Optimal window size selection for spectral information extraction of sampling points from UAV multispectral images for soil moisture content inversion

Abstract A soil moisture content map is important for providing information about the distribution of moisture in a given area. Moisture content directly influences agricultural yield thus it is crucial to have accurate and reliable information about moisture distribution and content in the field. Since soil is a porous medium modified generalized Archie’s equation provides the basic formula to calculate moisture content data based on measured ECa. In this study we aimed to find a more accurate and cost effective method for measuring moisture content than manual field sampling. Locations of 25 sampling points were chosen from our research field as a reference. We assumed that soil moisture content could be calculated by measuring apparent electrical conductivity (ECa) using the Veris-3100 on-the-go soil mapping tool. Statistical analysis was carried out on the 10.791 ECa raw data in order to filter the outliers. The applied statistical method was ±1.5 interquartile (IRQ) distance approach. The visualization of soil moisture distribution within the experimental field was carried out by means of ArcGIS/ArcMAP using the inverse distance weighting interpolation method. In the investigated 25 sampling points, coefficient of determination between calculated volumetric moisture content data and measured ECa was R2 = 0.87. According to our results, volumetric moisture content can be mapped by applying ECa measurements in these particular soil types.

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Analysis on Soil Moisture Content in the Middle Reaches of the Yellow River

Arid Zone Research ◽

10.3724/sp.j.1148.2011.00085 ◽

2011 ◽

Vol 28 (1) ◽

pp. 85-91 ◽

Cited By ~ 2

Author(s):

Run-chun LI ◽

Xiu-zhi ZHANG ◽

Li-hua WANG ◽

Xin-yan LV ◽

Yuan GAO

Keyword(s):

Soil Moisture ◽

Moisture Content ◽

Soil Moisture Content ◽

Yellow River ◽

The Yellow River

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Analysis on Grey Correlation Degree of Soil Moisture Content under Different Land Use Types in Wind-Blown Sand Area

Arid Zone Research ◽

10.3724/sp.j.1148.2009.00840 ◽

2010 ◽

Vol 26 (6) ◽

pp. 840-845

Author(s):

Lin CHEN ◽

Lei WANG ◽

Qing-xia ZHANG ◽

Nai-ping SONG

Keyword(s):

Land Use ◽

Soil Moisture ◽

Moisture Content ◽

Soil Moisture Content ◽

Grey Correlation ◽

Land Use Types ◽

Correlation Degree ◽

Grey Correlation Degree

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

Silva Gandavensis ◽

10.21825/sg.v66i0.817 ◽

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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Mapping barrier island soil moisture using a radiative transfer model of hyperspectral imagery from an unmanned aerial system

Scientific Reports ◽

10.1038/s41598-021-82783-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Rehman S. Eon ◽

Charles M. Bachmann

Keyword(s):

Soil Moisture ◽

Radiative Transfer ◽

Moisture Content ◽

Layer Thickness ◽

Soil Moisture Content ◽

Hyperspectral Imagery ◽

Barrier Island ◽

Water Layer ◽

Radiative Transfer Model ◽

Transfer Model

AbstractThe advent of remote sensing from unmanned aerial systems (UAS) has opened the door to more affordable and effective methods of imaging and mapping of surface geophysical properties with many important applications in areas such as coastal zone management, ecology, agriculture, and defense. We describe a study to validate and improve soil moisture content retrieval and mapping from hyperspectral imagery collected by a UAS system. Our approach uses a recently developed model known as the multilayer radiative transfer model of soil reflectance (MARMIT). MARMIT partitions contributions due to water and the sediment surface into equivalent but separate layers and describes these layers using an equivalent slab model formalism. The model water layer thickness along with the fraction of wet surface become parameters that must be optimized in a calibration step, with extinction due to water absorption being applied in the model based on equivalent water layer thickness, while transmission and reflection coefficients follow the Fresnel formalism. In this work, we evaluate the model in both field settings, using UAS hyperspectral imagery, and laboratory settings, using hyperspectral spectra obtained with a goniometer. Sediment samples obtained from four different field sites representing disparate environmental settings comprised the laboratory analysis while field validation used hyperspectral UAS imagery and coordinated ground truth obtained on a barrier island shore during field campaigns in 2018 and 2019. Analysis of the most significant wavelengths for retrieval indicate a number of different wavelengths in the short-wave infra-red (SWIR) that provide accurate fits to measured soil moisture content in the laboratory with normalized root mean square error (NRMSE)< 0.145, while independent evaluation from sequestered test data from the hyperspectral UAS imagery obtained during the field campaign obtained an average NRMSE = 0.169 and median NRMSE = 0.152 in a bootstrap analysis.

