scholarly journals Improved Crop Classification with Rotation Knowledge using Sentinel-1 and -2 Time Series

2020 ◽  
Vol 86 (7) ◽  
pp. 431-441 ◽  
Author(s):  
Sébastien Giordano ◽  
Simon Bailly ◽  
Loic Landrieu ◽  
Nesrine Chehata
Keyword(s):  
Time Series ◽  
Conditional Random Field ◽  
Temporal Structure ◽  
Linear Chain ◽  
Agricultural Practices ◽  
Annual Crops ◽  
Automatic Integration ◽  
Crop Type ◽  
Crop Types ◽  
Type Classification

Leveraging the recent availability of accurate, frequent, and multimodal (radar and optical) Sentinel-1 and -2 acquisitions, this paper investigates the automation of land parcel identi- fication system (LPIS ) crop type classification. Our approach allows for the automatic integration of temporal knowledge, i.e., crop rotations using existing parcel-based land cover databases and multi-modal Sentinel-1 and -2 time series. The temporal evolution of crop types was modeled with a linear- chain conditional random field, trained with time series of multi-modal (radar and optical) satellite acquisitions and associated LPIS. Our model was tested on two study areas in France (≥ 1250 km2) which show different crop types, various parcel sizes, and agricultural practices: . the Seine et Marne and the Alpes de Haute-Provence classified accordingly to a fine national 25-class nomenclature. We first trained a Random Forest classifier without temporal structure to achieve 89.0% overall accuracy in Seine et Marne (10 classes) and 73% in Alpes de Haute-Provence (14 classes). We then demonstrated experimentally that taking into account the temporal structure of crop rotation with our model resulted in an increase of 3% to +5% in accuracy. This increase was especially important (+12%) for classes which were poorly classified without using the temporal structure. A stark posi- tive impact was also demonstrated on permanent crops, while it was fairly limited or even detrimental for annual crops.

scholarly journals National Crop Mapping Using Sentinel-1 Time Series: A Knowledge-Based Descriptive Algorithm

2021 ◽  
Vol 13 (5) ◽  
pp. 846
Author(s):  
Carole Planque ◽  
Richard Lucas ◽  
Suvarna Punalekar ◽  
Sebastien Chognard ◽  
Clive Hurford ◽  
...  
Keyword(s):  
Time Series ◽  
Crop Growth ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Growth Stages ◽  
Knowledge Based ◽  
Crop Type ◽  
Crop Mapping ◽  
Crop Growth Stages ◽  
Crop Types

National-level mapping of crop types is important to monitor food security, understand environmental conditions, inform optimal use of the landscape, and contribute to agricultural policy. Countries or economic regions currently and increasingly use satellite sensor data for classifying crops over large areas. However, most methods have been based on machine learning algorithms, with these often requiring large training datasets that are not always available and may be costly to produce or collect. Focusing on Wales (United Kingdom), the research demonstrates how the knowledge that the agricultural community has gathered together over past decades can be used to develop algorithms for mapping different crop types. Specifically, we aimed to develop an alternative method for consistent and accurate crop type mapping where cloud cover is quite persistent and without the need for extensive in situ/ground datasets. The classification approach is parcel-based and informed by concomitant analysis of knowledge-based crop growth stages and Sentinel-1 C-band SAR time series. For 2018, crop type classifications were generated nationally for Wales, with regional overall accuracies ranging between 85.8% and 90.6%. The method was particularly successful in distinguishing barley from wheat, which is a major source of error in other crop products available for Wales. This study demonstrates that crops can be accurately identified and mapped across a large area (i.e., Wales) using Sentinel-1 C-band data and by capitalizing on knowledge of crop growth stages. The developed algorithm is flexible and, compared to the other methods that allow crop mapping in Wales, the approach provided more consistent discrimination and lower variability in accuracies between classes and regions.

scholarly journals Mapping Crop Types and Cropping Systems in Nigeria with Sentinel-2 Imagery

2021 ◽  
Vol 13 (17) ◽  
pp. 3523
Author(s):  
Esther Shupel Ibrahim ◽  
Philippe Rufin ◽  
Leon Nill ◽  
Bahareh Kamali ◽  
Claas Nendel ◽  
...  
Keyword(s):  
Time Series ◽  
Cropping Systems ◽  
Early Warning Systems ◽  
Sub Saharan Africa ◽  
Mixed Cropping ◽  
Smallholder Farming ◽  
Jos Plateau ◽  
Crop Type ◽  
Crop Types ◽  
The Creation

