Capabilities of Hyperspectral Remote Sensing Data to Detect Soil Salinity
The objectives of the current study were to investigate the opportunity of estimating soil salinity from hyperspectral data and identifying the most informative spectral zones for estimation. Electrical conductivity (EC) measurements of ninety topsoil samples (0–30 cm) collected from Toshka, Egypt,...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | Arabic |
| Published: |
The Union of Arab Universities
2021-12-01
|
| Series: | Arab Universities Journal of Agricultural Sciences |
| Subjects: | |
| Online Access: | https://ajs.journals.ekb.eg/article_211686_44569dcc9c94f3c91aeba49ee4d166ee.pdf |
| _version_ | 1827323668403847168 |
|---|---|
| author | Abdelrahman Medhat Saleh Mohammed Abd-Elwahed Yasser Metwally Sayed Arafat |
| author_facet | Abdelrahman Medhat Saleh Mohammed Abd-Elwahed Yasser Metwally Sayed Arafat |
| author_sort | Abdelrahman Medhat Saleh |
| collection | DOAJ |
| description | The objectives of the current study were to investigate the opportunity of estimating soil salinity from hyperspectral data and identifying the most informative spectral zones for estimation. Electrical conductivity (EC) measurements of ninety topsoil samples (0–30 cm) collected from Toshka, Egypt, were used as data set. Analytical spectral device was employed to collect the reflectance spectral signatures of soil samples. Both linear regression and HSD Tukey’s analyses displayed that the SWIR1 and SWIR2 zones are the most suitable for soil salinity prediction while, blue, green and NIR were the wickedest. Moreover, EC estimation was better in case of lower soil salinity (0-2 dS m-1) than higher levels (8-1). Partial-least-squares-regression (ΡLSR) was employed to establish soil salinity prediction model using the training set of soil samples (n=75). The PLSR model was set up using the most informative wave bands (SWIR1 and SWIR2). The result showed that PLSR linear model gave a precise prediction of soil salinity (R2 = 0.93). The results revealed that employing reflectance values in SWIR in the model variables increases the precision of soil EC prediction. |
| first_indexed | 2024-04-25T01:55:36Z |
| format | Article |
| id | doaj.art-afe99d9805114b209ebdc029e17b0213 |
| institution | Directory Open Access Journal |
| issn | 1110-2675 2636-3585 |
| language | Arabic |
| last_indexed | 2024-04-25T01:55:36Z |
| publishDate | 2021-12-01 |
| publisher | The Union of Arab Universities |
| record_format | Article |
| series | Arab Universities Journal of Agricultural Sciences |
| spelling | doaj.art-afe99d9805114b209ebdc029e17b02132024-03-07T17:27:41ZaraThe Union of Arab UniversitiesArab Universities Journal of Agricultural Sciences1110-26752636-35852021-12-0129394395210.21608/ajs.2021.87863.1402211686Capabilities of Hyperspectral Remote Sensing Data to Detect Soil SalinityAbdelrahman Medhat Saleh0Mohammed Abd-Elwahed1Yasser Metwally2Sayed Arafat3Soil Science Department, Faculty of Agriculture, Ain Shams University, Cairo EgyptSoil Science Department, Faculty of Agriculture, Ain Shams University, Cairo EgyptSoil Science Department, Faculty of Agriculture, Ain Shams University, Cairo, EgyptAgric. appl., Soils and Marine Division, National Authority for Remote Sensing and Space Sci., (NARSS), Cairo, EgyptThe objectives of the current study were to investigate the opportunity of estimating soil salinity from hyperspectral data and identifying the most informative spectral zones for estimation. Electrical conductivity (EC) measurements of ninety topsoil samples (0–30 cm) collected from Toshka, Egypt, were used as data set. Analytical spectral device was employed to collect the reflectance spectral signatures of soil samples. Both linear regression and HSD Tukey’s analyses displayed that the SWIR1 and SWIR2 zones are the most suitable for soil salinity prediction while, blue, green and NIR were the wickedest. Moreover, EC estimation was better in case of lower soil salinity (0-2 dS m-1) than higher levels (8-1). Partial-least-squares-regression (ΡLSR) was employed to establish soil salinity prediction model using the training set of soil samples (n=75). The PLSR model was set up using the most informative wave bands (SWIR1 and SWIR2). The result showed that PLSR linear model gave a precise prediction of soil salinity (R2 = 0.93). The results revealed that employing reflectance values in SWIR in the model variables increases the precision of soil EC prediction.https://ajs.journals.ekb.eg/article_211686_44569dcc9c94f3c91aeba49ee4d166ee.pdfsoil salinityhyperspectralremote sensingplsr model |
| spellingShingle | Abdelrahman Medhat Saleh Mohammed Abd-Elwahed Yasser Metwally Sayed Arafat Capabilities of Hyperspectral Remote Sensing Data to Detect Soil Salinity Arab Universities Journal of Agricultural Sciences soil salinity hyperspectral remote sensing plsr model |
| title | Capabilities of Hyperspectral Remote Sensing Data to Detect Soil Salinity |
| title_full | Capabilities of Hyperspectral Remote Sensing Data to Detect Soil Salinity |
| title_fullStr | Capabilities of Hyperspectral Remote Sensing Data to Detect Soil Salinity |
| title_full_unstemmed | Capabilities of Hyperspectral Remote Sensing Data to Detect Soil Salinity |
| title_short | Capabilities of Hyperspectral Remote Sensing Data to Detect Soil Salinity |
| title_sort | capabilities of hyperspectral remote sensing data to detect soil salinity |
| topic | soil salinity hyperspectral remote sensing plsr model |
| url | https://ajs.journals.ekb.eg/article_211686_44569dcc9c94f3c91aeba49ee4d166ee.pdf |
| work_keys_str_mv | AT abdelrahmanmedhatsaleh capabilitiesofhyperspectralremotesensingdatatodetectsoilsalinity AT mohammedabdelwahed capabilitiesofhyperspectralremotesensingdatatodetectsoilsalinity AT yassermetwally capabilitiesofhyperspectralremotesensingdatatodetectsoilsalinity AT sayedarafat capabilitiesofhyperspectralremotesensingdatatodetectsoilsalinity |