Abstract: The new developments of satellite series in China and Europe are bringing new opportunities to advance the agricultural monitoring with abundant satellite data. The Sentinel and GF are both quite similar high resolution satellite series onboard European and Chinese satellites, respectively. The Proba-V and FY3-MERSI both have quite similar channels and their own advantages in the medium to low resolution satellite.

This project is going to focus on the crop mapping, crop condition monitoring and crop drought monitoring with both satellite data. The Ningxia Hui autonomous region, one of small size provinces in China, was selected as the study area for the crop mapping study with GF and Sentinel optical satellite data. The field survey was conducted in June, 2016 and 2017 as well as in June/July this year. Sen2Agri, an open source system has been developed and demonstrated in various continents and is now considered as an operational system enabling the delivery in near real time of four products for any region in the world. The GF satellite data were also collected as much as possible for the coverage of Ningxia in the growing season. The processing method of GF data is now developing in order to automatically ingest large volume data. Based on the Sen2Agri system, the 2017 cropland product is already quite promising, with an overall accuracy of 86%. The compatibility of GF data need to be evaluated and combined with Sentinel-2 data in order to improve the classification accuracy.

Another two major agricultural production areas in China, North China Plain and Northeast China Plain were also selected for the crop monitoring and crop mapping with both medium to low resolution satellite data. The field surveys were conducted in summer 2016 and spring in 2017. The relevant Proba-V satellite data have been downloaded and a processing code was developed to extract the Proba-V data for the area of interesting. The FY-MERSI process chain has been developed in the past. The classification approach was integrated with Radom Forest, Support Vector Machine and Neural Net. Hopefully the preliminary results may be reported at the symposium.

Keywords: Crop Mapping; Classification; GF; Sentinel, Sen2Agri

Sentinel-2 for Agriculture system for crop mapping along the season in the Ningxia Hui Autonomous region

Mathilde De Vroey¹, Jinlong Fan², Nicolas Bellemans¹, Xiaoyu Zhang³ ,Lei Zhang³, Qi Xu^2,4,QiLiang Li^2,4, Hao Gao², Sophie Bontemps¹ and Pierre Defourny¹

¹ Earth and Life Institue, Université catholique de Louvain, Louvain-la-Neuve, Belgium

² National Satellite Meteorological Center, China

³ Ningxia Meteorological Science Institute, China

⁴ Shanxi Agricultural University, China

Abstract: Crop area extent estimates and crop type maps provide crucial information for agricultural monitoring and management. Remote sensing imagery in general and, more specifically, high temporal and high spatial resolution data, such as Sentinel-2A and B, constitute a major asset for this kind of application. The flows of observation data provided by these new sensors introduced new conceptual and processing challenges. The development of the Sentinel-2 for Agriculture system (www.esa-sen2agri.org) was supported by the European Space Agency to facilitate the Sentinel-2 and Landsat-8 time series exploitation for agriculture monitoring in most agricultural systems across the globe.

This open source system has been developed and demonstrated in various continents. Sen2Agri is now considered as an operational system enabling the delivery in near real time of four products for any region in the world, namely (1) a monthly cloud free surface reflectance composite at 10-20m, (2) a binary map identifying annually cultivated land at 10m updated every month, (3) a crop type map at 10m (provided twice along the season) for the main regional crops type and (4) an NDVI and LAI maps at 10m describing the vegetative development of crops on a 5 to 10 day basis. This Sen2Agri system can still be improved and further research is needed to optimize the use of the available processing chains and adapt them to the diversity of agricultural landscapes and biophysical environments.

In the context of a Dragon 4 project, this research aims to validate the system for the Ningxia Hui Autonomous Region in China and to evaluate the precision and accuracy of the crop mask and crop type products (L4A and L4B respectively) obtained from Sentinel-2A and Sentinel_2B. In 2017 a field campaign allowed collecting calibration and validation for the whole irrigated floodplain. The ground dataset have been complemented by delineating additional non cropland samples to cover the whole range of the landscape diversity. The whole study site covers an area of 66500 km² corresponding to 6 Sentinel-2 tiles. The Sentinel-2 images of the same season have been downloaded and pre-processed automatically by Sen2Agri system. The Sentinel-2 surface reflectance time series was then processed to generate a crop mask and then a crop type map from the ground truth data provided by a field campaign in 2017. The 2017 cropland product is already quite promising, with an overall accuracy of 86%. Secondly, the Sen2Agri system generates using a random forest classifier a very accurate and precise classification for the main crop types of the region. Nevertheless, several issues were brought to light. Firstly, Sen2Agri tends to neglect the marginal classes, which are much less represented in the training dataset. Secondly, the crop mask which should be generated without any in situ data, i.e. using ESA’s CCI Land Cover 2010 as default base map, needs be improved either by using the ESA’s CCI Land Cover 2015 or by alternative processing strategies. Based on the crop calendars, the timeliness of the products is still to be discussed to understand how long before harvesting an accurate crop type classification can be obtained.

