Comparison of Recent Remote Sensing Data Using an Artificial Neural Network to Predict Soil Moisture by Focusing on Radiometric Indices
Remote sensing data is widely used as a common variable for digital soil mapping estimating models. The aim of this study, quite recently made available to researchers Operational Land Imager 2 (OLI–2) have structure Landsat 9 and Landsat 8 (OLI) and Sentinel 2A (MSI) to compare the performance of s...
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Hasan Eleroğlu
2022-12-01
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| Series: | Turkish Journal of Agriculture: Food Science and Technology |
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| Online Access: | http://www.agrifoodscience.com/index.php/TURJAF/article/view/5477 |
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| author | Miraç Kılıç Recep Gündoğan |
| author_facet | Miraç Kılıç Recep Gündoğan |
| author_sort | Miraç Kılıç |
| collection | DOAJ |
| description | Remote sensing data is widely used as a common variable for digital soil mapping estimating models. The aim of this study, quite recently made available to researchers Operational Land Imager 2 (OLI–2) have structure Landsat 9 and Landsat 8 (OLI) and Sentinel 2A (MSI) to compare the performance of soil moisture estimation in multi-layer perceptron network (MLP) artificial intelligence algorithm of image data. The working area is 886.78 km2 and soil sampling was performed at 66 points for gravimetric soil moisture determination. In addition, after the satellite images were pre-processed, Soil Adjusted Vegetation Index (SAVI) and Normalized Difference Moisture Index (NDMI) were calculated. Landsat 9 (OLI-2) based SAVI and NDMI showed a moderately significant positive correlation relationship with gravimetric soil moisture (rSAVI-SM=0.62, rNMDI-SM=0.44). The relationship between Landsat 8 (OLI) (rSAVI-SM=0.57, rNDMI-SM=0.11) and Sentinel 2A (MSI) (rSAVI-SM=0.42, rNDMI-SM=0.27) based radiometric indices and soil moisture was lower than Landsat 9 (OLI-2). RMSE values of MLP models were found to be respectively 0.79, 1.16 and 1.17 for Landsat 9 (OLI-2), Landsat 8 (OLI) and Sentinel 2A (MSI). Our results showed that with an Operational Land Imager (OLI-2) and near and short-wave infrared wavelengths improvements to multispectral imaging have improved soil moisture estimation success. |
| format | Article |
| id | doaj-art-a56788f5f0c344c19dcd2a5e54dc2160 |
| institution | Kabale University |
| issn | 2148-127X |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Hasan Eleroğlu |
| record_format | Article |
| series | Turkish Journal of Agriculture: Food Science and Technology |
| spelling | doaj-art-a56788f5f0c344c19dcd2a5e54dc21602025-08-20T03:38:31ZengHasan EleroğluTurkish Journal of Agriculture: Food Science and Technology2148-127X2022-12-0110122438244510.24925/turjaf.v10i12.2438-2445.54772691Comparison of Recent Remote Sensing Data Using an Artificial Neural Network to Predict Soil Moisture by Focusing on Radiometric IndicesMiraç Kılıç0Recep Gündoğan1Department of Plant and Animal Production, Vocational School of Kahta, Adıyaman University, AdıyamanDepartment of Soil Science and Plant Nutrition, Agriculture Faculty, Harran University, SanlıurfaRemote sensing data is widely used as a common variable for digital soil mapping estimating models. The aim of this study, quite recently made available to researchers Operational Land Imager 2 (OLI–2) have structure Landsat 9 and Landsat 8 (OLI) and Sentinel 2A (MSI) to compare the performance of soil moisture estimation in multi-layer perceptron network (MLP) artificial intelligence algorithm of image data. The working area is 886.78 km2 and soil sampling was performed at 66 points for gravimetric soil moisture determination. In addition, after the satellite images were pre-processed, Soil Adjusted Vegetation Index (SAVI) and Normalized Difference Moisture Index (NDMI) were calculated. Landsat 9 (OLI-2) based SAVI and NDMI showed a moderately significant positive correlation relationship with gravimetric soil moisture (rSAVI-SM=0.62, rNMDI-SM=0.44). The relationship between Landsat 8 (OLI) (rSAVI-SM=0.57, rNDMI-SM=0.11) and Sentinel 2A (MSI) (rSAVI-SM=0.42, rNDMI-SM=0.27) based radiometric indices and soil moisture was lower than Landsat 9 (OLI-2). RMSE values of MLP models were found to be respectively 0.79, 1.16 and 1.17 for Landsat 9 (OLI-2), Landsat 8 (OLI) and Sentinel 2A (MSI). Our results showed that with an Operational Land Imager (OLI-2) and near and short-wave infrared wavelengths improvements to multispectral imaging have improved soil moisture estimation success.http://www.agrifoodscience.com/index.php/TURJAF/article/view/5477multi-layer perceptronlandsat 9soil moisturesoil adjusted vegetation indexnormalized difference moisture index |
| spellingShingle | Miraç Kılıç Recep Gündoğan Comparison of Recent Remote Sensing Data Using an Artificial Neural Network to Predict Soil Moisture by Focusing on Radiometric Indices Turkish Journal of Agriculture: Food Science and Technology multi-layer perceptron landsat 9 soil moisture soil adjusted vegetation index normalized difference moisture index |
| title | Comparison of Recent Remote Sensing Data Using an Artificial Neural Network to Predict Soil Moisture by Focusing on Radiometric Indices |
| title_full | Comparison of Recent Remote Sensing Data Using an Artificial Neural Network to Predict Soil Moisture by Focusing on Radiometric Indices |
| title_fullStr | Comparison of Recent Remote Sensing Data Using an Artificial Neural Network to Predict Soil Moisture by Focusing on Radiometric Indices |
| title_full_unstemmed | Comparison of Recent Remote Sensing Data Using an Artificial Neural Network to Predict Soil Moisture by Focusing on Radiometric Indices |
| title_short | Comparison of Recent Remote Sensing Data Using an Artificial Neural Network to Predict Soil Moisture by Focusing on Radiometric Indices |
| title_sort | comparison of recent remote sensing data using an artificial neural network to predict soil moisture by focusing on radiometric indices |
| topic | multi-layer perceptron landsat 9 soil moisture soil adjusted vegetation index normalized difference moisture index |
| url | http://www.agrifoodscience.com/index.php/TURJAF/article/view/5477 |
| work_keys_str_mv | AT mirackılıc comparisonofrecentremotesensingdatausinganartificialneuralnetworktopredictsoilmoisturebyfocusingonradiometricindices AT recepgundogan comparisonofrecentremotesensingdatausinganartificialneuralnetworktopredictsoilmoisturebyfocusingonradiometricindices |