Comparison of statistical prediction methods for characterizing the spatial variability of apparent electrical conductivity in coastal salt-affected farmland

Yao, Rongjiang; Yang, Jann N.; Gao, Peng; Shao, Hongbo; Liu, G. M.

doi:10.1007/s12665-013-2427-7

Cited by 5 publications

(2 citation statements)

References 33 publications

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“…With recent advances in sensor technology and the perspective of new satellites being launched with hyperspectral instruments, different studies are necessary for a better understanding of the spectral response of saline soils [6][7][8][9]. One state-of-the-art example is the German Environmental Mapping and Analysis (EnMAP) mission, scheduled for launch in 2019, carrying a sensor with more than 200 bands (400-2500 nm), with a spatial resolution of 30 m and an imaging swath width of 30 km [10].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Remote Sensing for Detecting Soil Salinization Using ProSpecTIR-VS Aerial Imagery and Sensor Simulation

et al. 2017

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Soil salinization due to irrigation affects agricultural productivity in the semi-arid region of Brazil. In this study, the performance of four computational models to estimate electrical conductivity (EC) (soil salinization) was evaluated using laboratory reflectance spectroscopy. To investigate the influence of bandwidth and band positioning on the EC estimates, we simulated the spectral resolution of two hyperspectral sensors (airborne ProSpecTIR-VS and orbital Hyperspectral Infrared Imager (HyspIRI)) and three multispectral instruments (RapidEye/REIS, High Resolution Geometric (HRG)/SPOT-5, and Operational Land Imager (OLI)/Landsat-8)). Principal component analysis (PCA) and the first-order derivative analysis were applied to the data to generate metrics associated with soil brightness and spectral features, respectively. The three sets of data (reflectance, PCA, and derivative) were tested as input variable for Extreme Learning Machine (ELM), Ordinary Least Square regression (OLS), Partial Least Squares Regression (PLSR), and Multilayer Perceptron (MLP). Finally, the laboratory models were inverted to a ProSpecTIR-VS image (400-2500 nm) acquired with 1-m spatial resolution in the northeast of Brazil. The objective was to estimate EC over exposed soils detected using the Normalized Difference Vegetation Index (NDVI). The results showed that the predictive ability of the linear models and ELM was better than that of the MLP, as indicated by higher values of the coefficient of determination (R 2 ) and ratio of the performance to deviation (RPD), and lower values of the root mean square error (RMSE). Metrics associated with soil brightness (reflectance and PCA scores) were more efficient in detecting changes in the EC produced by soil salinization than metrics related to spectral features (derivative). When applied to the image, the PLSR model with reflectance had an RMSE of 1.22 dS·m −1 and an RPD of 2.21, and was more suitable for detecting salinization (10-20 dS·m −1 ) in exposed soils (NDVI < 0.30) than the other models. For all computational models, lower values of RMSE and higher values of RPD were observed for the narrowband-simulated sensors compared to the broadband-simulated instruments. The soil EC estimates improved from the RapidEye to the HRG and OLI spectral resolutions, showing the importance of shortwave intervals (SWIR-1 and SWIR-2) in detecting soil salinization when the reflectance of selected bands is used in data modelling.

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Section: Introductionmentioning

confidence: 99%

Hyperspectral Remote Sensing for Detecting Soil Salinization Using ProSpecTIR-VS Aerial Imagery and Sensor Simulation

et al. 2017

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“…Scholars have done a great deal of research on this hot spot [13][14][15]. Compared with the spatial interpolation method [16] and the method of using a portable spectrometer to measure the spectral data, using the data measured by a satellite-borne imaging spectrometer to estimate soil salinization-related indicators [17] has the advantages of a small workload, strong timeliness, and a large spatial range. At present, most soil salinization mapping studies have been based on multispectral satellite image data [8,18,19], drone remote sensing data, and proximal hyperspectral remote sensing data [9,14,15].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Soil Salt and Ion Contents Based on Hyperspectral Remote Sensing Data: A Case Study of Baidunzi Basin, China

