Combining High-Resolution Remote Sensing Products with a Crop Model to Estimate Carbon and Water Budget Components: Application to Sunflower
The global increase in food demand in the context of climate change requires a clear understanding of cropland function and of its impact on biogeochemical cycles. However, although gas exchange between croplands and the atmosphere is measurable in the field, it is difficult to quantify at the plot...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-09-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/18/2967 |
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| author | Gaétan Pique Rémy Fieuzal Philippe Debaeke Ahmad Al Bitar Tiphaine Tallec Eric Ceschia |
| author_facet | Gaétan Pique Rémy Fieuzal Philippe Debaeke Ahmad Al Bitar Tiphaine Tallec Eric Ceschia |
| author_sort | Gaétan Pique |
| collection | DOAJ |
| description | The global increase in food demand in the context of climate change requires a clear understanding of cropland function and of its impact on biogeochemical cycles. However, although gas exchange between croplands and the atmosphere is measurable in the field, it is difficult to quantify at the plot scale over relatively large areas because of the heterogeneous character of landscapes and differences in crop management. However, assessing accurate carbon and water budgets over croplands is essential to promote sustainable agronomic practices and reduce the water demand and the climatic impacts of croplands while maintaining sufficient yields. From this perspective, we developed a crop model, SAFYE-CO<sub>2</sub>, that assimilates high spatial- and temporal-resolution (HSTR) remote sensing products to estimate daily crop biomass, water and CO<sub>2</sub> fluxes, annual yields, and carbon budgets at the parcel level over large areas. This modeling approach was evaluated for sunflower against two in situ datasets. First, the model’s output was compared to data acquired during two cropping seasons at the Auradé integrated carbon observation system (ICOS) instrumented site in southwestern France. The model accurately simulated the daily net CO<sub>2</sub> flux (root mean square error (RMSE) = 0.97 gC·m<sup>−2</sup>·d<sup>−1</sup> and determination coefficient (R<sup>2</sup>) = 0.83) and water flux (RMSE = 0.68 mm·d<sup>−1</sup> and R<sup>2</sup> = 0.79). The model’s performance was then evaluated against biomass and yield data collected from 80 plots located in southwestern France. The model was able to satisfactorily estimate biomass dynamics and yield (RMSE = 66 and 54 g·m<sup>−2</sup>, respectively). To investigate the potential application of the proposed approach at a large scale, given that soil properties are important factors affecting the model, a sensitivity analysis of two existing soil products (GlobalSoilMap and SoilGrids) was carried out. Our results show that these products are not sufficiently accurate for inclusion as inputs to the model, which requires more accurate information on soil water retention capacity to assess water fluxes. Additionally, we argue that no water stress should be considered in the crop growth computation since this stress is already present because of remote sensing information in the proposed approach. This study should be considered a first step to fulfill the existing gap in quantifying carbon budgets at the plot scale over large areas and to accurately estimate the effects of management practices, such as the use of cover crops or specific crop rotations on cropland C and water budgets. |
| first_indexed | 2024-03-10T16:23:10Z |
| format | Article |
| id | doaj.art-0e8a59a1b6504faf81cc01c4328d4348 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T16:23:10Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-0e8a59a1b6504faf81cc01c4328d43482023-11-20T13:28:22ZengMDPI AGRemote Sensing2072-42922020-09-011218296710.3390/rs12182967Combining High-Resolution Remote Sensing Products with a Crop Model to Estimate Carbon and Water Budget Components: Application to SunflowerGaétan Pique0Rémy Fieuzal1Philippe Debaeke2Ahmad Al Bitar3Tiphaine Tallec4Eric Ceschia5CESBIO, Université de Toulouse, CNES/CNRS/INRAE/IRD/UPS, 31400 Toulouse, FranceCESBIO, Université de Toulouse, CNES/CNRS/INRAE/IRD/UPS, 31400 Toulouse, FranceINRAE, UMR 1248 AGIR, 31326 Castanet-Tolosan, FranceCESBIO, Université de Toulouse, CNES/CNRS/INRAE/IRD/UPS, 31400 