Evaluation of soil moisture estimation techniques based on Sentinel-1 observations over wheat fields
Soil moisture (SM) is a key variable in agriculture and its monitoring is essential. SM determines the amount of water available to plants, having a direct impact on the development of crops, on the forecasting of crop yields and on the surveillance of food security. Microwave remote sensing offers...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-09-01
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| Series: | Agricultural Water Management |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0378377423002871 |
| _version_ | 1797746075998617600 |
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| author | María Arias Claudia Notarnicola Miguel Ángel Campo-Bescós Luis Miguel Arregui Jesús Álvarez-Mozos |
| author_facet | María Arias Claudia Notarnicola Miguel Ángel Campo-Bescós Luis Miguel Arregui Jesús Álvarez-Mozos |
| author_sort | María Arias |
| collection | DOAJ |
| description | Soil moisture (SM) is a key variable in agriculture and its monitoring is essential. SM determines the amount of water available to plants, having a direct impact on the development of crops, on the forecasting of crop yields and on the surveillance of food security. Microwave remote sensing offers a great potential for estimating SM because it is sensitive to the dielectric characteristics of observed surface that depend on surface soil moisture. The objective of this study is the evaluation of three change detection methodologies for SM estimation over wheat at the agricultural field scale based on Sentinel-1 time series: Short Term Change Detection (STCD), TU Wien Change Detection (TUWCD) and Multitemporal Bayesian Change Detection (MTBCD). Different methodological alternatives were proposed for the implementation of these techniques at the agricultural field scale. Soil moisture measurements from eight experimental wheat fields were used for validating the methodologies. All available Sentinel-1 acquisitions were processed and the eventual benefit of correcting for vegetation effects in backscatter time series was evaluated. The results were rather variable, with some experimental fields achieving successful performance metrics (ubRMSE ∼ 0.05 m3/m3) and some others rather poor ones (ubRMSE > 0.12 m3/m3). Evaluating median performance metrics, it was observed that both TUWCD and MTBCD methods obtained better results when run with vegetation corrected backscatter time series (ubRMSE ∼0.07 m3/m3) whereas STCD produced similar results with and without vegetation correction (ubRMSE ∼0.08 m3/m3). The soil moisture content had an influence on the accuracy of the different methodologies, with higher errors observed for drier conditions and rain-fed fields, in comparison to wetter conditions and irrigated fields. Taking into account the spatial scale of this case study, results were considered promising for the future application of these techniques in irrigation management. |
| first_indexed | 2024-03-12T15:31:52Z |
| format | Article |
| id | doaj.art-d34e91fbcc464bd6975f584374512c81 |
| institution | Directory Open Access Journal |
| issn | 1873-2283 |
| language | English |
| last_indexed | 2024-03-12T15:31:52Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Agricultural Water Management |
| spelling | doaj.art-d34e91fbcc464bd6975f584374512c812023-08-10T04:33:35ZengElsevierAgricultural Water Management1873-22832023-09-01287108422Evaluation of soil moisture estimation techniques based on Sentinel-1 observations over wheat fieldsMaría Arias0Claudia Notarnicola1Miguel Ángel Campo-Bescós2Luis Miguel Arregui3Jesús Álvarez-Mozos4Institute for Sustainability & Food Chain Innovation (IS-FOOD), Department of Engineering, Public University of Navarre (UPNA), Arrosadia Campus, 31006 Pamplona, SpainInstitute for Earth Observation, EURAC Research, Viale Druso, 1, 39100 Bolzano, ItalyInstitute for Sustainability & Food Chain Innovation (IS-FOOD), Department of Engineering, Public University of Navarre (UPNA), Arrosadia Campus, 31006 Pamplona, SpainInstitute for Sustainability & Food Chain Innovation (IS-FOOD), Department of Agricultural Engineering, Biotechnology and Food, Public University of Navarre (UPNA), Arrosadia Campus, 31006 Pamplona, SpainInstitute for Sustainability & Food Chain Innovation (IS-FOOD), Department of Engineering, Public University of Navarre (UPNA), Arrosadia Campus, 31006 Pamplona, Spain; Correspondence to: Public University of Navarre, Department of Engineering, Los Tejos Building, Campus de Arrosadia, 31006 Pamplona, Spain.Soil moisture (SM) is a key variable in agriculture and its monitoring is essential. SM determines the amount of water available to plants, having a direct impact on the development of crops, on the forecasting of crop yields and on the surveillance of food security. Microwave remote sensing offers a great potential for estimating SM because it is sensitive to the dielectric characteristics of observed surface that depend on surface soil moisture. The objective of this study is the evaluation of three change detection methodologies for SM estimation over wheat at the agricultural field scale based on Sentinel-1 time series: Short Term Change Detection (STCD), TU Wien Change Detection (TUWCD) and Multitemporal Bayesian Change Detection (MTBCD). Different methodological alternatives were proposed for the implementation of these techniques at the agricultural field scale. Soil moisture measurements from eight experimental wheat fields were used for validating the methodologies. All available Sentinel-1 acquisitions were processed and the eventual benefit of correcting for vegetation effects in backscatter time series was evaluated. The results were rather variable, with some experimental fields achieving successful performance metrics (ubRMSE ∼ 0.05 m3/m3) and some others rather poor ones (ubRMSE > 0.12 m3/m3). Evaluating median performance metrics, it was observed that both TUWCD and MTBCD methods obtained better results when run with vegetation corrected backscatter time series (ubRMSE ∼0.07 m3/m3) whereas STCD produced similar results with and without vegetation correction (ubRMSE ∼0.08 m3/m3). The soil moisture content had an influence on the accuracy of the different methodologies, with higher errors observed for drier conditions and rain-fed fields, in comparison to wetter conditions and irrigated fields. Taking into account the spatial scale of this case study, results were considered promising for the future application of these techniques in irrigation management.http://www.sciencedirect.com/science/article/pii/S0378377423002871Soil wetnessAgricultureSARChange detectionField scale |
| spellingShingle | María Arias Claudia Notarnicola Miguel Ángel Campo-Bescós Luis Miguel Arregui Jesús Álvarez-Mozos Evaluation of soil moisture estimation techniques based on Sentinel-1 observations over wheat fields Agricultural Water Management Soil wetness Agriculture SAR Change detection Field scale |
| title | Evaluation of soil moisture estimation techniques based on Sentinel-1 observations over wheat fields |
| title_full | Evaluation of soil moisture estimation techniques based on Sentinel-1 observations over wheat fields |
| title_fullStr | Evaluation of soil moisture estimation techniques based on Sentinel-1 observations over wheat fields |
| title_full_unstemmed | Evaluation of soil moisture estimation techniques based on Sentinel-1 observations over wheat fields |
| title_short | Evaluation of soil moisture estimation techniques based on Sentinel-1 observations over wheat fields |
| title_sort | evaluation of soil moisture estimation techniques based on sentinel 1 observations over wheat fields |
| topic | Soil wetness Agriculture SAR Change detection Field scale |
| url | http://www.sciencedirect.com/science/article/pii/S0378377423002871 |
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