Wang

Zhang

Shen

et al. 2021

Water

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Soil salinity due to irrigation diversion affects regional agriculture, and the development of soil composition estimation models for the dynamic monitoring of regional salinity is important for salinity control. In this study, we evaluated the performance of hyperspectral data measured using an analytical spectral device (ASD) field spec standard-res hand-held spectrometer and satellite sensor visible shortwave infrared advanced hyperspectral imager (AHSI) in estimating the soil salt content (SSC). First derivative analysis (FDA) and principal component analysis (PCA) were applied to the data using the raw spectra (RS) to select the best model input data. We tested the ability of these three groups of data as input data for partial least squares regression (PLSR), principal component regression (PCR), and multiple linear regression (MLR). Finally, an estimation model of the SSC, Na+, Cl−, and SO42− contents was established using the best input data and modeling method, and a spatial distribution map of the soil composition content was drawn. The results show that the soil spectra obtained from the satellite hyperspectral data (AHSI) and laboratory spectral data (ASD) were consistent when the SSC was low, and as the SSC increased, the spectral curves of the ASD data showed little change in the curve characteristics, while the AHSI data showed more pronounced features, and this change was manifested in the AHSI images as darker pixels with a lower SSC and brighter pixels with a higher SSC. The AHSI data demonstrated a strong response to the change in SSC; therefore, the AHSI data had a greater advantage compared with the ASD data in estimating the soil salt content. In the modeling process, RS performed the best in estimating the SSC and Na+ content, with the R2 reaching 0.79 and 0.58, respectively, and obtaining low root mean squared error (RMSE) values. FDA and PCA performed the best in estimating Cl− and SO42−, while MLR outperformed PLSR and PCR in estimating the content of the soil components in the region. In addition, the hyperspectral camera data used in this study were very cost-effective and can potentially be used for the evaluation of soil salinization with a wide range and high accuracy, thus reducing the errors associated with the collection of individual samples using hand-held hyperspectral instruments.

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Review of soil salinity assessment for agriculture across multiple scales using proximal and/or remote sensors

Corwin

Scudiero

2019

Advances in Agronomy

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Mapping and monitoring soil spatial variability is particularly problematic for temporally and spatially dynamic properties such as soil salinity. The tools necessary to address this classic problem only reached maturity within the past 2 decades to enable field-to regional-scale salinity assessment of the root zone, including GPS, GIS, geophysical techniques involving proximal and remote sensors, and a greater understanding of apparent soil electrical conductivity (EC a ) and multi-and hyperspectral imagery. The concurrent development and application of these tools have made it possible to map soil salinity across multiple scales, which back in the 1980s was prohibitively expensive and impractical even at field scale. The combination of EC a -directed soil sampling and remote imagery has played a key role in mapping and monitoring soil salinity at large spatial extents with accuracy sufficient for applications ranging from field-scale site-specific management to statewide water allocation management to control salinity within irrigation districts. The objective of this paper is: (i) to present a review of the geophysical and remote imagery techniques used to assess soil salinity variability within the root zone from field to regional scales; (ii) to elucidate gaps in our knowledge and understanding of mapping soil salinity; and (iii) to synthesize existing knowledge to give new insight into the direction soil salinity mapping is heading to benefit policy makers, land resource managers, producers, agriculture consultants, extension specialists, and resource conservation field staff. The review covers the need and justification for mapping and monitoring salinity, basic concepts of soil salinity and its measurement, past geophysical and remote imagery research critical to salinity assessment, current approaches for mapping salinity at different scales, milestones in multi-scale salinity assessment, and future direction of field-to regional-scale salinity assessment. Highlights• A review of multi-scale soil salinity assessment using proximal and remote sensors is presented.

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Comparison of statistical prediction methods for characterizing the spatial variability of apparent electrical conductivity in coastal salt-affected farmland

Cited by 5 publications

References 33 publications

Hyperspectral Remote Sensing for Detecting Soil Salinization Using ProSpecTIR-VS Aerial Imagery and Sensor Simulation

Hyperspectral Remote Sensing for Detecting Soil Salinization Using ProSpecTIR-VS Aerial Imagery and Sensor Simulation

Estimation of Soil Salt and Ion Contents Based on Hyperspectral Remote Sensing Data: A Case Study of Baidunzi Basin, China

Review of soil salinity assessment for agriculture across multiple scales using proximal and/or remote sensors

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