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

Abstract

Abstract: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 R 2 = 0.86 *** at the national scale to R 2 = 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 OPEN ACCESS Remote Sens. 2014, 6 8542 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.

Multiscale Remote Sensing to Map the Spatial Distribution and Extent of Cropland in the Sudanian Savanna of West Africa

“…However, for the purposes of comparison, a constant value of 0.5 (i.e., 50%) was used. This is in line with the assumptions made by previous studies (e.g., [3,10]). …”

“…Compared to their estimates, errors in the MODIS_FCM can be considered to be consistent due to the aforementioned relatively small variation in MAE, although the number of validation sites used could have influenced the degree of variation. In terms of overall accuracy, the value obtained in this study represents a significant improvement over the reported 51% for the MODIS_LCP [3], which has been found to have a better accuracy than most global LULC products [10].…”

“…2017, 9, 839 13 of 24 over the reported 51% for the MODIS_LCP [3], which has been found to have a better accuracy than most global LULC products [10].…”

“…The variability in accuracy observed across the study area has been reported by other studies that assessed other global products. For example, in assessing the accuracy of an improved global LULC map, Leroux et al [3] reported variability in accuracy across 55 validation sites where high resolution images were classified into crop and non-crop areas through photo interpretation. For West Africa, they found a considerable variation in accuracy of between 47% and 70%.…”

“…However, the success of agricultural production depends on sustainability [2] which may be achieved through thorough analysis of the situation and development of cropland and its reactions to environmental conditions and variability as well as human interactions. Such analyses require accurate and regularly updated information on the spatial distribution and extent of cropland [3,4]. In West Africa, cropland information is commonly generated by agricultural ministries at national and sub-national scales through agricultural censuses.…”