipitation simulation shows that LR scheme has the best simulation effect.Therefore， a new scheme of STC that more suitable for climate simulation in northern China is added to CLM4.5， which promotes the development of the land surface model.

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objectives: Near surface air temperature (NSAT) is a key parameter in the land-atmosphere interaction process. Sparse NSAT observations from in-situ stations usually cannot fully describe the spatial distribution of NSAT, so estimating NSAT by land surface temperature (LST) and auxiliary variables has become an effective approach to obtain the spatial distribution of NSAT. Although there are some multisource LST products published, the LST from moderate-resolution imaging spectroradiometer （MODIS）, Landsat, and global land data assimilation system (GLDAS), the applicability of each LST product in NSAT estimation still needs further investigation. Methods: Taken the Yellow River Basin Region as the study area, summer NSAT from 2003 to 2019 was estimated based on the Google Earth Engine platform with random forest algorithm in this study, and the mean, maximum and minimum NSAT was estimated in two scales (30 m and 1 000 m) using three LST data sources (Landsat, MODIS and GLDAS). The observed LST from in-situ stations over the Yellow River Basin Region were compared with the estimated NSAT by the ten-cross validation method to evaluate the accuracy of different LST sources when estimating NSAT. Results and Conclusions: The results indicate that the differences of accuracy of the three LST sources are small in terms of mean NSAT. In terms of the maximum and minimum NSAT, the GLDAS LST shows the significant higher accuracy than MODIS and Landsat LST. The root mean squared error of estimated mean NSAT are smaller than maximum and minimum NSAT estimation when using the same LST source. For the spatial distribution of accuracy, the stations with higher error mainly located in the southern or western of the study region. The temporal resolution of LST source is significantly important in NSAT estimation. The GLDAS LST shows the highest accuracy in this study especially for extreme NSAT estimation. Besides, the mean NSAT estimation has higher accuracy than that of maximum or minimum NSAT for each LST source. [ABSTRACT FROM AUTHOR]

Copyright of Arid Zone Research / Ganhanqu Yanjiu is the property of Arid Zone Research Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Mongolian plateau was divided into "vegetation area" and "non-vegetation area" using the threshold value of the multi-year monthly mean NDVI value above 0.1. Based on an examination of the time lagged response of vegetation to precipitation in the "vegetation area" and the association between various data values of land surface temperature and precipitation in the "non-vegetation area", a geographically weighted regression (GWR) model of TRMM 3B43 data with the elevation, slope, slope direction data, and normalized vegetation index (NDVI)/land surface temperature (LST) data was constructed to obtain monthly precipitation downscaling simulations at 1km spatial resolution from May to October 2006-2015 in the different areas. Utilizing information from 141 local meteorological stations, the accuracy of downscaled simulation data was validated. The results showed that, (1) there is a time lag in the response of vegetation to precipitation in the "vegetated area" of the Mongolian plateau, which is about one month. The most significant correlation between daytime and nighttime land surface temperature difference (LST_D_N) values and precipitation is found in the "non-vegetated area" in most months. (2) The descending-scale simulations agree with the meteorological station data, with a monthly-scale correlation coefficient of 0.83 and correlation coefficients ranging from 0.42 to 0.98 for each station. (3) On the growing season and monthly mean scales, the downscaled simulation data are highly accurate, with September and October data being more accurate than TRMM 3B43 data. The overall accuracy of the downscaled simulation data is higher, and together with the filling of areas not covered by the original data above 50°N and the increase in spatial resolution. It can provide essential data support for research on water cycle changes, agricultural and livestock production, and drought monitoring in the region. [ABSTRACT FROM AUTHOR]

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Given the land surface types and atmospheric features of the Heihe River basin, this study calculated the surface emissivity of the study area using the ASTER Global Emissivity Database and the vegetation cover method (VCM) and estimated the atmospheric water vapor content using the improved multilayer feed - forward neural network ( MFNN) . Moreover, by establishing the coefficient lookup table of input parameter groups, this study developed an ASTER data - based split - window algorithm for the remote sensing inversion of land surface temperature. To validate the applicability and accuracy of the split - window algorithm, this study elevated the algorithm using the measured site data on the land surface temperature of the Heihe River basin in 2019 and MODIS instruments. Compared with the site data, the results of the split - window algorithm had root mean square errors of 1.81 ~ 3.01 K. In the cross - validation using the MODIS instruments, the split - window algorithm had relatively small errors and deviations, with root mean square errors of 1. 11 ~ 1. 75 IK Overall, the accuracy of the land surface temperature obtained from the inversion using the split - window algorithm can meet the needs of meteorological and climatological studies. Moreover, the development philosophy of the split - window algorithm can be used as a reference for similar thermal infrared sensors. [ABSTRACT FROM AUTHOR]

