scholarly journals Using of Multi-Source and Multi-Temporal Remote Sensing Data Improves Crop-Type Mapping in the Subtropical Agriculture Region

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2401 ◽  
Author(s):  
Chuanliang Sun ◽  
Yan Bian ◽  
Tao Zhou ◽  
Jianjun Pan
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Crop Growth ◽  
Landsat 8 ◽  
Growth Status ◽  
Sensing Data ◽  
Crop Type ◽  
Multi Temporal ◽  
Crop Types ◽  
Sentinel 2

Crop-type identification is very important in agricultural regions. Most researchers in this area have focused on exploring the ability of synthetic-aperture radar (SAR) sensors to identify crops. This paper uses multi-source (Sentinel-1, Sentinel-2, and Landsat-8) and multi-temporal data to identify crop types. The change detection method was used to analyze spectral and indices information in time series. Significant differences in crop growth status during the growing season were found. Then, three obviously differentiated time features were extracted. Three advanced machine learning algorithms (Support Vector Machine, Artificial Neural Network, and Random Forest, RF) were used to identify the crop types. The results showed that the detection of (Vertical-vertical) VV, (Vertical-horizontal) VH, and Cross Ratio (CR) changes was effective for identifying land cover. Moreover, the red-edge changes were obviously different according to crop growth periods. Sentinel-2 and Landsat-8 showed different normalized difference vegetation index (NDVI) changes also. By using single remote sensing data to classify crops, Sentinel-2 produced the highest overall accuracy (0.91) and Kappa coefficient (0.89). The combination of Sentinel-1, Sentinel-2, and Landsat-8 data provided the best overall accuracy (0.93) and Kappa coefficient (0.91). The RF method had the best performance in terms of identity classification. In addition, the indices feature dominated the classification results. The combination of phenological period information with multi-source remote sensing data can be used to explore a crop area and its status in the growing season. The results of crop classification can be used to analyze the density and distribution of crops. This study can also allow to determine crop growth status, improve crop yield estimation accuracy, and provide a basis for crop management.

scholarly journals MONITORING WHEAT CROP GROWTH PARAMETERS USING TIME SERIES SENTINEL-1 AND SENTINEL-2 DATA FOR AGRICULTURAL APPLICATION IN MONGOLIA

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 989-994
Author(s):  
B. Gansukh ◽  
B. Batsaikhan ◽  
A. Dorjsuren ◽  
C. Jamsran ◽  
N. Batsaikhan
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Growth Parameters ◽  
Remote Sensing Data ◽  
Crop Growth ◽  
Wheat Crop ◽  
Sensing Data ◽  
Application Method ◽  
Agricultural Application ◽  
Sentinel 2

Abstract. Wheat is the most important food crop in Mongolia, most of the croplands are utilizing for wheat cultivating area in the central northern region of Mongolia. The Mongolian government has several policies on the agricultural sector with wheat production in the study region has been intensified to meet people’s food demands and economic development. Monitoring wheat-growing areas is thus important to developing strategies for food security in the region. In the present study, we aimed to develop an agricultural application method using remote sensing data. Sentinel-1 SAR and Sentinel-2 MSI analysis of time series data were carried out to monitor the wheat crop growth parameters. Time-series images were acquired during May 2019–September 2019 at different growth stages in Bornuur soum, Tuv province of Mongolia. The wheat crop parameters, i.e. normalized difference vegetation index, vegetation water content, backscatter value of VV, VH channels were estimated using remote sensing data with reference data as cadastre polygons of current cropland area. The results showed that provide timely and valuable information for agricultural production, management and policy-making. The agricultural application method will help to agriculture management and monitoring include crop identification and cropland mapping, crop growth monitoring, inversion of key biophysical, biochemical and environmental parameters, crop damage/disaster monitoring, precision agriculture, etc.

scholarly journals Application of Deep Learning Algorithm to Build an Automated Cloud Segmentation Model Based on Open Data Cube Framework

2020 ◽  
Vol 36 (4) ◽  
Author(s):  
Pham Vu Dong ◽  
Bui Quang Thanh ◽  
Nguyen Quoc Huy ◽  
Vo Hong Anh ◽  
Pham Van Manh
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Rapid Development ◽  
Open Data ◽  
Remote Sensing Data ◽  
Data Cube ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Sensing Data ◽  
Multi Temporal

