Fringe 2017 > Session details
Paper 288 - Session title: Thematic mapping, vegetation and DEMs
14:00 Impact Of Different Satellite Data On The Crop Classification Map Accuracy In Ukraine
Kussul, Nataliia (1); Lavreniuk, Mykola (2); Shelestov, Andrii (2); Yailymov, Bohdan (1) 1: Space Research Institute, Ukraine; 2: National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”
Remote sensing images from the space have always been an obvious and promising source of information for deriving crop maps. This is mainly due capabilities to timely acquire images and provide repeatable, continuous, human independent measurements for large territories. Crop mapping and classification of agricultural crops is extremely valuable source of information for many applied problems in agricultural monitoring and food security.
Taking into account that free optical satellite data was available for many past years and the weather-independent synthetic-aperture radar (SAR) images were very expensive, a lot of studies on crop classification tasks had been done using only optical data. In the same time, for time-series based on optical data there are some issues, such as clouds and shadows effect, and as a result different number of observation for the study area. There are different techniques to deal with this issues: methods for clouds and shadows restoration , feature extraction methods  etc.
Thanks to the launching Sentinel-1A (S1A) SAR satellite by European Space Agency (ESA) in 2014, we have access to the free high resolution weather-independent SAR images. It allows us to solve the problem with clouds, to equalize number of observation for the all study area and to increase the number of observation.
In this study, we compare three data sources for crop classification maps derivation: Sentinel-1A data, Sentinel-2A data and data fusion from Sentinel-1A and Sentinel-2A. For Sentinel-1A SAR series, only pre-processing to produce geocoded imagery is required before classification, for which we use the SNAP Toolbox. For Sentinel-2A time-series we use only 4 bands with 10m spatial resolution (Blue, Green, Red and near infrared (NIR)). Ground truth data were collected within along the road surveys in 2016 and were randomly divided for training and test samples in equal proportions. Test set was using for independent result validation. For this crop classification investigation ensemble of neural networks had been utilized .
Detailed experimental results in term of overall, user accuracy, producer accuracy and crop classification maps for Sentinel-1A, Sentinel-2A and fusion of Sentinel-1A and Sentinel-2A will be presented.
Keywords: agriculture, image processing, data fusion, Sentinel-1, Sentinel-2.
 N. Kussul, S. Skakun, A. Shelestov, M. Lavreniuk, B. Yailymov, and O. Kussul, “Regional Scale Crop Mapping Using Multi-Temporal Satellite Imagery,” Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, pp. 45–52, 2015.
 N. Matton, et al., “An automated method for annual cropland mapping along the season for various globally-distributed agrosystems using high spatial and temporal resolution time series,” Remote Sensing, vol. 7, no. 10, pp. 13208-13232, 2015.
 S. Skakun, N. Kussul, A. Y. Shelestov, M. Lavreniuk, O. Kussul, “Efficiency Assessment of Multitemporal C-Band Radarsat-2 Intensity and Landsat-8 Surface Reflectance Satellite Imagery for Crop Classification in Ukraine,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, DOI: 10.1109/JSTARS.2015.2454297.
Paper 326 - Session title: Thematic mapping, vegetation and DEMs
14:20 Incoherent and interferometric coherent models to interpreter the Rice Phenology from dual polarimetric C-band Sentinel-1
Ndikumana, Emile; Ho Tong Minh, Dinh; Baghdadi, Nicolas IRSTEA, France
Rice is one of the most important staple foods for a large part of the world. For this reason, monitoring of its biophysical variables is necessary for farm management and performance prediction. Synthetic Aperture Radar (SAR) is the dominant high-resolution remote sensing data source for agricultural applications in tropical and subtropical regions.
The Sentinel-1 mission, continuously with the successful of ERS-1/2 and Envisat ASAR, it is mainly dedicated to urban applications through multi-temporal C-band (wavelength 5.6 cm) SAR interferometry technique. However, the Terrain Observation with Progressive Scan (TOPS) mode of Sentinel-1 offers an unique opportunity : day revist 12 days an even 6 days with Sentinel-1 A/B. Hence, this opens a window to study its coherence for agriculture context.
The objective of this study is to model rice backscattering and coherence to better understand the dual polarimetric Sentinel-1.
For the incoherent model, we adapt the MIMICS (Michigan Microwave Canopy Scattering), a radar scattering model was developed for forest . MIMICS is a physical model of radar backscatter, based on the theoretical principle of the first order of transfer radiactif model to simulate the radar backscatter from the ground with dense vegetation. The forest backscattering model MIMICS is adapted to accommodate agricultural parameters by removing its trunk contribution.
