Estimation of High-Resolution Soil Moisture in Canadian Croplands Using Deep Neural Network with Sentinel-1 and Sentinel-2 Images
Soil moisture (SM) is a crucial hydrologic factor that affects the global cycle of energy, carbon, and water, as well as plant growth and crop yield; therefore, an accurate estimate of SM is important for both the global environment and agriculture. Satellite-based SM data have been provided by the...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-08-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/16/4063 |
| _version_ | 1827728781704429568 |
|---|---|
| author | Soo-Jin Lee Chuluong Choi Jinsoo Kim Minha Choi Jaeil Cho Yangwon Lee |
| author_facet | Soo-Jin Lee Chuluong Choi Jinsoo Kim Minha Choi Jaeil Cho Yangwon Lee |
| author_sort | Soo-Jin Lee |
| collection | DOAJ |
| description | Soil moisture (SM) is a crucial hydrologic factor that affects the global cycle of energy, carbon, and water, as well as plant growth and crop yield; therefore, an accurate estimate of SM is important for both the global environment and agriculture. Satellite-based SM data have been provided by the National Aeronautics and Space Administration (NASA)’s Soil Moisture Active Passive (SMAP) and the European Space Agency (ESA)’s Soil Moisture and Ocean Salinity (SMOS) satellite missions, but these data are based on passive microwave sensors, which have limited spatial resolution. Thus, detailed observations and analyses of the local distribution of SM are limited. The recent emergence of deep learning techniques, such as rectified linear unit (ReLU) and dropout, has produced effective solutions to complex problems. Deep neural networks (DNNs) have been used to accurately estimate hydrologic factors, such as SM and evapotranspiration, but studies of SM estimates derived from the joint use of DNN and high-resolution satellite data, such as Sentinel-1 and Sentinel-2, are lacking. In this study, we aim to estimate high-resolution SM at 30 m resolution, which is important for local-scale SM monitoring in croplands. We used a variety of input data, such as radar factors, optical factors, and vegetation indices, which can be extracted from Sentinel-1 and -2, terrain information (e.g., elevation), and crop information (e.g., cover type and month), and developed an integrated SM model across various crop surfaces by using these input data and DNN (which can learn the complexity and nonlinearity of the various data). The study was performed in the agricultural areas of Manitoba and Saskatchewan, Canada, and the in situ SM data for these areas were obtained from the Agriculture and Agri-Food Canada (AAFC) Real-time In Situ Soil Monitoring for Agriculture (RISMA) network. We conducted various experiments with several hyperparameters that affected the performance of the DNN-based model and ultimately obtained a high-performing SM model. The optimal SM model had a root-mean-square error (RMSE) of 0.0416 m<sup>3</sup>/m<sup>3</sup> and a correlation coefficient (CC) of 0.9226. This model’s estimates showed better agreement with in situ SM than the SMAP 9 km SM. The accuracy of the model was high when the daily precipitation was zero or very low and also during the vegetation growth stage. However, its accuracy decreased when precipitation or the vitality of the vegetation were high. This suggests that precipitation affects surface erosion and water layer formation, and vegetation adds complexity to the SM estimate. Nevertheless, the distribution of SM estimated by our model generally reflected the local soil characteristics. This work will aid in drought and flood prevention and mitigation, and serve as a tool for assessing the potential growth of crops according to SM conditions. |
| first_indexed | 2024-03-10T23:36:46Z |
| format | Article |
| id | doaj.art-89b11ec38ec04b79b6c8b546b19d2f07 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T23:36:46Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-89b11ec38ec04b79b6c8b546b19d2f072023-11-19T02:54:01ZengMDPI AGRemote Sensing2072-42922023-08-011516406310.3390/rs15164063Estimation of High-Resolution Soil Moisture in Canadian Croplands Using Deep Neural Network with Sentinel-1 and Sentinel-2 ImagesSoo-Jin Lee0Chuluong Choi1Jinsoo Kim2Minha Choi3Jaeil Cho4Yangwon Lee5Geomatics Research Institute, Pukyong National University, Busan 48513, Republic of KoreaDepartment of Spatial Information Engineering, Pukyong National University, Busan 48513, Republic of KoreaDepartment of Spatial Information Engineering, Pukyong National University, Busan 48513, Republic of KoreaDepartment of Water Resources, Graduate School of Water Resources, Sungkyunkwan University, Suwon 16419, Republic of KoreaDepartment of Applied Plant Science, Chonnam National University, Gwangju 61186, Republic of KoreaDepartment of Spatial Information Engineering, Pukyong National University, Busan 48513, Republic of KoreaSoil moisture (SM) is a crucial hydrologic factor that affects the global