Mapping Crop Cycles in China Using MODIS-EVI Time Series

Abstract

Abstract:As the Earth's population continues to grow and demand for food increases, the need for improved and timely information related to the properties and dynamics of global agricultural systems is becoming increasingly important. Global land cover maps derived from satellite data provide indispensable information regarding the geographic distribution and areal extent of global croplands. However, land use information, such as cropping intensity (defined here as the number of cropping cycles per year), is not routinely available over large areas because mapping this information from remote sensing is challenging. In this study, we present a simple but efficient algorithm for automated mapping of cropping intensity based on data from NASA's (NASA: The National Aeronautics and Space Administration) MODerate Resolution Imaging Spectroradiometer (MODIS). The proposed algorithm first applies an adaptive Savitzky-Golay filter to smooth Enhanced Vegetation Index (EVI) time series derived from MODIS surface reflectance data. It then uses an iterative moving-window methodology to identify cropping cycles from the smoothed EVI time series. Comparison of results from our algorithm with OPEN ACCESSRemote Sens. 2014, 6 2474 national survey data at both the provincial and prefectural level in China show that the algorithm provides estimates of gross sown area that agree well with inventory data. Accuracy assessment comparing visually interpreted time series with algorithm results for a random sample of agricultural areas in China indicates an overall accuracy of 91.0% for three classes defined based on the number of cycles observed in EVI time series. The algorithm therefore appears to provide a straightforward and efficient method for mapping cropping intensity from MODIS time series data.

Mapping Paddy Rice in China in 2002, 2005, 2010 and 2014 with MODIS Time Series

“…Our results show that the overall paddy rice area remained largely stable between 2002 and 2014, which is in line with previous studies [23,31,34,53] and the agricultural census data published by the National Bureau of Statistics of China. Our maps are able to show detailed shifts in rice area on the local to county scale, which could not be deduced from the publicly-available agricultural statistics.…”

“…In provinces with favorable climate multi-cropping, the growing of multiple crops in succession throughout one year is practiced. The cropping intensity gradually increases from one crop in the northern provinces to up to three crops per year in the southern provinces [53], with the boundaries closely resembling China's different climate zones. North of the Yangtze River and in the westernmost provinces, only one season of rice per year is cultivated.…”

Standardized Time-Series and Interannual Phenological Deviation: New Techniques for Burned-Area Detection Using Long-Term MODIS-NBR Dataset

“…Thus, Chen et al [71] propose a method based on the S-G filter to smooth the effects of cloud contamination and atmospheric variability on the NDVI time-series. Based upon this study, different researches show the successful application of the S-G filter in the MODIS vegetation indices, by minimizing overall noise and preserving higher vegetation-index values (e.g., [69,71,72]). Geng et al [67] compare eight techniques for smoothing multi-temporal NDVI data, considering different vegetation types and sensors, and conclude that the S-G filter achieve best results in most situations.…”

Phenological Observations on Classical Prehistoric Sites in the Middle and Lower Reaches of the Yellow River Based on Landsat NDVI Time Series

“…The NDVI data has been smoothed using the Savitzky-Golay filter to reduce noise. Previous research shows that the results can provide a better fit, particularly in pixels where multicropping was present [26]. Winter wheat and summer maize both have fibrous root systems, and the root depth for winter wheat is generally 200 cm around loess regions [27] while the maximum root depth for summer maize is 230 cm [28].…”