Land Cover and Crop Type Classification along the Season Based on Biophysical Variables Retrieved from Multi-Sensor High-Resolution Time Series

Abstract

Abstract:With the ever-increasing number of satellites and the availability of data free of charge, the integration of multi-sensor images in coherent time series offers new opportunities for land cover and crop type classification. This article investigates the potential of structural biophysical variables as common parameters to consistently combine multi-sensor time series and to exploit them for land/crop cover classification. Artificial neural networks were trained based on a radiative transfer model in order to retrieve high resolution LAI, FAPAR and FCOVER from Landsat-8 and SPOT-4. The correlation coefficients between field measurements and the retrieved biophysical variables were 0.83, 0.85 and 0.79 for LAI, FAPAR and FCOVER, respectively. The retrieved biophysical variables' time series displayed consistent average temporal trajectories, even though the class variability and signal-to-noise ratio increased compared to NDVI. Six random forest classifiers were trained and applied along the season with different inputs: spectral bands, NDVI, as well as FAPAR, LAI and FCOVER, separately Remote Sens. 2015, 7 10401 and jointly. Classifications with structural biophysical variables reached end-of-season overall accuracies ranging from 73%-76% when used alone and 77% when used jointly. This corresponds to 90% and 95% of the accuracy level achieved with the spectral bands and NDVI. FCOVER appears to be the most promising biophysical variable for classification. When assuming that the cropland extent is known, crop type classification reaches 89% with spectral information, 87% with the NDVI and 81%-84% with biophysical variables.

First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe

“…At this time many of the crops are in an advanced growth stage or already in senescence ( Figure 5). Better results can be expected by either choosing a more suitable date for image acquisition or by using multi-temporal data [53][54][55][56][57]. Nevertheless, even using a single (and not perfectly timed) image, the results are already comparable to the outcome of the study presented in Inglada et al [16].…”

“…Also for forest classifications, images acquired earlier (end of spring) or later (beginning of autumn) in the year would probably lead to higher classification accuracies. For all land cover types, we expect higher classification accuracies with better timing of the acquisitions and in particular by using multi-temporal data [57,[73][74][75].…”

Cropland Mapping over Sahelian and Sudanian Agrosystems: A Knowledge-Based Approach Using PROBA-V Time Series at 100-m

“…The features were surface reflectance composited at specific events of the growing season when crops are expected to behave differently than other land covers do. Among the cropland discrimination studies [46][47][48][49], five temporal features were selected: the maximum of the red band (max red), the minimum and maximum of the Normalized Difference Vegetation Index (NDVI) and the increasing and decreasing slopes of the NDVI profile ( Figure 7b). Soil preparation practices, such as tillage and sowing, clear the land surface contrasting with naturally-vegetated areas and resulting in higher reflectance in the red band.…”

Early Detection of Summer Crops Using High Spatial Resolution Optical Image Time Series

“…2016, 8,591 3 of 15 region of alluvial plains and hills, the steepest slopes are mainly found in the transition areas between low and high altitudes. The site is oriented towards polyculture/livestock, and is composed of crops, forests, urban areas, grasslands and water bodies.…”