Toulouse, FranceCESBIO, Université de Toulouse, CNES/CNRS/INRAE/IRD/UPS, 31400 Toulouse, FranceCESBIO, Université de Toulouse, CNES/CNRS/INRAE/IRD/UPS, 31400 Toulouse, FranceThe global increase in food demand in the context of climate change requires a clear understanding of cropland function and of its impact on biogeochemical cycles. However, although gas exchange between croplands and the atmosphere is measurable in the field, it is difficult to quantify at the plot scale over relatively large areas because of the heterogeneous character of landscapes and differences in crop management. However, assessing accurate carbon and water budgets over croplands is essential to promote sustainable agronomic practices and reduce the water demand and the climatic impacts of croplands while maintaining sufficient yields. From this perspective, we developed a crop model, SAFYE-CO<sub>2</sub>, that assimilates high spatial- and temporal-resolution (HSTR) remote sensing products to estimate daily crop biomass, water and CO<sub>2</sub> fluxes, annual yields, and carbon budgets at the parcel level over large areas. This modeling approach was evaluated for sunflower against two in situ datasets. First, the model’s output was compared to data acquired during two cropping seasons at the Auradé integrated carbon observation system (ICOS) instrumented site in southwestern France. The model accurately simulated the daily net CO<sub>2</sub> flux (root mean square error (RMSE) = 0.97 gC·m<sup>−2</sup>·d<sup>−1</sup> and determination coefficient (R<sup>2</sup>) = 0.83) and water flux (RMSE = 0.68 mm·d<sup>−1</sup> and R<sup>2</sup> = 0.79). The model’s performance was then evaluated against biomass and yield data collected from 80 plots located in southwestern France. The model was able to satisfactorily estimate biomass dynamics and yield (RMSE = 66 and 54 g·m<sup>−2</sup>, respectively). To investigate the potential application of the proposed approach at a large scale, given that soil properties are important factors affecting the model, a sensitivity analysis of two existing soil products (GlobalSoilMap and SoilGrids) was carried out. Our results show that these products are not sufficiently accurate for inclusion as inputs to the model, which requires more accurate information on soil water retention capacity to assess water fluxes. Additionally, we argue that no water stress should be considered in the crop growth computation since this stress is already present because of remote sensing information in the proposed approach. This study should be considered a first step to fulfill the existing gap in quantifying carbon budgets at the plot scale over large areas and to accurately estimate the effects of management practices, such as the use of cover crops or specific crop rotations on cropland C and water budgets.https://www.mdpi.com/2072-4292/12/18/2967crop modelingcarbon budgetwater budgetremote sensingsoil properties |
| spellingShingle | Gaétan Pique Rémy Fieuzal Philippe Debaeke Ahmad Al Bitar Tiphaine Tallec Eric Ceschia Combining High-Resolution Remote Sensing Products with a Crop Model to Estimate Carbon and Water Budget Components: Application to Sunflower Remote Sensing crop modeling carbon budget water budget remote sensing soil properties |
| title | Combining High-Resolution Remote Sensing Products with a Crop Model to Estimate Carbon and Water Budget Components: Application to Sunflower |
| title_full | Combining High-Resolution Remote Sensing Products with a Crop Model to Estimate Carbon and Water Budget Components: Application to Sunflower |
| title_fullStr | Combining High-Resolution Remote Sensing Products with a Crop Model to Estimate Carbon and Water Budget Components: Application to Sunflower |
| title_full_unstemmed | Combining High-Resolution Remote Sensing Products with a Crop Model to Estimate Carbon and Water Budget Components: Application to Sunflower |
| title_short | Combining High-Resolution Remote Sensing Products with a Crop Model to Estimate Carbon and Water Budget Components: Application to Sunflower |
| title_sort | combining high resolution remote sensing products with a crop model to estimate carbon and water budget components application to sunflower |
| topic | crop modeling carbon budget water budget remote sensing soil properties |
| url | https://www.mdpi.com/2072-4292/12/18/2967 |
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