Do Agrometeorological Data Improve Optical Satellite-Based Estimations of the Herbaceous Yield in Sahelian Semi-Arid Ecosystems?
Quantitative estimates of forage availability at the end of the growing season in rangelands are helpful for pastoral livestock managers and for local, national and regional stakeholders in natural resource management. For this reason, remote sensing data such as the Fraction of Absorbed Photosynthe...
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-08-01
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| Series: | Remote Sensing |
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| Online Access: | http://www.mdpi.com/2072-4292/8/8/668 |
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| author | Abdoul Aziz Diouf Pierre Hiernaux Martin Brandt Gayane Faye Bakary Djaby Mouhamadou Bamba Diop Jacques André Ndione Bernard Tychon |
| author_facet | Abdoul Aziz Diouf Pierre Hiernaux Martin Brandt Gayane Faye Bakary Djaby Mouhamadou Bamba Diop Jacques André Ndione Bernard Tychon |
| author_sort | Abdoul Aziz Diouf |
| collection | DOAJ |
| description | Quantitative estimates of forage availability at the end of the growing season in rangelands are helpful for pastoral livestock managers and for local, national and regional stakeholders in natural resource management. For this reason, remote sensing data such as the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) have been widely used to assess Sahelian plant productivity for about 40 years. This study combines traditional FAPAR-based assessments with agrometeorological variables computed by the geospatial water balance program, GeoWRSI, using rainfall and potential evapotranspiration satellite gridded data to estimate the annual herbaceous yield in the semi-arid areas of Senegal. It showed that a machine-learning model combining FAPAR seasonal metrics with various agrometeorological data provided better estimations of the in situ annual herbaceous yield (R2 = 0.69; RMSE = 483 kg·DM/ha) than models based exclusively on FAPAR metrics (R2 = 0.63; RMSE = 550 kg·DM/ha) or agrometeorological variables (R2 = 0.55; RMSE = 585 kg·DM/ha). All the models provided reasonable outputs and showed a decrease in the mean annual yield with increasing latitude, together with an increase in relative inter-annual variation. In particular, the additional use of agrometeorological information mitigated the saturation effects that characterize the plant indices of areas with high plant productivity. In addition, the date of the onset of the growing season derived from smoothed FAPAR seasonal dynamics showed no significant relationship (0.05 p-level) with the annual herbaceous yield across the whole studied area. The date of the onset of rainfall however, was significantly related to the herbaceous yield and its inclusion in fodder biomass models could constitute a significant improvement in forecasting risks of a mass herbaceous deficit at an early stage of the year. |
| first_indexed | 2024-12-13T10:49:59Z |
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| id | doaj.art-c43d417f599b47d9a72b69207920a5d3 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-13T10:49:59Z |
| publishDate | 2016-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-c43d417f599b47d9a72b69207920a5d32022-12-21T23:49:53ZengMDPI AGRemote Sensing2072-42922016-08-018866810.3390/rs8080668rs8080668Do Agrometeorological Data Improve Optical Satellite-Based Estimations of the Herbaceous Yield in Sahelian Semi-Arid Ecosystems?Abdoul Aziz Diouf0Pierre Hiernaux1Martin Brandt2Gayane Faye3Bakary Djaby4Mouhamadou Bamba Diop5Jacques André Ndione6Bernard Tychon7Centre de Suivi Ecologique, Rue Aimé Césaire x Léon Gontran Damas, BP 15532 Fann-Dakar, SenegalGéosciences Environnement Toulouse (GET), Observatoire Midi-Pyrénées, UMR 5563 (CNRS/UPS/IRD/CNES), 14 Avenue Edouard Belin, 31400 Toulouse, FranceDepartment of Geosciences and Natural Resource Management, University of Copenhagen, 1350 Copenhagen, DenmarkCentre