Cloud detection is a significant task in optical remote sensing to reconstruct the contaminated cloud area from multi-temporal satellite images. Besides, the rapid development of machine learning techniques, especially deep learning algorithms, can detect clouds over a large area in optical remote sensing data. In this study, the method based on the proposed deep-learning method called ODC-Cloud, which was built on convolutional blocks and integrating with the Open Data Cube (ODC) platform. The results showed that our proposed model achieved an overall 90% accuracy in detecting cloud in Landsat 8 OLI imagery and successfully integrated with the ODC to perform multi-scale and multi-temporal analysis. This is a pioneer study in techniques of storing and analyzing big optical remote sensing data.

scholarly journals A RADIOMETRIC NORMALIZATION METHOD OF CONTROLLING NO-CHANGED SET (CNCS) FOR DIVERSE LANDCOVER USING MULTI-SENSOR DATA

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 863-870
Author(s):  
L. T. Huang ◽  
W. L. Jiao ◽  
T. F. Long ◽  
C. L. Kang
Keyword(s):  
Remote Sensing ◽  
Linear Relationship ◽  
Land Surface ◽  
Remote Sensing Data ◽  
Normalization Method ◽  
Sensor Data ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Sensing Data ◽  
Sentinel 2

Abstract. The accurate acquisition of land surface reflectance (SR) data determines the accuracy of ground objects recognition, classification and land surface parameter inversion using remote sensing data, which is the basis of remote sensing data application. In this study, a Control No-Changed Set (CNCS) radiometric normalization method is proposed to realize spectral information transformation of multi-sensor data, which is based on the Iteratively Reweighted Multivariate Alteration Detection (IR-MAD), and includes automatic selection and step-by-step optimization of no-change pixels. The No-Changed set (NC) is obtained by selecting the original no-change pixels between the target image and the reference image according to the linear relationship. In the obtained original no-change regions, IR-MAD rules with iterative control are used to fix the final no-change pixels, after regression modeling and calculation, the normalized images are obtained. The method is tested on multi-images from multi-sensors in three groups of experiments (GF-1 WFV and Landsat-8 OLI, GF-1 PMS and Sentinel-2 MSI, and Landsat-8 OLI and Sentinel-2 MSI) with different landcover areas. The results of radiometric normalization are evaluated qualitatively and quantitatively. The data of the three groups of experiments have a high correlation (correlation coefficient r values > 0.85), indicating that they can be used together as complementary data. The Root Mean Squared Error (RMSE) values calculate from the NC between the reference and normalized target images are much smaller than those between the reference and original target images. The radiometric colour composition effects, and the typical ground objects spectral reflective curves of the reference and normalized target images are very similar after radiometric normalization. These results indicate that the CNCS method considers the linear relationship of the no-change pixels and is effective, stable, and can be used to improve the consistency of SR of multi-images from multi-sensors.

On the potential of multi-source remote sensing data in characterizing the Total Suspended Matter variability in inland waters.

2021 ◽  
Author(s):  
Emanuele Ciancia ◽  
Alessandra Campanelli ◽  
Teodosio Lacava ◽  
Angelo Palombo ◽  
Simone Pascucci ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Methodological Approach ◽  
Remote Sensing Data ◽  
Spatiotemporal Variability ◽  
Water Level Fluctuations ◽  
Inland Waters ◽  
Landsat 8 ◽  
Ground Truth Data ◽  
Sensing Data ◽  
Multi Temporal

<p>The assessment of TSM spatiotemporal variability plays a key role in inland water management, considering how these fluctuations affect water transparency, light availability, and the physical, chemical, and biological processes. All the above-mentioned topics highlight the need to develop innovative methodologies of data analysis that are able to handle multi-mission and multi-source remote sensing data, fostering the implementation of integrated and sustainable approaches. Sentinel-2A multispectral instrument (MSI) and Landsat 8 operational land instrument (OLI) data offer unique opportunities for investigating certain in-water constituents (e.g., TSM and chlorophyll-a) mainly owing to their spatial resolution (10–60 m). Furthermore, the joint use of these sensors offers the opportunity to build time series with an improved revisiting time thus enabling limnologists, aquatic ecologists and water resource managers to enhance their monitoring efforts. In this framework, the potential of MSI–OLI combined data in characterizing the multi-temporal (2014–2018) TSM variability in Pertusillo Lake (Basilicata region, Southern Italy) has been evaluated in this work. In particular, a customized MSI-based TSM model (R<sup>2</sup>=0.81) has been developed and validated by using ground truth data acquired during specific measurement campaigns. The model was then exported on OLI data through an inter-calibration procedure (R<sup>2</sup>=0.87), allowing for the generation of a TSM multi-temporal MSI–OLI merged dataset. The analysis of the derived multi-year TSM monthly maps has shown the influence of hydrological factors on the TSM seasonal dynamics over two sub-regions of the lake, the west and east areas. The western side appears more affected by inflowing rivers and water level fluctuations, whose  effects  tend to longitudinally decrease, leading to less sediment within the eastern sub-area. The achieved results highlight how the proposed methodological approach (i.e. in situ data collection, satellite data processing and modeling) can be exported in other inland waters that deserve to be investigated for a better management of water quality and monitoring systems.</p>

