scholarly journals Crop Mapping with Combined Use of European and Chinese Satellite Data

2021 ◽  
Vol 13 (22) ◽  
pp. 4641
Author(s):  
Jinlong Fan ◽  
Pierre Defourny ◽  
Xiaoyu Zhang ◽  
Qinghan Dong ◽  
Limin Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Satellite Data ◽  
Yellow River ◽  
Regional Scale ◽  
Agricultural Landscapes ◽  
Use Of Time ◽  
Medium Resolution ◽  
Combined Use ◽  
Crop Mapping ◽  
Crop Types

Agricultural landscapes are characterized by diversity and complexity, which makes crop mapping at a regional scale a top priority for different purposes such as administrative decisions and farming management. Project 32194 of the Dragon 4 Program was implemented to meet the requirements of crop mapping, with the specific objective to develop suitable approaches for precise crop mapping with combined uses of European and Chinese high- and medium-resolution satellite images. Two sub-projects were involved in the project. The first was to focus on the use of time series high-resolution satellite data, including Sentinel-2 (S2, European satellite data) and Gaofen-1 (GF-1, Chinese satellite data), due to their similar spectral bands for Earth observation, while the second was to focus on medium-resolution data sources, i.e., the European Project for On-Board Autonomy–Vegetation (PROBA-V) and Chinese Fengyun-3 Medium Resolution Spectral Imager (FY-3 MERSI) satellite data, also due to their similar spectral channels. The approach of the European Space Agency (ESA) Sent2Agri project for crop mapping was adapted in the first sub-project and applied to the Yellow River irrigated district (YERID) of Ningxia in northwest China in order to assess its ability to accurately identify crop types in China. The goal of the second sub-project was to explore the potential of both European and Chinese medium-resolution satellite data for crop assessment in a large area. Methods to handle the data and retrieve the required information for the precise crop mapping were developed in the study, including the adaptation of the ESA approach to GF-1 data and the application of algorithms for classification. A scheme for the validation of the crop mapping was developed in the study. The results of implementing the scheme to the YERID in Ningxia indicated that the overall accuracies of crop mapping with S2 and GF-1 can be high, up to 94–97%, and the mapping had an accuracy of 88% with the PROBA-V and FY3B-MERSI data. The very high accuracy suggests the possibility of precise crop mapping with the combined use of time series high- and medium-resolution satellite data when suitable approaches are chosen to handle the data for the classification of crop types.

scholarly journals Assessing macrophyte seasonal dynamics using dense time series of medium resolution satellite data

2018 ◽  
Vol 216 ◽  
pp. 230-244 ◽  
Author(s):  
Paolo Villa ◽  
Monica Pinardi ◽  
Rossano Bolpagni ◽  
Jean-Marc Gillier ◽  
Peggy Zinke ◽  
...  
Keyword(s):  
Time Series ◽  
Satellite Data ◽  
Seasonal Dynamics ◽  
Medium Resolution

scholarly journals Combined Use of Landsat-8 and Sentinel-2A Images for Winter Crop Mapping and Winter Wheat Yield Assessment at Regional Scale

2017 ◽  
Vol 3 (2) ◽  
pp. 163-186 ◽  
Author(s):  
Sergii Skakun ◽  
◽  
Eric Vermote ◽  
Jean-Claude Roger ◽  
Belen Franch ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Regional Scale ◽  
Wheat Yield ◽  
Landsat 8 ◽  
Combined Use ◽  
Winter Crop ◽  
Winter Wheat Yield ◽  
Crop Mapping ◽  
Sentinel 2A

scholarly journals Classification of Crops, Pastures, and Tree Plantations along the Season with Multi-Sensor Image Time Series in a Subtropical Agricultural Region

