Next Article in Journal
Land Cover Characterization and Classification of Arctic Tundra Environments by Means of Polarized Synthetic Aperture X- and C-Band Radar (PolSAR) and Landsat 8 Multispectral Imagery — Richards Island, Canada
Next Article in Special Issue
Defining the Spatial Resolution Requirements for Crop Identification Using Optical Remote Sensing
Previous Article in Journal
Evaluation of Satellite Retrievals of Ocean Chlorophyll-a in the California Current
Previous Article in Special Issue
Investigating the Relationship between the Inter-Annual Variability of Satellite-Derived Vegetation Phenology and a Proxy of Biomass Production in the Sahel
Article

How Reliable is the MODIS Land Cover Product for Crop Mapping Sub-Saharan Agricultural Landscapes?

1
CIRAD—UMR TETIS (Centre de Coopération International en Recherche Agronomique pour le Développement), 500 rue JF Breton, 34093 Montpellier, France
2
AGRHYMET (AGRiculture, Hydrology and METeorology), Centre Régional Agrhymet, BP 11011 Niamey, Niger
*
Author to whom correspondence should be addressed.
Remote Sens. 2014, 6(9), 8541-8564; https://doi.org/10.3390/rs6098541
Received: 25 June 2014 / Revised: 27 August 2014 / Accepted: 4 September 2014 / Published: 11 September 2014
(This article belongs to the Special Issue Remote Sensing in Food Production and Food Security)
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. View Full-Text
Keywords: MODIS land cover; agricultural statistics; cropland; Africa; classification accuracy; landscape metrics MODIS land cover; agricultural statistics; cropland; Africa; classification accuracy; landscape metrics
Show Figures

Graphical abstract

MDPI and ACS Style

Leroux, L.; Jolivot, A.; Bégué, A.; Seen, D.L.; Zoungrana, B. How Reliable is the MODIS Land Cover Product for Crop Mapping Sub-Saharan Agricultural Landscapes? Remote Sens. 2014, 6, 8541-8564. https://doi.org/10.3390/rs6098541

AMA Style

Leroux L, Jolivot A, Bégué A, Seen DL, Zoungrana B. How Reliable is the MODIS Land Cover Product for Crop Mapping Sub-Saharan Agricultural Landscapes? Remote Sensing. 2014; 6(9):8541-8564. https://doi.org/10.3390/rs6098541

Chicago/Turabian Style

Leroux, Louise, Audrey Jolivot, Agnès Bégué, Danny L. Seen, and Bernardin Zoungrana. 2014. "How Reliable is the MODIS Land Cover Product for Crop Mapping Sub-Saharan Agricultural Landscapes?" Remote Sensing 6, no. 9: 8541-8564. https://doi.org/10.3390/rs6098541

Find Other Styles

Article Access Map by Country/Region

1
Only visits after 24 November 2015 are recorded.
Back to TopTop