How reliable is the MODIS land cover product for crop mapping Sub-Saharan agricultural landscapes?
Accurate cropland maps at the global and local scales are crucial for scientists, government and nongovernment agencies, farmers and other stakeholders, particularly in food-insecure regions, such as Sub-Saharan Africa. In this study, we aim to qualify the crop classes of the MODIS Land Cover Product (LCP) in Sub-Saharan Africa using FAO (Food and Agricultural Organisation) and AGRHYMET (AGRiculture, Hydrology and METeorology) statistical data of agriculture and a sample of 55 very-high-resolution images. In terms of cropland acreage and dynamics, we found that the correlation between the statistical data and MODIS LCP decreases when we localize the spatial scale (from R2 = 0.86 *** at the national scale to R2 = 0.26 *** at two levels below the national scale). In terms of the cropland spatial distribution, our findings indicate a strong relationship between the user accuracy and the fragmentation of the agricultural landscape, as measured by the MODIS LCP; the accuracy decreases as the crop fraction increases. In addition, thanks to the Pareto boundary method, we were able to isolate and quantify the part of the MODIS classification error that could be directly linked to the performance of the adopted classification algorithm. Finally, based on these results, (i) a regional map of the MODIS LCP user accuracy estimates for cropland classes was produced for the entire Sub-Saharan region; this map presents a better accuracy in the western part of the region (43%-70%) compared to the eastern part (17%-43%); (ii) Theoretical user and producer accuracies for a given set of spatial resolutions were provided; the simulated future Sentinel-2 system would provide theoretical 99% user and producer accuracies given the landscape pattern of the region.
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dig-cirad-fr-5739192024-01-28T22:12:06Z http://agritrop.cirad.fr/573919/ http://agritrop.cirad.fr/573919/ How reliable is the MODIS land cover product for crop mapping Sub-Saharan agricultural landscapes? Leroux Louise, Jolivot Audrey, Bégué Agnès, Lo Seen Danny, Zoungrana Bernardin. 2014. Remote Sensing, 6 (9) : 8541-8564.https://doi.org/10.3390/rs6098541 <https://doi.org/10.3390/rs6098541> How reliable is the MODIS land cover product for crop mapping Sub-Saharan agricultural landscapes? Leroux, Louise Jolivot, Audrey Bégué, Agnès Lo Seen, Danny Zoungrana, Bernardin eng 2014 Remote Sensing U30 - Méthodes de recherche U10 - Informatique, mathématiques et statistiques modèle télédétection cartographie couverture du sol terre cultivée classification statistiques agricoles couverture végétale analyse d'image paysage conditions météorologiques mesure (activité) http://aims.fao.org/aos/agrovoc/c_4881 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_1344 http://aims.fao.org/aos/agrovoc/c_37897 http://aims.fao.org/aos/agrovoc/c_16212 http://aims.fao.org/aos/agrovoc/c_1653 http://aims.fao.org/aos/agrovoc/c_49977 http://aims.fao.org/aos/agrovoc/c_25409 http://aims.fao.org/aos/agrovoc/c_36762 http://aims.fao.org/aos/agrovoc/c_4185 http://aims.fao.org/aos/agrovoc/c_29565 http://aims.fao.org/aos/agrovoc/c_4668 Afrique au sud du Sahara http://aims.fao.org/aos/agrovoc/c_166 Accurate cropland maps at the global and local scales are crucial for scientists, government and nongovernment agencies, farmers and other stakeholders, particularly in food-insecure regions, such as Sub-Saharan Africa. In this study, we aim to qualify the crop classes of the MODIS Land Cover Product (LCP) in Sub-Saharan Africa using FAO (Food and Agricultural Organisation) and AGRHYMET (AGRiculture, Hydrology and METeorology) statistical data of agriculture and a sample of 55 very-high-resolution images. In terms of cropland acreage and dynamics, we found that the correlation between the statistical data and MODIS LCP decreases when we localize the spatial scale (from R2 = 0.86 *** at the national scale to R2 = 0.26 *** at two levels below the national scale). In terms of the cropland spatial distribution, our findings indicate a strong