2019 ◽  
Vol 11 (3) ◽  
pp. 334 ◽  
Author(s):  
Cecília Lira Melo de Oliveira Santos ◽  
Rubens Augusto Camargo Lamparelli ◽  
Gleyce Kelly Dantas Araújo Figueiredo ◽  
Stéphane Dupuy ◽  
Julie Boury ◽  
...  
Keyword(s):  
Time Series ◽  
Cloud Cover ◽  
Radar Data ◽  
Agricultural Landscapes ◽  
Gap Filling ◽  
Use Of Time ◽  
High Quality Image ◽  
Low Cloud ◽  
Low Cloud Cover ◽  
Heterogeneous Region

Timely and efficient land-cover mapping is of high interest, especially in agricultural landscapes. Classification based on satellite images over the season, while important for cropland monitoring, remains challenging in subtropical agricultural areas due to the high diversity of management systems and seasonal cloud cover variations. This work presents supervised object-based classifications over the year at 2-month time-steps in a heterogeneous region of 12,000 km2 in the Sao Paulo region of Brazil. Different methods and remote-sensing datasets were tested with the random forest algorithm, including optical and radar data, time series of images, and cloud gap-filling methods. The final selected method demonstrated an overall accuracy of approximately 0.84, which was stable throughout the year, at the more detailed level of classification; confusion mainly occurred among annual crop classes and soil classes. We showed in this study that the use of time series was useful in this context, mainly by including a small number of highly discriminant images. Such important images were eventually distant in time from the prediction date, and they corresponded to a high-quality image with low cloud cover. Consequently, the final classification accuracy was not sensitive to the cloud gap-filling method, and simple median gap-filling or linear interpolations with time were sufficient. Sentinel-1 images did not improve the classification results in this context. For within-season dynamic classes, such as annual crops, which were more difficult to classify, field measurement efforts should be densified and planned during the most discriminant window, which may not occur during the crop vegetation peak.

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 Optimizing Feature Selection of Individual Crop Types for Improved Crop Mapping

2020 ◽  
Vol 12 (1) ◽  
pp. 162 ◽  
Author(s):  
Leikun Yin ◽  
Nanshan You ◽  
Geli Zhang ◽  
Jianxi Huang ◽  
Jinwei Dong
Keyword(s):  
Feature Selection ◽  
Agricultural Development ◽  
Development Planning ◽  
Sanjiang Plain ◽  
The Sanjiang Plain ◽  
Regional Food ◽  
Probability Maps ◽  
Medium Resolution ◽  
Crop Mapping ◽  
Crop Types

Accurate crop planting area information is of significance for understanding regional food security and agricultural development planning. While increasing numbers of medium resolution satellite imagery and improved classification algorithms have been used for crop mapping, limited efforts have been made in feature selection, despite its vital impacts on crop classification. Furthermore, different crop types have their unique spectral and phenology characteristics; however, the different features of individual crop types have not been well understood and considered in previous studies of crop mapping. Here, we examined an optimized strategy to integrate specific features of individual crop types for mapping an improved crop type layer in the Sanjiang Plain, a new food bowl in China, by using all Sentinel-2 time series images in 2018. First, an automatic spectro-temporal feature selection (ASTFS) method was used to obtain optimal features for individual crops (rice, corn, and soybean), including sorting all features by the global separability indices for each crop and removing redundant features by accuracy changes when adding new features. Second, the ASTFS-based optimized feature sets for individual crops were used to produce three crop probability maps with the Random Forest classifier. Third, the probability maps were then composited into the final crop layer by considering the probability of each crop at every pixel. The resultant crop layer showed an improved accuracy (overall accuracy = 93.94%, Kappa coefficient = 0.92) than the other classifications without such a feature optimizing process. Our results indicate the potential of the ASTFS method for improving regional crop mapping.

scholarly journals Large-Scale Crop Mapping Based on Machine Learning and Parallel Computation with Grids

2019 ◽  
Vol 11 (12) ◽  
pp. 1500 ◽  
Author(s):  
Ning Yang ◽  
Diyou Liu ◽  
Quanlong Feng ◽  
Quan Xiong ◽  
Lin Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Time Series ◽  
Parallel Computing ◽  
Random Forest ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Remote Sensing Images ◽  
Medium Resolution ◽  
Crop Mapping

