Abstract
A method was developed for crop area mapping inspired by spectral matching techniques (SMTs) and based on phenological characteristics of different crop types applied using 100-m Proba-V NDVI data for the season 2014–2015. Ten-daily maximum value NDVI composites were created and smoothed in SPIRITS (spirits.jrc.ec.europa.eu). The study sites were globally spread agricultural areas located in Flanders (Belgium), Sria (Russia), Kyiv (Ukraine) and Sao Paulo (Brazil). For each pure pixel within the field, the NDVI profile of the crop type for its growing season was matched with the reference NDVI profile based on the training set extracted from the study site where the crop type originated. Three temporal windows were tested within the growing season: green-up to senescence, green-up to dormancy and minimum NDVI at the beginning of the growing season to minimum NDVI at the end of the growing season. Post classification rules were applied to the results to aggregate the crop type at the plot level. The overall accuracy (%) ranged between 65 and 86, and the kappa coefficient changed from 0.43–0.84 according to the site and the temporal window. In order of importance, the crop phenological development period, parcel size, shorter time window, number of ground-truth parcels and crop calendar similarity were the main reasons behind the differences between the results. The methodology described in this study demonstrated that 100-m Proba-V has the potential to be used in crop area mapping across different regions in the world.
Highlights
IntroductionAccurate and timely information on the cropping area and crop type obtained from remote sensing data either or not in combination with ground surveys is key for estimating crop production
Accurate and timely information on the cropping area and crop type obtained from remote sensing data either or not in combination with ground surveys is key for estimating crop production.This information has significant environmental, policy, agricultural and economic implications for most national governments, since crop production figures are used for determining the amount of food to import or export at the end of the growing season [1,2]
The objective of this study is to develop a crop mapping approach applicable at a global level inspired by the spectral matching techniques (SMTs) method [21] on a seasonal basis using 100-m Proba-V NDVI data
Summary
Accurate and timely information on the cropping area and crop type obtained from remote sensing data either or not in combination with ground surveys is key for estimating crop production. This information has significant environmental, policy, agricultural and economic implications for most national governments, since crop production figures are used for determining the amount of food to import or export at the end of the growing season [1,2]. For remote sensing-based crop production estimates, the ideal approach would be to combine biomass proxies and crop maps. Cropland maps, regardless of crop type, have proved to improve crop production forecasting [4]
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