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Estimating Agricultural Soil Moisture Content through UAV-Based Hyperspectral Images in the Arid Region

Remote Sensing ◽

10.3390/rs13081562 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1562

Author(s):

Xiangyu Ge ◽

Jianli Ding ◽

Xiuliang Jin ◽

Jingzhe Wang ◽

Xiangyue Chen ◽

...

Keyword(s):

Data Mining ◽

Soil Moisture ◽

Moisture Content ◽

Hyperspectral Imaging ◽

Fractional Order ◽

Arid Regions ◽

Soil Moisture Content ◽

Order Derivative ◽

Imaging Data ◽

Strategy I

Unmanned aerial vehicle (UAV)-based hyperspectral remote sensing is an important monitoring technology for the soil moisture content (SMC) of agroecological systems in arid regions. This technology develops precision farming and agricultural informatization. However, hyperspectral data are generally used in data mining. In this study, UAV-based hyperspectral imaging data with a resolution o 4 cm and totaling 70 soil samples (0–10 cm) were collected from farmland (2.5 × 104 m2) near Fukang City, Xinjiang Uygur Autonomous Region, China. Four estimation strategies were tested: the original image (strategy I), first- and second-order derivative methods (strategy II), the fractional-order derivative (FOD) technique (strategy III), and the optimal fractional order combined with the optimal multiband indices (strategy IV). These strategies were based on the eXtreme Gradient Boost (XGBoost) algorithm, with the aim of building the best estimation model for agricultural SMC in arid regions. The results demonstrated that FOD technology could effectively mine information (with an absolute maximum correlation coefficient of 0.768). By comparison, strategy IV yielded the best estimates out of the methods tested (R2val = 0.921, RMSEP = 1.943, and RPD = 2.736) for the SMC. The model derived from the order of 0.4 within strategy IV worked relatively well among the different derivative methods (strategy I, II, and III). In conclusion, the combination of FOD technology and the optimal multiband indices generated a highly accurate model within the XGBoost algorithm for SMC estimation. This research provided a promising data mining approach for UAV-based hyperspectral imaging data.

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Estimation of soil microbial C by A fumigation-extraction procedure: Influence of soil moisture content

Soil Biology and Biochemistry ◽

10.1016/0038-0717(89)90168-5 ◽

1989 ◽

Vol 21 (6) ◽

pp. 767-772 ◽

Cited By ~ 41

Author(s):

D ROSS

Keyword(s):

Soil Moisture ◽

Moisture Content ◽

Soil Moisture Content ◽

Extraction Procedure ◽

Soil Microbial ◽

Microbial C ◽

Fumigation Extraction

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A GNSS-IR Method for Retrieving Soil Moisture Content from Integrated Multi-Satellite Data That Accounts for the Impact of Vegetation Moisture Content

Remote Sensing ◽

10.3390/rs13132442 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2442

Author(s):

Jichao Lv ◽

Rui Zhang ◽

Jinsheng Tu ◽

Mingjie Liao ◽

Jiatai Pang ◽

...

Keyword(s):

Soil Moisture ◽

Moisture Content ◽

Satellite Data ◽

Vegetation Index ◽

Soil Moisture Content ◽

Support Vector ◽

Observation Data ◽

Multipath Effect ◽

Mars Model ◽

The Impact

There are two problems with using global navigation satellite system-interferometric reflectometry (GNSS-IR) to retrieve the soil moisture content (SMC) from single-satellite data: the difference between the reflection regions, and the difficulty in circumventing the impact of seasonal vegetation growth on reflected microwave signals. This study presents a multivariate adaptive regression spline (MARS) SMC retrieval model based on integrated multi-satellite data on the impact of the vegetation moisture content (VMC). The normalized microwave reflection index (NMRI) calculated with the multipath effect is mapped to the normalized difference vegetation index (NDVI) to estimate and eliminate the impact of VMC. A MARS model for retrieving the SMC from multi-satellite data is established based on the phase shift. To examine its reliability, the MARS model was compared with a multiple linear regression (MLR) model, a backpropagation neural network (BPNN) model, and a support vector regression (SVR) model in terms of the retrieval accuracy with time-series observation data collected at a typical station. The MARS model proposed in this study effectively retrieved the SMC, with a correlation coefficient (R2) of 0.916 and a root-mean-square error (RMSE) of 0.021 cm3/cm3. The elimination of the vegetation impact led to 3.7%, 13.9%, 11.7%, and 16.6% increases in R2 and 31.3%, 79.7%, 49.0%, and 90.5% decreases in the RMSE for the SMC retrieved by the MLR, BPNN, SVR, and MARS model, respectively. The results demonstrated the feasibility of correcting the vegetation changes based on the multipath effect and the reliability of the MARS model in retrieving the SMC.

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