Extensive use of wind energy resources in China not only brings huge economic benefits， but also reduces environmental pollution caused by various fossil fuels.However， some studies have shown that the rotation of the rotor blades will increase surface roughness and then enhance turbulence in the rotor wakes by changing the stability of the atmospheric boundary layer （ABL）.If these changes are spatially large enough， they may affect the local and even regional weather and climate.With increasing installed capacity of wind farms， a correct understanding of the possible impacts of wind power on climate is needed， especially its impact on temperature.It is of great significance for the scientific use of wind energy resources and ensuring the sustainable development of energy in China.Satellite remote sensing technology can conduct large-scale， long-term， and regular observations of the land surface， and data acquisition is relatively easy.The MODIS satellite data has been used in studies of the impact of wind farms on the land surface temperature.Based on MODIS satellite data from 2003 to 2019， this paper explores the impact of two large wind farms in Shangyi and Zhangbei in northern Zhangjiakou on land surface temperature.Combined with wind direction， vegetation index， surface albedo， and land use data， the results show that： （1）During spring， autumn， and winter， the land surface temperature downwind of the wind farm is higher than that upwind during nighttime， the opposite is true during the day.The land surface temperature in the downwind area of the wind farm is significantly affected by the wind turbine wake.（2）Compared with the surrounding areas not affected by wind farms， the land surface temperature within wind farms increased significantly in spring and summer， especially during spring night with an increaing trend of 0.379 ℃·（10a）-1， followed by summer night and autumn night with increaing trends of 0.206 ℃·（10a）-1 and 0.211 ℃·（10a）-1， respectively.The warming trends` spatial pattern couples well with the geographic distribution of wind turbines.（3）Although wind farms in Shangyi and Zhangbei have impacts on LST， compared with other studies the increasing trends caused by wind farms in this study area is weak and much smaller than the interannual LST increasing trend in the Bashang area with increaing trends of 0.817 ℃·（10a）-1， the land surface temperature increases caused by the operation of wind turbines is negligible compared with which caused by fossil fuels.Changes in land surface temperature within wind farms are not only affected by the operation of wind farms but also a combination of various influencing factors， such as climate change， vegetation， and land-use changes.

Since powdery mildew (Blumeria graminis f. sp. tritici) mainly infects the foliar of wheat, satellite remote sensing technology can be used to monitor and assess it on a large scale. In this study, multisource and multitemporal satellite images were used to monitor the disease and improve the classification accuracy. Specifically, four Landsat-8 thermal infrared sensor (TIRS) and twenty MODerate-resolution imaging spectroradiometer (MODIS) temperature product (MOD11A1) were used to retrieve the land surface temperature (LST), and four Chinese Gaofen-1 (GF-1) wide field of view (WFV) images was used to identify the wheat-growing areas and calculate the vegetation indices (VIs). ReliefF algorithm was first used to optimally select the vegetation index (VIs) sensitive to wheat powdery mildew, spatial-temporal fusion between Landsat-8 LST and MOD11A1 data was performed using the spatial and temporal adaptive reflectance fusion model (STARFM). The Z-score standardization method was then used to unify the VIs and LST data. Four monitoring models were then constructed through a single Landsat-8 LST, multitemporal Landsat-8 LSTs (SLST), cumulative MODIS LST (MLST) and the combination of cumulative Landsat-8 and MODIS LST (SMLST) using the Support Vector Machine (SVM) classifier, that were LST-SVM, SLST-SVM, MLST-SVM and SMLST-SVM. Four assessment indicators including user accuracy, producer accuracy, overall accuracy and Kappa coefficient were used to compare the four models. The results showed that, the proposed SMLST-SVM obtained the best identification accuracies. The overall accuracy and Kappa coefficient of the SMLST-SVM model had the highest values of 81.2% and 0.67, respectively, while they were respectively 76.8% and 0.59 for the SLST-SVM model. Consequently, multisource and multitemporal LSTs can considerably improve the differentiation accuracies of wheat powdery mildew.

Objectives: Traditional remote sensing-based air temperature estimation method usually used the global models, which ignored the effects of spatiotemporal heterogeneity, especially for the researches in large regional areas. Methods: Taking the Yangtze River Economic Zone as a typical research area, this paper introduces the geographically and temporally neural networks for high-precision temperature estimation. The influence of spatiotemporal heterogeneity was considered by establishing the local models in the generalized regression neural network. Remote sensing data, assimilation data and station data were fused to obtain the spatially continuous near-surface temperature. The model performance was evaluated by the site-based ten-fold cross-validation method. Results: The results show that the geographically and temporally weighted neural network had effectively improved the estimation accuracy with the root mean square error(RMSE) =1.899 ℃, mean absolute error (MAE) =1.310 ℃ and correlation coefficient (R) =0.976. Compared with the multiple linear regression method and the traditional global neural network, the MAE value decreased by 1.112 ℃ and 0.378 ℃, respectively. Conclusions: The temperature mapping results indicated that the model used in this paper can well reflect the spatial distribution differences, which means that this study is possible to provide a new way for temperature estimation with high precision. [ABSTRACT FROM AUTHOR]

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Copyright of Nonferrous Metals (Mining Section) is the property of Beijing Research Institute of Mining & Metallurgy Technology Group and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Ecology & Rural Environment is the property of Journal of Ecology & Rural Environment Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)