scholarly journals Integrating Remote Sensing Data with WRF for Improved Simulations of Oasis Effects on Local Weather Processes over an Arid Region in Northwestern China

2012 ◽  
Vol 13 (2) ◽  
pp. 573-587 ◽  
Author(s):  
Xiaohang Wen ◽  
Shihua Lu ◽  
Jiming Jin
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Relative Humidity ◽  
Soil Temperature ◽  
Air Temperature ◽  
Remote Sensing Data ◽  
Heat Fluxes ◽  
Near Surface ◽  
Sensing Data ◽  
Soil Temperature And Moisture

Abstract Land use/cover types derived by satellite remote sensing data from the Earth Observing System Moderate Resolution Imaging Spectroradiometer (MODIS) were used to replace the U.S. Geological Survey (USGS) data in the Weather Research and Forecasting Model (WRF). Simulations in this study were further improved by modifying the initial fields of WRF with soil temperature and moisture observations, because these two variables are important to producing “cold–wet island” effects. A series of WRF simulations were performed to describe microclimate characteristics and the local thermal circulation generated by the inhomogeneous surface over the Jinta oasis, which is located in Gansu—a northwestern province of China. Comparison between simulations and observations showed that the WRF results produced with observed soil temperature and moisture initializations agreed well with near-surface measurements of air temperature, relative humidity, and wind direction. Moreover, low temperatures over the oasis were found to coexist with high temperatures over the bare land, further leading to developments of local atmospheric circulation. The simulated winds over the oasis showed airmass divergence over the surface layer, triggering local circulation in the upper level. The integration of the MODIS land use/cover data with WRF and the initialization of WRF’s soil temperature and moisture with in situ observations improved the simulations in air temperature, relative humidity, and heat fluxes. These improvements enabled the WRF to reproduce the observed “cold and wet island” effects of the oasis.

Retrieval of Hourly Records of Surface Hydrometeorological Variables Using Satellite Remote Sensing Data

2015 ◽  
Vol 16 (1) ◽  
pp. 147-157 ◽  
Author(s):  
Sanaz Moghim ◽  
Andrew Jay Bowen ◽  
Sepideh Sarachi ◽  
Jingfeng Wang
Keyword(s):  
Remote Sensing ◽  
Air Temperature ◽  
Sensible Heat Flux ◽  
Surface Air Temperature ◽  
Remote Sensing Data ◽  
Heat Fluxes ◽  
Production Model ◽  
Integral Model ◽  
Near Surface ◽  
Surface Heat Fluxes

Abstract A new algorithm is formulated for retrieving hourly time series of surface hydrometeorological variables including net radiation, sensible heat flux, and near-surface air temperature aided by hourly visible images from the Geostationary Operational Environmental Satellite (GOES) and in situ observations of mean daily air temperature. The algorithm is based on two unconventional, recently developed methods: the maximum entropy production model of surface heat fluxes and the half-order derivative–integral model that has been tested previously. The close agreement between the retrieved hourly variables using remotely sensed input and the corresponding field observations indicates that this algorithm is an effective tool in remote sensing of the earth system.

scholarly journals Monitoring Hybrid Rice Phenology at Initial Heading Stage Based on Low-Altitude Remote Sensing Data

2020 ◽  
Vol 13 (1) ◽  
pp. 86
Author(s):  
Yi Ma ◽  
Qi Jiang ◽  
Xianting Wu ◽  
Renshan Zhu ◽  
Yan Gong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Air Temperature ◽  
Hybrid Rice ◽  
Remote Sensing Data ◽  
Rice Cultivars ◽  
Sensing Data ◽  
Gauss Function ◽  
Low Altitude ◽  
Rice Phenology ◽  
Heading Stage

Accurate monitoring of hybrid rice phenology (RP) is crucial for breeding rice cultivars and controlling fertilizing amount. The aim of this study is to monitor the exact date of hybrid rice initial heading stage (IHSDAS) based on low-altitude remote sensing data and analyze the influence factors of RP. In this study, six field experiments were conducted in Ezhou city and Lingshui city from 2016 to 2019, which involved different rice cultivars and nitrogen rates. Three low-altitude remote sensing platforms were used to collect rice canopy reflectance. Firstly, we compared the performance of normalized difference vegetation index (NDVI) and red edge chlorophyll index (CIred edge) for monitoring RP. Secondly, double logistic function (DLF), asymmetric gauss function (AGF), and symmetric gauss function (SGF) were used to fit time-series CIred edge for acquiring phenological curves (PC), the feature: maximum curvature (MC) of PC was extracted to monitor IHSDAS. Finally, we analyzed the influence of rice cultivars, N rates, and air temperature on RP. The results indicated that CIred edge was more appropriate than NDVI for monitoring RP without saturation problem. Compared with DLF and AGF, SGF could fit CIred edge without over fitting problem. MC of SGF_CIred edge from all three platforms showed good performance in monitoring IHSDAS with good robustness, R2 varied between 0.82 and 0.95, RMSE ranged from 2.31 to 3.81. In addition, the results demonstrated that high air temperature might cause a decrease of IHSDAS, and the growth process of rice was delayed when more nitrogen fertilizer was applied before IHSDAS. This study illustrated that low-altitude remote sensing technology could be used for monitoring field-scale hybrid rice IHSDAS accurately.