Reliable crop type maps from satellite data are an essential prerequisite for quantifying crop growth, health, and yields. However, such maps do not exist for most parts of Africa, where smallholder farming is the dominant system. Prevalent cloud cover, small farm sizes, and mixed cropping systems pose substantial challenges when creating crop type maps for sub-Saharan Africa. In this study, we provide a mapping scheme based on freely available Sentinel-2A/B (S2) time series and very high-resolution SkySat data to map the main crops—maize and potato—and intercropping systems including these two crops on the Jos Plateau, Nigeria. We analyzed the spectral-temporal behavior of mixed crop classes to improve our understanding of inter-class spectral mixing. Building on the Framework for Operational Radiometric Correction for Environmental monitoring (FORCE), we preprocessed S2 time series and derived spectral-temporal metrics from S2 spectral bands for the main temporal cropping windows. These STMs were used as input features in a hierarchical random forest classification. Our results provide the first wall-to-wall crop type map for this key agricultural region of Nigeria. Our cropland identification had an overall accuracy of 84%, while the crop type map achieved an average accuracy of 72% for the five relevant crop classes. Our crop type map shows distinctive regional variations in the distribution of crop types. Maize is the dominant crop, followed by mixed cropping systems, including maize–cereals and potato–maize cropping; potato was found to be the least prevalent class. Plot analyses based on a sample of 1166 fields revealed largely homogeneous mapping patterns, demonstrating the effectiveness of our classification system also for intercropped classes, which are temporally and spatially highly heterogeneous. Moreover, we found that small field sizes were dominant in all crop types, regardless of whether or not intercropping was used. Maize–legume and maize exhibited the largest plots, with an area of up to 3 ha and slightly more than 10 ha, respectively; potato was mainly cultivated on fields smaller than 0.5 ha and only a few plots were larger than 1 ha. Besides providing the first spatially explicit map of cropping practices in the core production area of the Jos Plateau, Nigeria, the study also offers guidance for the creation of crop type maps for smallholder-dominated systems with intercropping. Critical temporal windows for crop type differentiation will enable the creation of mapping approaches in support of future smart agricultural practices for aspects such as food security, early warning systems, policies, and extension services.

scholarly journals TRACKING FARM MANAGEMENT PRACTICES WITH REMOTE SENSING

2017 ◽  
Vol XLII-3/W2 ◽  
pp. 197-208
Author(s):  
J. P. Stals ◽  
S. Ferreira
Keyword(s):  
South Africa ◽  
Remote Sensing ◽  
Large Scale ◽  
Management Practices ◽  
Farm Management ◽  
Data Set ◽  
Crop Type ◽  
Crop Types ◽  
Type Classification ◽  
Over Time

Earth observation (EO) data is effective in monitoring agricultural cropping activity over large areas. An example of such an application is the GeoTerraImage crop type classification for the South African Crop Estimates Committee (CEC). The satellite based classification of crop types in South Africa provides a large scale, spatial and historical record of agricultural practices in the main crop growing areas. The results from these classifications provides data for the analysis of trends over time, in order to extract valuable information that can aid decision making in the agricultural sector. Crop cultivation practices change over time as farmers adapt to demand, exchange rate and new technology. Through the use of remote sensing, grain crop types have been identified at field level since 2008, providing a historical data set of cropping activity for the three most important grain producing provinces of Mpumalanga, Freestate and North West province in South Africa. This historical information allows the analysis of farm management practices to identify changes and trends in crop rotation and irrigation practices. Analysis of crop type classification over time highlighted practices such as: frequency of cultivation of the same crop on a field, intensified cultivation on centre pivot irrigated fields with double cropping of a winter grain followed by a summer grain in the same year and increasing cultivation of certain types of crops over time such as soyabeans. All these practices can be analysed in a quantitative spatial and temporal manner through the use of the remote sensing based crop type classifications.

scholarly journals Evaluation of Five Deep Learning Models for Crop Type Mapping Using Sentinel-2 Time Series Images with Missing Information

2021 ◽  
Vol 13 (14) ◽  
pp. 2790
Author(s):  
Hongwei Zhao ◽  
Sibo Duan ◽  
Jia Liu ◽  
Liang Sun ◽  
Louis Reymondin
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Spatial Resolution ◽  
Missing Information ◽  
Type Mapping ◽  
Crop Type ◽  
Crop Types ◽  
Optical Imagery ◽  
Time Series Images ◽  
Sentinel 2

Accurate crop type maps play an important role in food security due to their widespread applicability. Optical time series data (TSD) have proven to be significant for crop type mapping. However, filling in missing information due to clouds in optical imagery is always needed, which will increase the workload and the risk of error transmission, especially for imagery with high spatial resolution. The development of optical imagery with high temporal and spatial resolution and the emergence of deep learning algorithms provide solutions to this problem. Although the one-dimensional convolutional neural network (1D CNN), long short-term memory (LSTM), and gate recurrent unit (GRU) models have been used to classify crop types in previous studies, their ability to identify crop types using optical TSD with missing information needs to be further explored due to their different mechanisms for handling invalid values in TSD. In this research, we designed two groups of experiments to explore the performances and characteristics of the 1D CNN, LSTM, GRU, LSTM-CNN, and GRU-CNN models for crop type mapping using unfilled Sentinel-2 (Sentinel-2) TSD and to discover the differences between unfilled and filled Sentinel-2 TSD based on the same algorithm. A case study was conducted in Hengshui City, China, of which 70.3% is farmland. The results showed that the 1D CNN, LSTM-CNN, and GRU-CNN models achieved acceptable classification accuracies (above 85%) using unfilled TSD, even though the total missing rate of the sample values was 43.5%; these accuracies were higher and more stable than those obtained using filled TSD. Furthermore, the models recalled more samples on crop types with small parcels when using unfilled TSD. Although LSTM and GRU models did not attain accuracies as high as the other three models using unfilled TSD, their results were almost close to those with filled TSD. This research showed that crop types could be identified by deep learning features in Sentinel-2 dense time series images with missing information due to clouds or cloud shadows randomly, which avoided spending a lot of time on missing information reconstruction.

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

2015 ◽  
Vol 7 (8) ◽  
pp. 10400-10424 ◽  
Author(s):  
François Waldner ◽  
Marie-Julie Lambert ◽  
Wenjuan Li ◽  
Marie Weiss ◽  
Valérie Demarez ◽  
...  
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Land Cover ◽  
Resolution Time ◽  
Crop Type ◽  
Type Classification
Sign in / Sign up

Export Citation Format

Share Document