In addition, this study aims to evaluate the potential contribution of GF images to crop mapping in combination with Sentinel-2. First of all the compatibility of GF data need to be evaluated and combined with Sentinel-2 data. Then the complementarity of both data sources will be assessed in terms of accuracy and timeliness.

Keywords: Sen2Agri; Crop Mapping; Classification; GF; Sentinel

Abstract: Identifying crop type with remotely sensed image is the fundamental step for calculating crop area and monitoring crop growth as well as estimating crop yield in the context of agricultural remote sensing. At present, the method of identifying single or two crops among the major staple crops, such as corn, rice and wheat, was well investigated by researchers, however, the identification of all crop types at the same image is very difficult and needs to be further improved.This study intends to use three kinds of classifiers, such as RF, SVM and NN with the Chinese High Resolution satellite (GF) data, to map the crop types in Ningxia. The crop types are recognized as rice, corn, wheat, clover, grapes, alfalfa, vegetables and greenhouse which are planted in the crop land. The Chinese High Resolution satellite (GF) data in 16m spatial resolution covering the entire Ningxia within the growing season was collected as much as possible. Around 1700 ground truth sample data were also collected In June 2017.

The main steps of the study are as (1) randomly dividing all field sample points into 70% training samples and 30% validation samples; further training more samples with the support of Google Earth image taking the crop phenology into account; adding more samples for non-crop area (Water, Built-up, Bareland, Forest, SolarPanel), and finally the best training sample datasets were obtained after the preliminary classification, self-test, and correction of training samples;(2) three classifiers are tuned to get the optimal classification model. The optimal NN activation function is Hyperbolic; The SVM optimal function is Polynomial with the Degree of Kernel Polynomial and Probability Threshold of 6,0.2 respectively; Number of trees and Number of features for RF were set as 1000 and 4 respectively;(3) the classification accuracy and the efficiency of the three classifiers were compared and evaluated. The accuracy evaluation indexes include Overall Accuracy, Producer accuracy, user accuracy, Kappa and F1 Score. The classification results show that NN>RF>SVM for the efficiency, RF>SVM>NN for the classification accuracy;(4) finally, the crop type map was created. The parameters for the Classifiers applied in this study were tuned specially with the training samples. It needs to be further investigated if those parameters may be extended to other areas and training samples.

Keywords: Classification; Crop type mapping; GF; RF;SVM;NN

Abstract: Crop and land cover classification requires a large amount of ground sample data with the location information in support of the supervised classification of remote sensing images and the accuracy evaluation. Due to the limitation of operating efficiency and cost, the traditional sampling method is not sufficient to support the crop classification at large scale. This study proposed an approach of retrieving the ground truth data from GPS photos taken as the vehicle is moving. The key technical aspects in the study include checking and restoring the photo location information; determining the observing azimuth; shifting the photo taken location to the object location; and interpreting the photos and outputting the data set with the crop type, code and the position information. (1) Checking and restoring the photo location information; Due to the failure connecting to the GPS signal, the GPS camera sometimes was not able to record the position information in the photo file. Another set of GPS recorder may be used to record the position as a complementary. The photos without GPS position may be added the position information later on. The photo and GPS records may be matched by the time but the time difference of two sets of equipment should be taken into account. The time difference may be calculated using the photos with the position information. In case that all photos do not have the position information, a few of typical photos should be checked and identified the position with the Google Earth image and then matched with GPS recorder data. An averaged time difference was further calculated and used as an offset to match both photos and the GPS recorder data. (2) Determining the observing azimuth. Many GPS cameras cannot record the observing azimuth. The observing azimuth may be 0-360 degree for one single sample point. When there are two sample points, the moving direction can be determined by the positions of two points. Adding the angle between the moving direction and the observing direction (close to 90 degree) to the azimuth of moving, the observing azimuth is available. The observation direction, left or right should be recorded as well. (3) Shifting the photo taken location to the object location; the position of the photo file is recorded as the position of photo taken and not the position of the object in the photo. The difference of the position should be compensated when the ground truth data is retrieving. The observing azimuth is available after the previous steps, and then the offset may be calculated with an estimated the distance between the photo taken and the position of the object in the photo. (4) Interpreting the photos and outputting the data set with the crop type, code and the position information. The software was developed to display the photo and select the preset crop types and the crop code. And finally, a text file with all these information was output as the ground truth data set. This approach and the software has been demonstrated for several case studies.

Keywords: Sample; GPS photo; GPS