de Suivi Ecologique, Rue Aimé Césaire x Léon Gontran Damas, BP 15532 Fann-Dakar, SenegalCentre Regional AGRHYMET, BP 11011 Niamey, NigerCentre de Suivi Ecologique, Rue Aimé Césaire x Léon Gontran Damas, BP 15532 Fann-Dakar, SenegalCentre de Suivi Ecologique, Rue Aimé Césaire x Léon Gontran Damas, BP 15532 Fann-Dakar, SenegalWater, Environment and Development Unit, University of Liège, Avenue de Longwy B6700, 6700 Arlon, BelgiumQuantitative estimates of forage availability at the end of the growing season in rangelands are helpful for pastoral livestock managers and for local, national and regional stakeholders in natural resource management. For this reason, remote sensing data such as the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) have been widely used to assess Sahelian plant productivity for about 40 years. This study combines traditional FAPAR-based assessments with agrometeorological variables computed by the geospatial water balance program, GeoWRSI, using rainfall and potential evapotranspiration satellite gridded data to estimate the annual herbaceous yield in the semi-arid areas of Senegal. It showed that a machine-learning model combining FAPAR seasonal metrics with various agrometeorological data provided better estimations of the in situ annual herbaceous yield (R2 = 0.69; RMSE = 483 kg·DM/ha) than models based exclusively on FAPAR metrics (R2 = 0.63; RMSE = 550 kg·DM/ha) or agrometeorological variables (R2 = 0.55; RMSE = 585 kg·DM/ha). All the models provided reasonable outputs and showed a decrease in the mean annual yield with increasing latitude, together with an increase in relative inter-annual variation. In particular, the additional use of agrometeorological information mitigated the saturation effects that characterize the plant indices of areas with high plant productivity. In addition, the date of the onset of the growing season derived from smoothed FAPAR seasonal dynamics showed no significant relationship (0.05 p-level) with the annual herbaceous yield across the whole studied area. The date of the onset of rainfall however, was significantly related to the herbaceous yield and its inclusion in fodder biomass models could constitute a significant improvement in forecasting risks of a mass herbaceous deficit at an early stage of the year.http://www.mdpi.com/2072-4292/8/8/668herbaceous annual yieldFAPARstart of seasongrasslandsGeoWRSIsatellite remote sensingCubistland cover classSahelSenegal |
| spellingShingle | Abdoul Aziz Diouf Pierre Hiernaux Martin Brandt Gayane Faye Bakary Djaby Mouhamadou Bamba Diop Jacques André Ndione Bernard Tychon Do Agrometeorological Data Improve Optical Satellite-Based Estimations of the Herbaceous Yield in Sahelian Semi-Arid Ecosystems? Remote Sensing herbaceous annual yield FAPAR start of season grasslands GeoWRSI satellite remote sensing Cubist land cover class Sahel Senegal |
| title | Do Agrometeorological Data Improve Optical Satellite-Based Estimations of the Herbaceous Yield in Sahelian Semi-Arid Ecosystems? |
| title_full | Do Agrometeorological Data Improve Optical Satellite-Based Estimations of the Herbaceous Yield in Sahelian Semi-Arid Ecosystems? |
| title_fullStr | Do Agrometeorological Data Improve Optical Satellite-Based Estimations of the Herbaceous Yield in Sahelian Semi-Arid Ecosystems? |
| title_full_unstemmed | Do Agrometeorological Data Improve Optical Satellite-Based Estimations of the Herbaceous Yield in Sahelian Semi-Arid Ecosystems? |
| title_short | Do Agrometeorological Data Improve Optical Satellite-Based Estimations of the Herbaceous Yield in Sahelian Semi-Arid Ecosystems? |
| title_sort | do agrometeorological data improve optical satellite based estimations of the herbaceous yield in sahelian semi arid ecosystems |
| topic | herbaceous annual yield FAPAR start of season grasslands GeoWRSI satellite remote sensing Cubist land cover class Sahel Senegal |
| url | http://www.mdpi.com/2072-4292/8/8/668 |
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