scholarly journals REMOTE SENSING DATA IN MUNICIPAL GOVERNMENT

2021 ◽  
Vol 30 (4) ◽  
Author(s):  
Jaroslav Nýdrle
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Vegetation Index ◽  
Spatial Information ◽  
Relevant Literature ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Remotely Sensed Data ◽  
Sensing Data ◽  
Sentinel 2

This article focuses on the issue of using data obtained through remote sensing methods  in the administrative district of the municipality with extended powers of Liberec (the Czech Republic). The first part of the article discusses the question of using Earth remote sensing data for city agendas in general. Then, it presents a questionnaire, created for evaluating the needs of the Liberec municipality. This questionnaire, focusing on the use of remotely sensed data, was created on the basis of a review of relevant literature. Based on the results of the questionnaire, the following spatial information requirements were chosen to be addressed: land surface temperature map - LST (Landsat 8), vegetation index - NDVI (Sentinel 2, Planet Scope), normalized difference water index - NDWI, NDWI 2 (Sentinel 2), normalized difference built-up index - NDBI (Sentinel 2). All data obtained during the creation of this study have become part of the database of the Urban Planning and GIS Department and are available to employees of the City of Liberec.

scholarly journals Some Peculiarities of Arable Soil Organic Matter Detection Using Optical Remote Sensing Data

2021 ◽  
Vol 13 (12) ◽  
pp. 2313
Author(s):  
Elena Prudnikova ◽  
Igor Savin
Keyword(s):  
Remote Sensing ◽  
Organic Matter ◽  
Soil Organic Matter ◽  
Spectral Reflectance ◽  
Remote Sensing Data ◽  
Soil Surface ◽  
Optical Remote Sensing ◽  
Test Field ◽  
Sensing Data ◽  
Sentinel 2

Optical remote sensing only provides information about the very thin surface layer of soil. Rainfall splash alters soil surface properties and its spectral reflectance. We analyzed the impact of rainfall on the success of soil organic matter (SOM) content (% by mass) detection and mapping based on optical remote sensing data. The subject of the study was the arable soils of a test field located in the Tula region (Russia), their spectral reflectance, and Sentinel-2 data. Our research demonstrated that rainfall negatively affects the accuracy of SOM predictions based on Sentinel-2 data. Depending on the average precipitation per day, the R2cv of models varied from 0.67 to 0.72, RMSEcv from 0.64 to 1.1% and RPIQ from 1.4 to 2.3. The incorporation of information on the soil surface state in the model resulted in an increase in accuracy of SOM content detection based on Sentinel-2 data: the R2cv of the models increased up to 0.78 to 0.84, the RMSEcv decreased to 0.61 to 0.71%, and the RPIQ increased to 2.1 to 2.4. Further studies are necessary to identify how the SOM content and composition of the soil surface change under the influence of rainfall for other soils, and to determine the relationships between rainfall-induced SOM changes and soil surface spectral reflectance.

scholarly journals Mapping Allochemical Limestone Formations in Hazara, Pakistan Using Google Cloud Architecture: Application of Machine-Learning Algorithms on Multispectral Data

2021 ◽  
Vol 10 (2) ◽  
pp. 58
Author(s):  
Muhammad Fawad Akbar Khan ◽  
Khan Muhammad ◽  
Shahid Bashir ◽  
Shahab Ud Din ◽  
Muhammad Hanif
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Learning Algorithms ◽  
Remote Sensing Data ◽  
Kappa Coefficient ◽  
Machine Learning Algorithms ◽  
Landsat 8 ◽  
Sensing Data ◽  
Fossiliferous Limestone

