scholarly journals GIS and Remote Sensing Aided Information for Soil Moisture Estimation: A Comparative Study of Interpolation Techniques

Resources ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 70 ◽  
Author(s):  
Prashant K. Srivastava ◽  
Prem C. Pandey ◽  
George P. Petropoulos ◽  
Nektarios N. Kourgialas ◽  
Varsha Pandey ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Moisture Distribution ◽  
Added Value ◽  
Gis And Remote Sensing ◽  
Study Results ◽  
Distance Weighting ◽  
Vital Component ◽  
Interpolation Techniques ◽  
Secondary Variable

Soil moisture represents a vital component of the ecosystem, sustaining life-supporting activities at micro and mega scales. It is a highly required parameter that may vary significantly both spatially and temporally. Due to this fact, its estimation is challenging and often hard to obtain especially over large, heterogeneous surfaces. This study aimed at comparing the performance of four widely used interpolation methods in estimating soil moisture using GPS-aided information and remote sensing. The Distance Weighting (IDW), Spline, Ordinary Kriging models and Kriging with External Drift (KED) interpolation techniques were employed to estimate soil moisture using 82 soil moisture field-measured values. Of those measurements, data from 54 soil moisture locations were used for calibration and the remaining data for validation purposes. The study area selected was Varanasi City, India covering an area of 1535 km2. The soil moisture distribution results demonstrate the lowest RMSE (root mean square error, 8.69%) for KED, in comparison to the other approaches. For KED, the soil organic carbon information was incorporated as a secondary variable. The study results contribute towards efforts to overcome the issue of scarcity of soil moisture information at local and regional scales. It also provides an understandable method to generate and produce reliable spatial continuous datasets of this parameter, demonstrating the added value of geospatial analysis techniques for this purpose.

Linking thermal UAS-based imagery and Cosmic-Ray Neutron Sensing data

2020 ◽  
Author(s):  
Veronika Döpper ◽  
Tobias Gränzig ◽  
Michael Förster ◽  
Birgit Kleinschmit
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Hydrological Modeling ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Cosmic Ray ◽  
Added Value ◽  
Innovative Technology ◽  
Sensing Data ◽  
Local Point

<p>Soil moisture content (SMC) is of fundamental importance to many hydrological, biological, biochemical and atmospheric processes. Common soil moisture measurements range from local point measurements to global remote sensing-based SMC datasets. Nevertheless, they always compromise between temporal and spatial resolution. Thus, it is still challenging to quantify spatially and temporally distributed SMC at a regional scale which is extremely relevant for hydrological modeling or agricultural management. The innovative technology Cosmic-Ray Neutron Sensing (CRNS) shows significant potential to fill this gap by quantifying the present hydrogen pools within footprints larger than 0.1 ha.</p><p>Owing to the difference in scale between the ground resolution of satellites used to retrieve soil moisture and the common point scale of ground-based soil moisture instruments, the large footprint of the CRNS poses a high potential for the validation of SMC remote sensing products. When linking the CRNS measurements with remote sensing data, the vertical and horizontal characteristics of its footprint need to be considered.</p><p>To examine the influence of the CRNS footprint characteristics on the linkage of CRNS and remote sensing data, we couple CRNS measurements with high-resolution UAS-based thermal imagery acquired at two sites in Bavaria and Brandenburg (Germany) using a radiometrically calibrated FLIR Tau 2 336 (FLIR Systems, Inc., Wilsonville, OR, USA) with a focal length of 9 mm. Within this context, we evaluate the added value of applying a horizontal weighting function to the spatially distributed thermal data in comparison to an unweighted mean when statistically representing the corrected neutron counting rates.</p><p>The project is part of the DFG-funded research group Cosmic Sense, which aims to provide interdisciplinary new representative insights into hydrological changes at the land surface.</p>

scholarly journals DOWNSCALING OF SMAP SOIL MOISTURE PRODUCT BY DATA FUSION WITH VIIRS LST/EVI PRODUCT

2021 ◽  
Vol XLVI-4/W5-2021 ◽  
pp. 355-360
Author(s):  
X. Lei ◽  
Y. Wang ◽  
T. Guo
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Data Fusion ◽  
Large Scale ◽  
Microwave Remote Sensing ◽  
Global Scale ◽  
Moisture Distribution ◽  
Optical Data ◽  
Hydrological Simulation ◽  
Original Dataset

