Advanced ground truth for remote sensing of soil moisture

1995 ◽  
Author(s):  
Alexander Brandelik ◽  
Christof Huebner
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Ground Truth

Airborne remote sensing for groundwater studies in Prairie environment

1969 ◽  
Vol 6 (4) ◽  
pp. 737-741 ◽  
Author(s):  
M. E. Chase
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Surface Water ◽  
Ground Truth ◽  
Infrared Photography ◽  
Black And White ◽  
Remote Sensors ◽  
Infrared Scanning ◽  
Southern Alberta ◽  
Lake Deposits

A deficiency of groundwater in an area of prairieland in southern Alberta prompted a survey by airborne remote sensors. Panchromatic and black and white true infrared photography coverages were obtained and studied, but were found to be of limited value. An infrared scanning survey, using the 2.5–5.6 μ band was flown at the same time and found to be more informative. Conditions of ground truth were not ideal, making the results less conclusive than desired. Till covers most of the area, with lake deposits in the eastern section. Soil moisture changes were registered on the imagery, but the depths of overburden to which these changes were recorded on the surface are unknown, due to the condition variations between the time of the survey and the drilling. Vegetation, surface water, soil moisture, and saline sloughs were found to have the strongest thermal signatures. Problems encountered in the survey are discussed and recommendations to eliminate them are given.

scholarly journals GNSS-R Soil Moisture Retrieval Based on a XGboost Machine Learning Aided Method: Performance and Validation

2019 ◽  
Vol 11 (14) ◽  
pp. 1655 ◽  
Author(s):  
Yan Jia ◽  
Shuanggen Jin ◽  
Patrizia Savi ◽  
Yun Gao ◽  
Jing Tang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Soil Moisture ◽  
Ground Truth ◽  
Satellite System ◽  
Soil Conditions ◽  
Retrieval Process ◽  
Method Performance ◽  
Ensemble Machine Learning ◽  
Input Variables

Global navigation satellite system (GNSS)-reflectometry is a type of remote sensing technology and can be applied to soil moisture retrieval. Until now, various GNSS-R soil moisture retrieval methods have been reported. However, there still exist some problems due to the complexity of modeling and retrieval process, as well as the extreme uncertainty of the experimental environment and equipment. To investigate the behavior of bistatic GNSS-R soil moisture retrieval process, two ground-truth measurements with different soil conditions were carried out and the performance of the input variables was analyzed from the mathematical statistical aspect. Moreover, the feature of XGBoost method was utilized as well. As a recently developed ensemble machine learning method, the XGBoost method just emerged for the classification of remote sensing and geographic data, to investigate the characterization of the input variables in the GNSS-R soil moisture retrieval. It showed a good correlation with the statistical analysis of ground-truth measurements. The variable contributions for the input data can also be seen and evaluated. The study of the paper provides some experimental insights into the behavior of the GNSS-R soil moisture retrieval. It is worthwhile before establishing models and can also help with understanding the underlying GNSS-R phenomena and interpreting data.

Soil moisture retrieval from remote sensing measurements: Current knowledge and directions for the future

2021 ◽  
pp. 103673
Author(s):  
Zhao-Liang Li ◽  
Pei Leng ◽  
Cheng-Hu Zhou ◽  
Kun-Shan Chen ◽  
Fang-Cheng Zhou ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Current Knowledge ◽  
The Future

scholarly journals Suitability Evaluation of Typical Drought Index in Soil Moisture Retrieval and Monitoring Based on Optical Images

2020 ◽  
Vol 12 (16) ◽  
pp. 2587
Author(s):  
Yan Nie ◽  
Ying Tan ◽  
Yuqin Deng ◽  
Jing Yu
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Arid Regions ◽  
Drought Index ◽  
Dynamic Monitoring ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Rapid Acquisition ◽  
Semi Arid ◽  
Soil Moisture Inversion

As a basic agricultural parameter in the formation, transformation, and consumption of surface water resources, soil moisture has a very important influence on the vegetation growth, agricultural production, and healthy operation of regional ecosystems. The Aksu river basin is a typical semi-arid agricultural area which seasonally suffers from water shortage. Due to the lack of knowledge on soil moisture change, the water management and decision-making processes have been a difficult issue for local government. Therefore, soil moisture monitoring by remote sensing became a reasonable way to schedule crop irrigation and evaluate the irrigation efficiency. Compared to in situ measurements, the use of remote sensing for the monitoring of soil water content is convenient and can be repetitively applied over a large area. To verify the applicability of the typical drought index to the rapid acquisition of soil moisture in arid and semi-arid regions, this study simulated, compared, and validated the effectiveness of soil moisture inversion. GF-1 WFV images, Landsat 8 OLI images, and the measured soil moisture data were used to determine the Perpendicular Drought Index (PDI), the Modified Perpendicular Drought Index (MPDI), and the Vegetation Adjusted Perpendicular Drought Index (VAPDI). First, the determination coefficients of the correlation analyses on the PDI, MPDI, VAPDI, and measured soil moisture in the 0–10, 10–20, and 20–30 cm depth layers based on the GF-1 WFV and Landsat 8 OLI images were good. Notably, in the 0–10 cm depth layers, the average determination coefficient was 0.68; all models met the accuracy requirements of soil moisture inversion. Both indicated that the drought indices based on the Near Infrared (NIR)-Red spectral space derived from the optical remote sensing images are more sensitive to soil moisture near the surface layer; however, the accuracy of retrieving the soil moisture in deep layers was slightly lower in the study area. Second, in areas of vegetation coverage, MPDI and VAPDI had a higher inversion accuracy than PDI. To a certain extent, they overcame the influence of mixed pixels on the soil moisture spectral information. VAPDI modified by Perpendicular Vegetation Index (PVI) was not susceptible to vegetation saturation and, thus, had a higher inversion accuracy, which makes it performs better than MPDI’s in vegetated areas. Third, the spatial heterogeneity of the soil moisture retrieved by the GF-1 WFV and Landsat 8 OLI image were similar. However, the GF-1 WFV images were more sensitive to changes in the soil moisture, which reflected the actual soil moisture level covered by different vegetation. These results provide a practical reference for the dynamic monitoring of surface soil moisture, obtaining agricultural information and agricultural condition parameters in arid and semi-arid regions.

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