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Africa has the largest grassland area among all grassland ecosystems in the world. As a typical agricultural and animal husbandry country in Africa, animal husbandry plays an important role in this region. The investigation of grassland resources and timely grasping the quantity and spatial distribution of grassland resources are of great significance to the stable development of local animal husbandry economy. Therefore, this paper uses Kenya as the study area to investigate the effective and fast approach for grassland mapping with 100-m resolution using the open resources in the Google Earth Engine cloud platform. The main conclusions are as follows. (1) In the feature combination optimization part of this paper, the machine learning algorithm is used to compare the scores and standard deviations of several common algorithms combined with RFE. It is concluded that the combination of RFE and random forest algorithm has the highest stability in modeling and the best feature optimization effect. (2) After feature optimization by the RFE-RF algorithm, the number of features is reduced from 12 to 8, which compressed the original feature space and reduced the redundancy of features. The optimal combination features are applied to random forest classification, and the overall accuracy and Kappa coefficient of classification are 0.87 and 0.85, respectively. The eight features are: elevation, NDVI, EVI, SWIR, RVI, BLUE, RED, and LSWI. (3) There are great differences in topographic features among the local land types in the study area, and the addition of topographic features is more conducive to the recognition and classification of various land types. There exists "salt-and-pepper phenomenon" in pixel-oriented classification. Later research focus will combine the RFE-RF algorithm and the segmentation algorithm to achieve object-oriented land cover classification. [ABSTRACT FROM AUTHOR]

Regional or continental-scale land cover mapping requires various amounts of months of multi-temporal satellite data to pick phenological variation in vegetation, enhancing differentiability among surface cover types and improving accuracy. However, little has been addressed about the number of months/multi-temporal images needed to obtain the best result and the impact of using different amounts of these data on the accuracy of individual classes. This work aimed to analyze these effects by utilizing the various amounts of months of time series FengYun-3C (FY-3C) data within one year for land cover mapping of parts of Africa using a random forest classifier. The study area covers roughly one-third of Africa, including eastern, central, and northern parts of the continent. One-year FY-3C ten-day composite images consisting of eleven-band each with 1-km spatial resolution were divided into seven input datasets that comprise stacked images of 1-month, 3-month, 6-month, consecutive 9-month, 12-month, selected images from 12 months using band/feature importance, and selected 9-month. Comparisons of these datasets on independent test samples revealed that overall accuracy, kappa coefficient, and the accuracy of the individual classes generally increase significantly with increasing the number of data/months. However, the highest accuracy and kappa coefficient, 0.86 and 0.83, were obtained by processing selected 9-month imageries. The second maximum accuracy and kappa (0.85 and 0.82,) were found by manipulating 12-month scenes which are the same as the results obtained by applying feature reduction. Although 4% and 5% higher accuracy were achieved by manipulating 3-month and 6-month data relative to 1-month imageries, no variation of accuracy was observed between six- and nine-months of consecutive data, both yielding equal accuracy and kappa value (0.84 and 0.81) indicating redundancy of information. Overall, the high accuracy results show the feasibility of FY-3C data for land cover mapping of Africa. [ABSTRACT FROM AUTHOR]