Large-scale crop mapping provides important information in agricultural applications. However, it is a challenging task due to the inconsistent availability of remote sensing data caused by the irregular time series and limited coverage of the images, together with the low spatial resolution of the classification results. In this study, we proposed a new efficient method based on grids to address the inconsistent availability of the high-medium resolution images for large-scale crop classification. First, we proposed a method to block the remote sensing data into grids to solve the problem of temporal inconsistency. Then, a parallel computing technique was introduced to improve the calculation efficiency on the grid scale. Experiments were designed to evaluate the applicability of this method for different high-medium spatial resolution remote sensing images and different machine learning algorithms and to compare the results with the widely used nonparallel method. The computational experiments showed that the proposed method was successful at identifying large-scale crop distribution using common high-medium resolution remote sensing images (GF-1 WFV images and Sentinel-2) and common machine learning classifiers (the random forest algorithm and support vector machine). Finally, we mapped the croplands in Heilongjiang Province in 2015, 2016, 2017, which used a random forest classifier with the time series GF-1 WFV images spectral features, the enhanced vegetation index (EVI) and normalized difference water index (NDWI). Ultimately, the accuracy was assessed using a confusion matrix. The results showed that the classification accuracy reached 88%, 82%, and 85% in 2015, 2016, and 2017, respectively. In addition, with the help of parallel computing, the calculation speed was significantly improved by at least seven-fold. This indicates that using the grid framework to block the data for classification is feasible for crop mapping in large areas and has great application potential in the future.

scholarly journals High-Resolution Rice Mapping Based on SNIC Segmentation and Multi-Source Remote Sensing Images

2021 ◽  
Vol 13 (6) ◽  
pp. 1148
Author(s):  
Lingbo Yang ◽  
Limin Wang ◽  
Ghali Abdullahi Abubakar ◽  
Jingfeng Huang
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
High Resolution ◽  
Synthetic Aperture Radar ◽  
Classification Accuracy ◽  
Synthetic Aperture ◽  
Resolution Time ◽  
Medium Resolution ◽  
Crop Mapping ◽  
Sentinel 2

High-resolution crop mapping is of great significance in agricultural monitoring, precision agriculture, and providing critical information for crop yield or disaster monitoring. Meanwhile, medium resolution time-series optical and synthetic aperture radar (SAR) images can provide useful phenological information. Combining high-resolution satellite data and medium resolution time-series images provides a great opportunity for fine crop mapping. Simple Non-Iterative Clustering (SNIC) is a state-of-the-art image segmentation algorithm that shows the advantages of efficiency and high accuracy. However, the application of SNIC in crop mapping based on the combination of high-resolution and medium-resolution images is unknown. Besides, there is still little research on the influence of the superpixel size (one of the key user-defined parameters of the SNIC method) on classification accuracy. In this study, we employed a 2 m high-resolution GF-1 pan-sharpened image and 10 m medium resolution time-series Sentinel-1 C-band Synthetic Aperture Radar Instrument (C-SAR) and Sentinel-2 Multispectral Instrument (MSI) images to carry out rice mapping based on the SNIC method. The results show that with the increase of the superpixel size, the classification accuracy increased at first and then decreased rapidly after reaching the summit when the superpixel size is 27. The classification accuracy of the combined use of optical and SAR data is higher than that using only Sentinel-2 MSI or Sentinel-1 C-SAR vertical transmitted and vertical received (VV) or vertical transmitted and horizontal received (VH) data, with overall accuracies of 0.8335, 0.8282, 0.7862, and 0.7886, respectively. Meanwhile, the results also indicate that classification based on superpixels obtained by SNIC significantly outperforms classification based on original pixels. The overall accuracy, producer accuracy, and user accuracy of SNIC superpixel-based classification increased by 9.14%, 17.16%, 27.35% and 1.36%, respectively, when compared with the pixel-based classification, based on the combination of optical and SAR data (using the random forest as the classifier). The results show that SNIC superpixel segmentation is a feasible method for high-resolution crop mapping based on multi-source remote sensing data. The automatic selection of the optimal superpixel size of SNIC will be focused on in future research.

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