relationship between the user accuracy and the fragmentation of the agricultural landscape, as measured by the MODIS LCP; the accuracy decreases as the crop fraction increases. In addition, thanks to the Pareto boundary method, we were able to isolate and quantify the part of the MODIS classification error that could be directly linked to the performance of the adopted classification algorithm. Finally, based on these results, (i) a regional map of the MODIS LCP user accuracy estimates for cropland classes was produced for the entire Sub-Saharan region; this map presents a better accuracy in the western part of the region (43%-70%) compared to the eastern part (17%-43%); (ii) Theoretical user and producer accuracies for a given set of spatial resolutions were provided; the simulated future Sentinel-2 system would provide theoretical 99% user and producer accuracies given the landscape pattern of the region. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/573919/1/document_573919.pdf application/pdf Cirad license info:eu-repo/semantics/openAccess https://agritrop.cirad.fr/mention_legale.html https://doi.org/10.3390/rs6098541 10.3390/rs6098541 info:eu-repo/semantics/altIdentifier/doi/10.3390/rs6098541 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.3390/rs6098541 |
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U30 - Méthodes de recherche U10 - Informatique, mathématiques et statistiques modèle télédétection cartographie couverture du sol terre cultivée classification statistiques agricoles couverture végétale analyse d'image paysage conditions météorologiques mesure (activité) http://aims.fao.org/aos/agrovoc/c_4881 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_1344 http://aims.fao.org/aos/agrovoc/c_37897 http://aims.fao.org/aos/agrovoc/c_16212 http://aims.fao.org/aos/agrovoc/c_1653 http://aims.fao.org/aos/agrovoc/c_49977 http://aims.fao.org/aos/agrovoc/c_25409 http://aims.fao.org/aos/agrovoc/c_36762 http://aims.fao.org/aos/agrovoc/c_4185 http://aims.fao.org/aos/agrovoc/c_29565 http://aims.fao.org/aos/agrovoc/c_4668 http://aims.fao.org/aos/agrovoc/c_166 U30 - Méthodes de recherche U10 - Informatique, mathématiques et statistiques modèle télédétection cartographie couverture du sol terre cultivée classification statistiques agricoles couverture végétale analyse d'image paysage conditions météorologiques mesure (activité) http://aims.fao.org/aos/agrovoc/c_4881 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_1344 http://aims.fao.org/aos/agrovoc/c_37897 http://aims.fao.org/aos/agrovoc/c_16212 http://aims.fao.org/aos/agrovoc/c_1653 http://aims.fao.org/aos/agrovoc/c_49977 http://aims.fao.org/aos/agrovoc/c_25409 http://aims.fao.org/aos/agrovoc/c_36762 http://aims.fao.org/aos/agrovoc/c_4185 http://aims.fao.org/aos/agrovoc/c_29565 http://aims.fao.org/aos/agrovoc/c_4668 http://aims.fao.org/aos/agrovoc/c_166 |
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U30 - Méthodes de recherche U10 - Informatique, mathématiques et statistiques modèle télédétection cartographie couverture du sol terre cultivée classification statistiques agricoles couverture végétale analyse d'image paysage conditions météorologiques mesure (activité) http://aims.fao.org/aos/agrovoc/c_4881 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_1344 http://aims.fao.org/aos/agrovoc/c_37897 http://aims.fao.org/aos/agrovoc/c_16212 http://aims.fao.org/aos/agrovoc/c_1653 http://aims.fao.org/aos/agrovoc/c_49977 http://aims.fao.org/aos/agrovoc/c_25409 http://aims.fao.org/aos/agrovoc/c_36762 http://aims.fao.org/aos/agrovoc/c_4185 http://aims.fao.org/aos/agrovoc/c_29565 http://aims.fao.org/aos/agrovoc/c_4668 http://aims.fao.org/aos/agrovoc/c_166 U30 - Méthodes de recherche U10 - Informatique, mathématiques et statistiques modèle télédétection cartographie couverture du sol terre cultivée classification statistiques agricoles couverture végétale analyse d'image paysage conditions météorologiques mesure (activité) http://aims.fao.org/aos/agrovoc/c_4881 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_1344 http://aims.fao.org/aos/agrovoc/c_37897 http://aims.fao.org/aos/agrovoc/c_16212 http://aims.fao.org/aos/agrovoc/c_1653 http://aims.fao.org/aos/agrovoc/c_49977 http://aims.fao.org/aos/agrovoc/c_25409 http://aims.fao.org/aos/agrovoc/c_36762 http://aims.fao.org/aos/agrovoc/c_4185 http://aims.fao.org/aos/agrovoc/c_29565 http://aims.fao.org/aos/agrovoc/c_4668 http://aims.fao.org/aos/agrovoc/c_166 Leroux, Louise Jolivot, Audrey Bégué, Agnès Lo Seen, Danny Zoungrana, Bernardin How reliable is the MODIS land cover product for crop mapping Sub-Saharan agricultural landscapes? |
