Development and testing of a remote sensing-based model for estimating groundwater levels in aeolian desert areas of China

2011 ◽  
Vol 91 (1) ◽  
pp. 29-37 ◽  
Author(s):  
Aidi Huo ◽  
Xunhong Chen ◽  
Huike Li ◽  
Ming Hou ◽  
Xiaojing Hou
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Groundwater Level ◽  
Remote Sensing Data ◽  
Water Levels ◽  
Desert Area ◽  
Groundwater Levels ◽  
Sensing Data ◽  
Desert Areas ◽  
Regional Groundwater

Huo, A., Chen, X., Li, H., Hou, M. and Hou, X. 2011. Development and testing of a remote sensing-based model for estimating groundwater levels in aeolian desert areas of China. Can. J. Soil Sci. 91: 29–37. Regional groundwater level is an important data set for understanding the relationships between groundwater resources and regional ecological environments. The decline in water table levels leads to vegetation degradation and thus affects the ecological environment. Such a negative effect is especially apparent in the desertification areas. In this study, a remote-sensing based method was proposed to predict the distribution of the regional groundwater level in an aeolian desert area in northern China. The study used the Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data and field investigations. Based on field investigation of groundwater level, soil moisture, and other supporting information in the aeolian desert area, as well as the soil moisture distribution derived from the MODIS images, empirical equations describing the relationship between the soil moisture and groundwater level were obtained. The groundwater levels derived using the MODIS image data were verified by groundwater levels measured from 58 wells. The results show that the correlation coefficient between the measured groundwater levels and the remote sensing-based estimated water levels was 0.868, indicating that the error is small and the predictions closely reflect the real water levels. This model can be used to predict groundwater levels in aeolian desert areas based on remote sensing data sets.

scholarly journals Land-Cover Dependent Relationships between Fire and Soil Moisture

Fire ◽  
2019 ◽  
Vol 2 (4) ◽  
pp. 55 ◽  
Author(s):  
Alexander J. Schaefer ◽  
Brian I. Magi
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Land Cover ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Fire Activity ◽  
Steep Decline ◽  
Human Use ◽  
Moisture Conditions ◽  
In Fire

For this study, we characterized the dependence of fire counts (FCs) on soil moisture (SM) at global and sub-global scales using 15 years of remote sensing data. We argue that this mathematical relationship serves as an effective way to predict fire because it is a proxy for the semi-quantitative fire–productivity relationship that describes the tradeoff between fuel availability and climate as constraints on fire activity. We partitioned the globe into land-use and land-cover (LULC) categories of forest, grass, cropland, and pasture to investigate how the fire–soil moisture (fire–SM) behavior varies as a function of LULC. We also partitioned the globe into four broadly defined biomes (Boreal, Grassland-Savanna, Temperate, and Tropical) to study the dependence of fire–SM behavior on LULC across those biomes. The forest and grass LULC fire–SM curves are qualitatively similar to the fire–productivity relationship with a peak in fire activity at intermediate SM, a steep decline in fire activity at low SM (productivity constraint), and gradual decline as SM increases (climate constraint), but our analysis highlights how forests and grasses differ across biomes as well. Pasture and cropland LULC are a distinctly human use of the landscape, and fires detected on those LULC types include intentional fires. Cropland fire–SM curves are similar to those for grass LULC, but pasture fires are evident at higher SM values than other LULC. This suggests a departure from the expected climate constraint when burning is happening at non-optimal flammability conditions. Using over a decade of remote sensing data, our results show that quantifying fires relative to a single physical climate variable (soil moisture) is possible on both cultivated and uncultivated landscapes. Linking fire to observable soil moisture conditions for different land-cover types has important applications in fire management and fire modeling.

Soil moisture and erosion degree estimation from multichannel microwave remote sensing data

1995 ◽  
Author(s):  
Gennady P. Kulemin ◽  
Andrei A. Kurekin ◽  
Vladimir V. Lukin ◽  
Alexander A. Zelensky
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
Sensing Data

Spatiotemporal monitoring of surface soil moisture using optical remote sensing data: a case study in a semi-arid area

2018 ◽  
Vol 65 (3) ◽  
pp. 481-499 ◽  
Author(s):  
Rida Khellouk ◽  
Ahmed Barakat ◽  
Abdelghani Boudhar ◽  
Rachid Hadria ◽  
Hayat Lionboui ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Surface Soil ◽  
Remote Sensing Data ◽  
Arid Area ◽  
Optical Remote Sensing ◽  
Surface Soil Moisture ◽  
Sensing Data ◽  
Semi Arid

scholarly journals A Soil Moisture Spatial and Temporal Resolution Improving Algorithm Based on Multi-Source Remote Sensing Data and GRNN Model

2020 ◽  
Vol 12 (3) ◽  
pp. 455 ◽  
Author(s):  
Yaokui Cui ◽  
Xi Chen ◽  
Wentao Xiong ◽  
Lian He ◽  
Feng Lv ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Temporal Resolution ◽  
Land Surface ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
The Tibetan Plateau ◽  
Sensing Data ◽  
Spatio Temporal ◽  
Input Variables

Surface soil moisture (SM) plays an essential role in the water and energy balance between the land surface and the atmosphere. Low spatio-temporal resolution, about 25–40 km and 2–3 days, of the commonly used global microwave SM products limits their application at regional scales. In this study, we developed an algorithm to improve the SM spatio-temporal resolution using multi-source remote sensing data and a machine-learning model named the General Regression Neural Network (GRNN). First, six high spatial resolution input variables, including Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), albedo, Digital Elevation Model (DEM), Longitude (Lon) and Latitude (Lat), were selected and gap-filled to obtain high spatio-temporal resolution inputs. Then, the GRNN was trained at a low spatio-temporal resolution to obtain the relationship between SM and input variables. Finally, the trained GRNN was driven by the high spatio-temporal resolution input variables to obtain high spatio-temporal resolution SM. We used the Fengyun-3B (FY-3B) SM over the Tibetan Plateau (TP) to test the algorithm. The results show that the algorithm could successfully improve the spatio-temporal resolution of FY-3B SM from 0.25° and 2–3 days to 0.05° and 1-day over the TP. The improved SM is consistent with the original product in terms of both spatial distribution and temporal variation. The high spatio-temporal resolution SM allows a better understanding of the diurnal and seasonal variations of SM at the regional scale, consequently enhancing ecological and hydrological applications, especially under climate change.

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