For the interferometric coherent model, we consider a two layers model which include ground and volume contributions. Their contribution are adapted to follow the growing of the rice. This model is based on the sum of two Kronecker products model for forest context .
Both incoherent and interferometric coherent models were tested and well matched with the VV and VH polarimetric Sentinel-1 data in Camargue, France. In future, we will investigate to what extent both models can help to understand the dual polalarimetric Sentinel-1 backscatter for rice application.
 Ulaby, F.T.; Sarabandi, K.; McDonald, K.; Whitt, M.; Dobson, M.C. Michigan microwave canopy scattering model. Int. J. Remote Sens., 1990, vol. 11, pp.1223–1253.
 S. Tebaldini and F. Rocca, “Multibaseline polarimetric SAR tomography of a boreal forest at P- and L-bands,” IEEE Trans. Geosci. Remote Sens., vol. 50, no. 1, pp. 232–246, Jan. 2012.
Paper 345 - Session title: Thematic mapping, vegetation and DEMs
14:40 Integration of Sentinel-1 coherence and backscattering signatures for delineation of agricultural management practices.
Lemoine, Guido; Leo, Olivier; Corbane, Christina European Commission, Italy
The introduction of Sentinel-1B in September 2016 provides a unique and novel opportunity to generate C-band interferometric coherence over very large areas for 6-day temporal baselines. In Europe, large areas are covered by both ascending and descending orbits, and, especially at higher latitudes, by adjacent orbits. Thus, coherence information can be collected for an even denser temporal sequence, though always interleaved for 6 day intervals. The interest of using coherence in agricultural use contexts has been relatively limited, mostly due to the fact that, during the growing season, vegetation cover often leads to very low coherence, especially if the temporal baselines are too long (e.g. 24 days for Radarsat, 35 days for ENVISAT). Although commercial SAR systems (e.g. TerraSAR-X, CosmoSkyMed) may be able to achieve better temporal baselines, though with a significantly higher frequency (X-band), their use for wide area monitoring is prohibitively expensive. The “full, free and open” data license of the Copernicus programme and the extraordinary performance of the Sentinels are essential to vastly scale up the use of SAR in crop monitoring.
Currently, the agricultural user community is abuzz with the more traditional application examples of SAR, and hybrid SAR and optical, data use, for instance, in crop classification. The joint JRC-ESA-SZIF experiment Czech-Agri (part of the Sen2Agri project) has demonstrated that country-wide consistent crop maps can be derived from combination of Sentinel-1 with Landsat-8 (2015) and Sentinel-1 with Sentinel-2 (2016) combined with existing reference parcel information and targeted surveying. Similar results are available, in other use contexts, in the United Kingdom, the Netherlands, Finland and Ukraine. The GEO Global Agricultural Monitoring (GEOGLAM) community of practice now clearly recognizes the need to integrate SAR in crop classification beyond the well-established use in rice monitoring. Classification accuracies in the examples typically reach the 85-90% overall accuracy range for a significant set of crop types. None of these activities include coherence analysis, however.
Coherence of agricultural surfaces is strongly related to the stability of the surface geometry. This is the key reason why crop canopies, with [moving] vegetation structures that are in the order of the C-band wavelength, exhibit low coherence. Undisturbed bare soil, however, tends to show high coherence. The key word here is “undisturbed”, implying that disturbances that significantly change the surface structure (e.g. ploughing, seedbed preparation, erosion) is detectable as a loss in coherence. Also, emergence of vegetation will lead to a, gradual, loss of coherence. Integrating the changes in backscattering intensity, and partial polarimetric decomposition, provides further clues about the direction of change, for instance, from a smooth surface to a rough surface. For the latter, the use of meteorological records is essential to understand the separate impacts of soil moisture, which may lead to coherent change in backscattering, and incoherent surface structure change. Apart from the potential to use coherence to further refine crop classification products, we expect the greatest added value in the analysis of crop phenology and variation within crop groups, as a contribution to refined crop yield modeling and crop production estimates.
We will demonstrate the use of a time series of combined Sentinel-1 coherence and backscattering intensities over the Netherlands, where we have a complete reference data set and detailed weather information. We highlight the effects of agricultural management practices and how their detection are input to [very] early delineation of crop type probability maps. Our time series will cover the initial growing season of 2016/2017 for which we will integrate available Sentinel-2 information to determine how sensitive the signatures are to crop emergence and canopy closure. We will discuss requirements for large area generation, most of which is automated, and the relevance of our work to European Common Agricultural Policy management and control, with selected examples.
Paper 546 - Session title: Thematic mapping, vegetation and DEMs
15:00 Round Table Discussion
All, All ESA, Italy
2.03.c Thematic mapping, vegetation and DEMs