Abstract. Soil moisture is an essential variable of environment and climate change, which affects the energy and water exchange between soil and atmosphere. The estimation of soil moisture is thus very important in geoscience, while at same time also challenging. Satellite remote sensing provides an efficient way for large-scale soil moisture distribution mapping, and microwave remote sensing satellites/sensors, such as Soil Moisture and Ocean Salinity (SMOS), Advanced Microwave Scanning Radiometer (AMSR), and Soil Moisture Active Passive (SMAP) satellite, are widely used to retrieve soil moisture in a global scale. However, most microwave products have relatively coarse resolution (tens of kilometres), which limits their application in regional hydrological simulation and disaster prevention. In this study, the SMAP soil moisture product with spatial resolution of 9km is downscaled to 750m by fusing with VIIRS optical products. The LST-EVI triangular space pattern provides the physical foundation for the microwave-optical data fusion, so that the downscaled soil moisture product not only matches well with the original SMAP product, but also presents more detailed distribution patterns compared with the original dataset. The results show a promising prospect to use the triangular method to produce finer soil moisture datasets (within 1 km) from the coarse soil moisture product.

scholarly journals Estimation of Bare Soil Moisture from Remote Sensing Indices in the 0.4–2.5 mm Spectral Range

2021 ◽  
Vol 2021 (2) ◽  
pp. 1-11
Author(s):  
Kubiak Katarzyna ◽  
Stypułkowska Justyna ◽  
Szymański Jakub ◽  
Spiralski Marcin
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Remote Sensing Data ◽  
Bare Soil ◽  
Moisture Distribution ◽  
Optical Methods ◽  
Soil Parameters ◽  
Sensing Data ◽  
Determination Coefficient ◽  
Remote Sensing Indices

Abstract Soil moisture content (SMC) is an important element of the environment, influencing water availability for plants and atmospheric parameters, and its monitoring is important for predicting floods or droughts and for weather and climate modeling. Optical methods for measuring soil moisture use spectral reflection analysis in the 350–2500 nm range. Remote sensing is considered to be an effective tool for monitoring soil parameters over large areas and to be more cost effective than in situ measurements. The aim of this study was to assess the SMC of bare soil on the basis of hyperspectral data from the ASD FieldSpec 4 Hi-Res field spectrometer by determining remote sensing indices and visualization based on multispectral data obtained from UAVs. Remote sensing measurements were validated on the basis of field humidity measurements with the HH2 Moisture Meter and ML3 ThetaProbe Soil Moisture Sensor. A strong correlation between terrestrial and remote sensing data was observed for 7 out of 11 selected indexes and the determination coefficient R2 values ranged from 67%– 87%. The best results were obtained for the NINSON index, with determination coefficient values of 87%, NSMI index (83.5%) and NINSOL (81.7%). We conclude that both hyperspectral and multispectral remote sensing data of bare soil moisture are valuable, providing good temporal and spatial resolution of soil moisture distribution in local areas, which is important for monitoring and forecasting local changes in climate.

Distribution of soil moisture content and its effect on the potential growth of the over-story species in North-East Chalkidiki

2001 ◽  
Vol 66 ◽  
Author(s):  
M. Aslanidou ◽  
P. Smiris
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Soil Depth ◽  
Moisture Distribution ◽  
Taxus Baccata ◽  
Mixed Stands ◽  
Potential Growth ◽  
North East ◽  
Soil Moisture Distribution

This  study deals with the soil moisture distribution and its effect on the  potential growth and    adaptation of the over-story species in north-east Chalkidiki. These  species are: Quercus    dalechampii Ten, Quercus  conferta Kit, Quercus  pubescens Willd, Castanea  sativa Mill, Fagus    moesiaca Maly-Domin and also Taxus baccata L. in mixed stands  with Fagus moesiaca.    Samples of soil, 1-2 kg per 20cm depth, were taken and the moisture content  of each sample    was measured in order to determine soil moisture distribution and its  contribution to the growth    of the forest species. The most important results are: i) available water  is influenced by the soil    depth. During the summer, at a soil depth of 10 cm a significant  restriction was observed. ii) the    large duration of the dry period in the deep soil layers has less adverse  effect on stands growth than in the case of the soil surface layers, due to the fact that the root system mainly spreads out    at a soil depth of 40 cm iii) in the beginning of the growing season, the  soil moisture content is    greater than 30 % at a soil depth of 60 cm, in beech and mixed beech-yew  stands, is 10-15 % in    the Q. pubescens  stands and it's more than 30 % at a soil depth of 60 cm in Q. dalechampii    stands.

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