Estimation of instantaneous air temperature using remote sensing data

2017 ◽  
Vol 39 (1) ◽  
pp. 258-275 ◽  
Author(s):  
Foroogh Golkar ◽  
Ali Akbar Sabziparvar ◽  
Reza Khanbilvardi ◽  
Mohammad Jafar Nazemosadat ◽  
Shahrokh Zand- Parsa ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Air Temperature ◽  
Remote Sensing Data ◽  
Sensing Data

scholarly journals Spatial evapotranspiration, rainfall and land use data in water accounting – Part 1: Review of the accuracy of the remote sensing data

2015 ◽  
Vol 19 (1) ◽  
pp. 507-532 ◽  
Author(s):  
P. Karimi ◽  
W. G. M. Bastiaanssen
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Water Resource Management ◽  
Hydrological Modeling ◽  
Remote Sensing Data ◽  
Actual Evapotranspiration ◽  
Primary Concern ◽  
Suitable Alternative ◽  
Sensing Data ◽  
Water Accounting

Abstract. The scarcity of water encourages scientists to develop new analytical tools to enhance water resource management. Water accounting and distributed hydrological models are examples of such tools. Water accounting needs accurate input data for adequate descriptions of water distribution and water depletion in river basins. Ground-based observatories are decreasing, and not generally accessible. Remote sensing data is a suitable alternative to measure the required input variables. This paper reviews the reliability of remote sensing algorithms to accurately determine the spatial distribution of actual evapotranspiration, rainfall and land use. For our validation we used only those papers that covered study periods of seasonal to annual cycles because the accumulated water balance is the primary concern. Review papers covering shorter periods only (days, weeks) were not included in our review. Our review shows that by using remote sensing, the absolute values of evapotranspiration can be estimated with an overall accuracy of 95% (SD 5%) and rainfall with an overall absolute accuracy of 82% (SD 15%). Land use can be identified with an overall accuracy of 85% (SD 7%). Hence, more scientific work is needed to improve the spatial mapping of rainfall and land use using multiple space-borne sensors. While not always perfect at all spatial and temporal scales, seasonally accumulated actual evapotranspiration maps can be used with confidence in water accounting and hydrological modeling.

scholarly journals Research on Land Use Changes in Panjin City Basing on Remote Sensing Data

2018 ◽  
Vol 153 ◽  
pp. 062050
Author(s):  
Hua Ding ◽  
Ru Ren Li ◽  
Li Shuang Sun ◽  
Xin Wang ◽  
Yu Mei Liu
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Use Changes ◽  
Remote Sensing Data ◽  
Sensing Data

scholarly journals Efficient land use based on remote sensing data

2021 ◽  
Vol 3 ◽  
pp. 180-185
Author(s):  
Y. M. Kenzhegaliyev ◽  
◽  
◽  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Remote Sensing Data ◽  
Crop Productivity ◽  
The State ◽  
Agricultural Crops ◽  
Earth Remote Sensing ◽  
Spectral Reflectivity ◽  
Sensing Data ◽  
Current Location

The goal -is to explore ways of using Earth remote sensing data for efficient land use. Methods - detailed information on current location of certain types of agricultural crops in the study areas has been summarized, which opens up opportunities for the effective use of cultivated areas. It was revealed that the basis of the principle of the method under consideration is the relationship between the state and structure of vegetation types with its reflective ability. It has been determined that information on the spectral reflective property of the vegetation cover in the future can help replace more laborious methods of laboratory analysis. For classification of farmland, satellite images of medium spatial resolution with a combination of channels in natural colors were selected. Results - a method for identifying agricultural plants by classification according to the maximum likelihood algorithm was considered. The commonly used complexes of geoinformation software products with modules for special image processing allow displaying indicators in the form of raster images. It is shown that the use of Earth remote sensing data is the most relevant solution in the field of crop recognition and makes it possible to simplify the implementation of such types of work as the analysis of the intensity of land use, the assessment of the degree of pollution with weeds and determination of crop productivity. Conclusions - the research results given in the article indicate that timely information on the current location of certain types of agricultural crops in the studied territories significantly simplifies the implementation of the tasks and increases the resource potential of agricultural lands. In turn, the timing of the survey and the state of environment affect the spectral reflectivity of vegetation.

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