description |
Accurate cropland maps at the global and local scales are crucial for scientists, government and nongovernment agencies, farmers and other stakeholders, particularly in food-insecure regions, such as Sub-Saharan Africa. In this study, we aim to qualify the crop classes of the MODIS Land Cover Product (LCP) in Sub-Saharan Africa using FAO (Food and Agricultural Organisation) and AGRHYMET (AGRiculture, Hydrology and METeorology) statistical data of agriculture and a sample of 55 very-high-resolution images. In terms of cropland acreage and dynamics, we found that the correlation between the statistical data and MODIS LCP decreases when we localize the spatial scale (from R2 = 0.86 *** at the national scale to R2 = 0.26 *** at two levels below the national scale). In terms of the cropland spatial distribution, our findings indicate a strong relationship between the user accuracy and the fragmentation of the agricultural landscape, as measured by the MODIS LCP; the accuracy decreases as the crop fraction increases. In addition, thanks to the Pareto boundary method, we were able to isolate and quantify the part of the MODIS classification error that could be directly linked to the performance of the adopted classification algorithm. Finally, based on these results, (i) a regional map of the MODIS LCP user accuracy estimates for cropland classes was produced for the entire Sub-Saharan region; this map presents a better accuracy in the western part of the region (43%-70%) compared to the eastern part (17%-43%); (ii) Theoretical user and producer accuracies for a given set of spatial resolutions were provided; the simulated future Sentinel-2 system would provide theoretical 99% user and producer accuracies given the landscape pattern of the region. |
format |
article |
topic_facet |
U30 - Méthodes de recherche U10 - Informatique, mathématiques et statistiques modèle télédétection cartographie couverture du sol terre cultivée classification statistiques agricoles couverture végétale analyse d'image paysage conditions météorologiques mesure (activité) http://aims.fao.org/aos/agrovoc/c_4881 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_1344 http://aims.fao.org/aos/agrovoc/c_37897 http://aims.fao.org/aos/agrovoc/c_16212 http://aims.fao.org/aos/agrovoc/c_1653 http://aims.fao.org/aos/agrovoc/c_49977 http://aims.fao.org/aos/agrovoc/c_25409 http://aims.fao.org/aos/agrovoc/c_36762 http://aims.fao.org/aos/agrovoc/c_4185 http://aims.fao.org/aos/agrovoc/c_29565 http://aims.fao.org/aos/agrovoc/c_4668 http://aims.fao.org/aos/agrovoc/c_166 |
author |
Leroux, Louise Jolivot, Audrey Bégué, Agnès Lo Seen, Danny Zoungrana, Bernardin |
author_facet |
Leroux, Louise Jolivot, Audrey Bégué, Agnès Lo Seen, Danny Zoungrana, Bernardin |
author_sort |
Leroux, Louise |
title |
How reliable is the MODIS land cover product for crop mapping Sub-Saharan agricultural landscapes? |
title_short |
How reliable is the MODIS land cover product for crop mapping Sub-Saharan agricultural landscapes? |
title_full |
How reliable is the MODIS land cover product for crop mapping Sub-Saharan agricultural landscapes? |
title_fullStr |
How reliable is the MODIS land cover product for crop mapping Sub-Saharan agricultural landscapes? |
title_full_unstemmed |
How reliable is the MODIS land cover product for crop mapping Sub-Saharan agricultural landscapes? |
title_sort |
how reliable is the modis land cover product for crop mapping sub-saharan agricultural landscapes? |
url |
http://agritrop.cirad.fr/573919/ http://agritrop.cirad.fr/573919/1/document_573919.pdf |
work_keys_str_mv |
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1792498683586019328 |