Low-resolution Geological Survey of Pakistan (GSP) maps surrounding the region of interest show oolitic and fossiliferous limestone occurrences correspondingly in Samanasuk, Lockhart, and Margalla hill formations in the Hazara division, Pakistan. Machine-learning algorithms (MLAs) have been rarely applied to multispectral remote sensing data for differentiating between limestone formations formed due to different depositional environments, such as oolitic or fossiliferous. Unlike the previous studies that mostly report lithological classification of rock types having different chemical compositions by the MLAs, this paper aimed to investigate MLAs’ potential for mapping subclasses within the same lithology, i.e., limestone. Additionally, selecting appropriate data labels, training algorithms, hyperparameters, and remote sensing data sources were also investigated while applying these MLAs. In this paper, first, oolitic (Samanasuk), fossiliferous (Lockhart and Margalla) limestone-bearing formations along with the adjoining Hazara formation were mapped using random forest (RF), support vector machine (SVM), classification and regression tree (CART), and naïve Bayes (NB) MLAs. The RF algorithm reported the best accuracy of 83.28% and a Kappa coefficient of 0.78. To further improve the targeted allochemical limestone formation map, annotation labels were generated by the fusion of maps obtained from principal component analysis (PCA), decorrelation stretching (DS), X-means clustering applied to ASTER-L1T, Landsat-8, and Sentinel-2 datasets. These labels were used to train and validate SVM, CART, NB, and RF MLAs to obtain a binary classification map of limestone occurrences in the Hazara division, Pakistan using the Google Earth Engine (GEE) platform. The classification of Landsat-8 data by CART reported 99.63% accuracy, with a Kappa coefficient of 0.99, and was in good agreement with the field validation. This binary limestone map was further classified into oolitic (Samanasuk) and fossiliferous (Lockhart and Margalla) formations by all the four MLAs; in this case, RF surpassed all the other algorithms with an improved accuracy of 96.36%. This improvement can be attributed to better annotation, resulting in a binary limestone classification map, which formed a mask for improved classification of oolitic and fossiliferous limestone in the area.

scholarly journals Comparing of Landsat 8 and Sentinel 2A using Water Extraction Indexes over Volta River

2017 ◽  
Vol 10 (1) ◽  
pp. 1 ◽  
Author(s):  
Clement Kwang ◽  
Edward Matthew Osei Jnr ◽  
Adwoa Sarpong Amoah
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Unsupervised Classification ◽  
Water Bodies ◽  
Water Extraction ◽  
Landsat 8 ◽  
Sensing Data ◽  
Optimal Classifier ◽  
Sentinel 2A

Remote sensing data are most often used in water bodies’ extraction studies and the type of remote sensing data used also play a crucial role on the accuracy of the extracted water features. The performance of the proposed water indexes among the various satellite images is not well documented in literature. The proposed water indexes were initially developed with a particular type of data and with advancement and introduction of new satellite images especially Landsat 8 and Sentinel, therefore the need to test the level of performance of these water indexes as new image datasets emerged. Landsat 8 and Sentinel 2A image of part Volta River was used. The water indexes were performed and then ISODATA unsupervised classification was done. The overall accuracy and kappa coefficient values range from 98.0% to 99.8% and 0.94 to 0.98 respectively. Most of water bodies enhancement indexes work better on Sentinel 2A than on Landsat 8. Among the Landsat based water bodies enhancement ISODATA unsupervised classification, the modified normalized water difference index (MNDWI) and normalized water difference index (NDWI) were the best classifier while for Sentinel 2A, the MNDWI and the automatic water extraction index (AWEI_nsh) were the optimal classifier. The least performed classifier for both Landsat 8 and Sentinel 2A was the automatic water extraction index (AWEI_sh). The modified normalized water difference index (MNDWI) has proved to be the universal water bodies enhancement index because of its performance on both the Landsat 8 and Sentinel 2A image.

scholarly journals Estimation of rice yield by SIMRIW-RS, a model that integrates remote sensing data into a crop growth model

2017 ◽  
Vol 73 (1) ◽  
pp. 2-8 ◽  
Author(s):  
Masayasu MAKI ◽  
Kosuke SEKIGUCHI ◽  
Koki HOMMA ◽  
Yoshihiro HIROOKA ◽  
Kazuo OKI
Keyword(s):  
Remote Sensing ◽  
Growth Model ◽  
Rice Yield ◽  
Remote Sensing Data ◽  
Crop Growth ◽  
Crop Growth Model ◽  
Sensing Data
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