Abstract
Timely and efficient land-cover mapping is of high interest, especially in agricultural landscapes. Classification based on satellite images over the season, while important for cropland monitoring, remains challenging in subtropical agricultural areas due to the high diversity of management systems and seasonal cloud cover variations. This work presents supervised object-based classifications over the year at 2-month time-steps in a heterogeneous region of 12,000 km2 in the Sao Paulo region of Brazil. Different methods and remote-sensing datasets were tested with the random forest algorithm, including optical and radar data, time series of images, and cloud gap-filling methods. The final selected method demonstrated an overall accuracy of approximately 0.84, which was stable throughout the year, at the more detailed level of classification; confusion mainly occurred among annual crop classes and soil classes. We showed in this study that the use of time series was useful in this context, mainly by including a small number of highly discriminant images. Such important images were eventually distant in time from the prediction date, and they corresponded to a high-quality image with low cloud cover. Consequently, the final classification accuracy was not sensitive to the cloud gap-filling method, and simple median gap-filling or linear interpolations with time were sufficient. Sentinel-1 images did not improve the classification results in this context. For within-season dynamic classes, such as annual crops, which were more difficult to classify, field measurement efforts should be densified and planned during the most discriminant window, which may not occur during the crop vegetation peak.
Highlights
Due to increases in the world population and changes in consumption habits, the current level of global food production needs to be doubled by 2050 [1,2,3] in addition to improvements in the distribution system, storage, price, and food access [1]
The advantage of using T5 was clearer in terms of overall accuracy (OA) for the first date, and T5 was, chosen as the optimal one
These results indicate that the classifier could be applied at larger scale or could be recalibrated and applied in other regions; this generalization test should be properly undertaken [90]
Summary
Due to increases in the world population and changes in consumption habits, the current level of global food production needs to be doubled by 2050 [1,2,3] in addition to improvements in the distribution system, storage, price, and food access [1]. Public and private sectors need timely and reliable agricultural information for assertive decision making that can ensure supply, generation of investments, cost reductions, and creation of agricultural policies. Because this information is important for the economy of such countries as Brazil, agricultural land-use mapping is essential for environmental and agricultural monitoring [6,7,8].
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