cycle of energy, carbon, and water, as well as plant growth and crop yield; therefore, an accurate estimate of SM is important for both the global environment and agriculture. Satellite-based SM data have been provided by the National Aeronautics and Space Administration (NASA)’s Soil Moisture Active Passive (SMAP) and the European Space Agency (ESA)’s Soil Moisture and Ocean Salinity (SMOS) satellite missions, but these data are based on passive microwave sensors, which have limited spatial resolution. Thus, detailed observations and analyses of the local distribution of SM are limited. The recent emergence of deep learning techniques, such as rectified linear unit (ReLU) and dropout, has produced effective solutions to complex problems. Deep neural networks (DNNs) have been used to accurately estimate hydrologic factors, such as SM and evapotranspiration, but studies of SM estimates derived from the joint use of DNN and high-resolution satellite data, such as Sentinel-1 and Sentinel-2, are lacking. In this study, we aim to estimate high-resolution SM at 30 m resolution, which is important for local-scale SM monitoring in croplands. We used a variety of input data, such as radar factors, optical factors, and vegetation indices, which can be extracted from Sentinel-1 and -2, terrain information (e.g., elevation), and crop information (e.g., cover type and month), and developed an integrated SM model across various crop surfaces by using these input data and DNN (which can learn the complexity and nonlinearity of the various data). The study was performed in the agricultural areas of Manitoba and Saskatchewan, Canada, and the in situ SM data for these areas were obtained from the Agriculture and Agri-Food Canada (AAFC) Real-time In Situ Soil Monitoring for Agriculture (RISMA) network. We conducted various experiments with several hyperparameters that affected the performance of the DNN-based model and ultimately obtained a high-performing SM model. The optimal SM model had a root-mean-square error (RMSE) of 0.0416 m<sup>3</sup>/m<sup>3</sup> and a correlation coefficient (CC) of 0.9226. This model’s estimates showed better agreement with in situ SM than the SMAP 9 km SM. The accuracy of the model was high when the daily precipitation was zero or very low and also during the vegetation growth stage. However, its accuracy decreased when precipitation or the vitality of the vegetation were high. This suggests that precipitation affects surface erosion and water layer formation, and vegetation adds complexity to the SM estimate. Nevertheless, the distribution of SM estimated by our model generally reflected the local soil characteristics. This work will aid in drought and flood prevention and mitigation, and serve as a tool for assessing the potential growth of crops according to SM conditions.https://www.mdpi.com/2072-4292/15/16/4063soil moisturedeep neural networkSentinel-1Sentinel-2 |
| spellingShingle | Soo-Jin Lee Chuluong Choi Jinsoo Kim Minha Choi Jaeil Cho Yangwon Lee Estimation of High-Resolution Soil Moisture in Canadian Croplands Using Deep Neural Network with Sentinel-1 and Sentinel-2 Images Remote Sensing soil moisture deep neural network Sentinel-1 Sentinel-2 |
| title | Estimation of High-Resolution Soil Moisture in Canadian Croplands Using Deep Neural Network with Sentinel-1 and Sentinel-2 Images |
| title_full | Estimation of High-Resolution Soil Moisture in Canadian Croplands Using Deep Neural Network with Sentinel-1 and Sentinel-2 Images |
| title_fullStr | Estimation of High-Resolution Soil Moisture in Canadian Croplands Using Deep Neural Network with Sentinel-1 and Sentinel-2 Images |
| title_full_unstemmed | Estimation of High-Resolution Soil Moisture in Canadian Croplands Using Deep Neural Network with Sentinel-1 and Sentinel-2 Images |
| title_short | Estimation of High-Resolution Soil Moisture in Canadian Croplands Using Deep Neural Network with Sentinel-1 and Sentinel-2 Images |
| title_sort | estimation of high resolution soil moisture in canadian croplands using deep neural network with sentinel 1 and sentinel 2 images |
| topic | soil moisture deep neural network Sentinel-1 Sentinel-2 |
| url | https://www.mdpi.com/2072-4292/15/16/4063 |
| work_keys_str_mv | AT soojinlee estimationofhighresolutionsoilmoistureincanadiancroplandsusingdeepneuralnetworkwithsentinel1andsentinel2images AT chuluongchoi estimationofhighresolutionsoilmoistureincanadiancroplandsusingdeepneuralnetworkwithsentinel1andsentinel2images AT jinsookim estimationofhighresolutionsoilmoistureincanadiancroplandsusingdeepneuralnetworkwithsentinel1andsentinel2images AT minhachoi estimationofhighresolutionsoilmoistureincanadiancroplandsusingdeepneuralnetworkwithsentinel1andsentinel2images AT jaeilcho estimationofhighresolutionsoilmoistureincanadiancroplandsusingdeepneuralnetworkwithsentinel1andsentinel2images AT yangwonlee estimationofhighresolutionsoilmoistureincanadiancroplandsusingdeepneuralnetworkwithsentinel1andsentinel2images |