Spatial downscaling of coarse passive radiometer soil moisture using radar, vegetation index and surface temperature

2013 ◽  
Author(s):  
Venkat Lakshmi ◽  
Bin Fang ◽  
Ujjwal Narayan
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
Vegetation Index ◽  
Spatial Downscaling

Characterizing waste disposal sites by using multi-spectral satellite imagery

2021 ◽  
Author(s):  
Gaetana Ganci ◽  
Annalisa Cappello ◽  
Giuseppe Bilotta ◽  
Giuseppe Pollicino ◽  
Luigi Lodato
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
Waste Disposal ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
The State ◽  
Dump Site ◽  
Temperature State ◽  
Surrounding Areas

<p>The application of remote sensing for monitoring, detecting and analysing the spatial and extents and temporal changes of waste dumping sites and landfills could become a cost-effective and powerful solution. Multi-spectral satellite images, especially in the thermal infrared, can be exploited to characterize the state of activity of a landfill.  Indeed, waste disposal sites, during the period of activity, can show differences in surface temperature (LST, Land Surface Temperature), state of vegetation (estimated through NDVI, Normalized Difference Vegetation Index) or soil moisture (estimated through NDWI, Normalized Difference Water Index) compared to neighboring areas. Landfills with organic waste typically show higher temperatures than surrounding areas due to exothermic decomposition activities. In fact, the biogas, in the absence or in case of inefficiency of the conveying plants, rises through the layers of organic matter and earth (landfill body) until it reaches the surface at a temperature of over 40 ° C. Moreover, in some cases, leachate contamination of the aquifers can be identified by analyzing the soil moisture, through the estimate of the NDWI, and the state of suffering of the vegetation surrounding the site, through the estimate of the NDVI. This latter can also be an indicator of soil contamination due to the presence of toxic and potentially dangerous waste when buried or present nearby. To take into account these facts, we combine the LST, NDVI and NDWI indices of the dump site and surrounding areas in order to characterize waste disposal sites. Preliminary results show how this approach can bring out the area and level of activity of known landfill sites. This could prove particularly useful for the definition of intervention priorities in landfill remediation works.</p>

scholarly journals NEURAL NETWORK METHOD FOR DROUGHT MODELING USING SATELLITE DATA

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 749-753
Author(s):  
R. Mokhtari ◽  
M. Akhoondzadeh
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Surface Temperature ◽  
Snow Cover ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Machine Learning Algorithms ◽  
Factors Affecting ◽  
Average Accuracy

Abstract. Drought is one of the natural crises in each region. Drought has a direct relationship with vegetation. Various factors affect vegetation. The relationship between these factors and vegetation can be expressed using methods of machine learning algorithms. Nowadays, using remote sensing images can be used to measure the factors affecting vegetation and investigate this phenomenon with high precision. In this research, vegetation and various factors affecting this factor, which can be measured using satellite imagery, are selected. The factors include land surface temperature (LST), evapotranspiration (ET), snow cover, rainfall, soil moisture that which are derived from the active and passive sensors of satellite sensors as the products of land surface temperature (LST), snow cover and vegetation, using images of products of the MODIS sensor and rainfall using the images of the TRMM satellite and soil moisture using the images of the SMOS satellite during a period from June 2010 to the end of 2018 for the central region of Iran has received and after that, primary processing was performed on these images. The vegetation index (NDVI) is modeled using artificial neural network algorithm for monthly periods. method have been able to achieve model with desirable accuracy. The average accuracy was RMSE = 0.048 and R2 = 0.867.

scholarly journals Development of Integrated Crop Drought Index by Combining Rainfall, Land Surface Temperature, Evapotranspiration, Soil Moisture, and Vegetation Index for Agricultural Drought Monitoring

2021 ◽  
Vol 13 (9) ◽  
pp. 1778
Author(s):  
Soo-Jin Lee ◽  
Nari Kim ◽  
Yangwon Lee
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
Crop Yield ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Drought Index ◽  
Vegetation Index ◽  
The State ◽  
Agricultural Drought ◽  
Drought Monitoring

Various drought indices have been used for agricultural drought monitoring, such as Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI), Soil Water Deficit Index (SWDI), Normalized Difference Vegetation Index (NDVI), Vegetation Health Index (VHI), Vegetation Drought Response Index (VegDRI), and Scaled Drought Condition Index (SDCI). They incorporate such factors as rainfall, land surface temperature (LST), potential evapotranspiration (PET), soil moisture content (SM), and vegetation index to express the meteorological and agricultural aspects of drought. However, these five factors should be combined more comprehensively and reasonably to explain better the dryness/wetness of land surface and the association with crop yield. This study aims to develop the Integrated Crop Drought Index (ICDI) by combining the weather factors (rainfall and LST), hydrological factors (PET and SM), and a vegetation factor (enhanced vegetation index (EVI)) to better express the wet/dry state of land surface and healthy/unhealthy state of vegetation together. The study area was the State of Illinois, a key region of the U.S. Corn Belt, and the quantification and analysis of the droughts were conducted on a county scale for 2004–2019. The performance of the ICDI was evaluated through the comparisons with SDCI and VegDRI, which are the representative drought index in terms of the composite of the dryness and vegetation elements. The ICDI properly expressed both the dry and wet trend of the land surface and described the state of the agricultural drought accompanied by yield damage. The ICDI had higher positive correlations with the corn yields than SDCI and VegDRI during the crucial growth period from June to August for 2004–2019, which means that the ICDI could reflect the agricultural drought well in terms of the dryness/wetness of land surface and the association with crop yield. Future work should examine the other factors for ICDI, such as locality, crop type, and the anthropogenic impacts, on drought. It is expected that the ICDI can be a viable option for agricultural drought monitoring and yield management.

scholarly journals Shading decreases and delays NDVI and flowering of prostrate arctic shrubs

2021 ◽  
Author(s):  
Jeremy May ◽  
Steve Oberbauer ◽  
Steven L. Unger ◽  
Matthew J. Simon ◽  
Katlyn R. Betway ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Surface Temperature ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Soil Moisture Content ◽  
Trophic Levels ◽  
Food Sources ◽  
The Arctic ◽  
The Impact

Increases in shrub growth and canopy cover are well documented community responses to climate warming in the Arctic. An important consequence of larger deciduous shrubs is shading of prostrate plant species, many of which are important sources of nectar and berries. Here we present the impact of a shading experiment on two prostrate shrubs, Vaccinium vitis-idaea and Arctous alpina, in northern Alaska over two growing seasons. We implemented three levels of shading (no shade, 40% shade, and 80% shade) in dry heath and moist acidic tundra. Plots were monitored for soil moisture content, surface temperature, normalized difference vegetation index (NDVI), and flowering. Shading was shown to, on average, lower surface temperature (0.7 to 5.3 ˚C) and increase soil moisture content (0.5 to 5.6%) in both communities. Both species- and plot-level NDVI values were delayed in timing of peak values (7 to 13 days) and decreased at the highest shading. Flower abundance of both species was lower in shaded plots and peak flowering was delayed (3 to 8 days) compared to controls. Changes in timing may result in phenological mismatches and can impact other trophic levels in the Arctic as both the flowers and resulting berries are important food sources for animals.

Sign in / Sign up

Export Citation Format

Share Document