Remote sensing of rice crop areas with UAVs data: Krasnodar region, Russia

2020 ◽  
Author(s):  
Anatoliy Pogorelov ◽  
Evgeniy Kiselev ◽  
Evgeniy Boyko ◽  
Viacheslav Krylenko
Keyword(s):  
Remote Sensing ◽  
Rice Crop ◽  
Krasnodar Region

scholarly journals Research of rice crops in krasnodar region by remote sensing data (preliminary analysis)

RICE GROWING ◽  
2020 ◽  
Vol 46 (1) ◽  
pp. 34-43
Author(s):  
E. N. Kiselev ◽  
◽  
A. V. Pogorelov ◽  
S. V. Garkusha ◽  
M. A. Skazhennik ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Preliminary Analysis ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Krasnodar Region

scholarly journals Rice Crop Monitoring and Yield Estimation Through Cosmo Skymed and TerraSAR-X: A SAR-Based Experience in India

2015 ◽  
Vol XL-7/W3 ◽  
pp. 85-92 ◽  
Author(s):  
S. Pazhanivelan ◽  
P. Kannan ◽  
P. Christy Nirmala Mary ◽  
E. Subramanian ◽  
S. Jeyaraman ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Food Security ◽  
Accuracy Assessment ◽  
Rice Crop ◽  
Policy Decisions ◽  
Simulation Accuracy ◽  
Sustainable Solutions ◽  
Rice Area ◽  
Sar Imagery ◽  
Crop Parameters

Rice is the most important cereal crop governing food security in Asia. Reliable and regular information on the area under rice production is the basis of policy decisions related to imports, exports and prices which directly affect food security. Recent and planned launches of SAR sensors coupled with automated processing can provide sustainable solutions to the challenges on mapping and monitoring rice systems. High resolution (3m) Synthetic Aperture Radar (SAR) imageries were used to map and monitor rice growing areas in selected three sites in TamilNadu, India to determine rice cropping extent, track rice growth and estimate yields. A simple, robust, rule-based classification for mapping rice area with multi-temporal, X-band, HH polarized SAR imagery from COSMO Skymed and TerraSAR X and site specific parameters were used. The robustness of the approach is demonstrated on a very large dataset involving 30 images across 3 footprints obtained during 2013-14. A total of 318 in-season site visits were conducted across 60 monitoring locations for rice classification and 432 field observations were made for accuracy assessment. Rice area and Start of Season (SoS) maps were generated with classification accuracies ranging from 87- 92 per cent. Using ORYZA2000, a weather driven process based crop growth simulation model; yield estimates were made with the inclusion of rice crop parameters derived from the remote sensing products viz., seasonal rice area, SoS and backscatter time series. Yield Simulation accuracy levels of 87 per cent at district level and 85- 96 per cent at block level demonstrated the suitability of remote sensing products for policy decisions ensuring food security and reducing vulnerability of farmers in India.

scholarly journals QUALITY CHECKING OF CROP CUTTING EXPERIMENTS USING REMOTE SENSING DATA: A CASE STUDY FOR RICE CROP IN ODISHA

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 461-466
Author(s):  
S. K. Dubey ◽  
D. Mandloi ◽  
A. S. Gavli ◽  
A. Latwal ◽  
R. Das ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Rice Crop ◽  
High Yield ◽  
Equal Proportion ◽  
Yield Data ◽  
Crop Condition ◽  
Crop Variety ◽  
Cutting Experiments

<p><strong>Abstract.</strong> Under Pradhan Mantri Fasal Bima Yojana (PMFBY), a large number of Crop Cutting Experiments (CCEs) were conducted by Odisha State for Kharif Rice in the year 2016 and 2017. The present study was carried out to examine the quality of the performed CCEs using statistical methods and Remote Sensing (RS) technique. Total 24389 and 34725 CCEs were conducted. After removing outliers, 22083 and 26848 CCE points were analyzed for the year 2016 and 2017, respectively. Multi-date RISAT-1 (2016) and Sentinel-1A (2017) satellite data were used for generating the Kharif Rice crop mask, which was used to get NDVI and NDWI values for Rice pixels, from MODIS VI products. The values of these indices were divided into four strata from highest A, followed by B, C, and D (Lowest Value) based on the range (minimum and maximum) of values. The CCE based yield data were then divided into four yield strata of equal proportion. Yield and RS (NDVI+NDWI) based strata were combined to examine whether the CCE Points having high yield fall under good NDVI zone or vice versa. The results showed that there was strong match between CCE strata and the vegetation index strata in both the years. Therefore, it could be be concluded that RS based indices have the capability to assess the quality/accuracy of CCEs. Furthermore, the large variety of information available with CCEs such that crop variety, crop condition, water sources, stress conditions etc., can be used as input parameters to train any model to predict better results.</p>

scholarly journals Sentinel-2 Images and Machine Learning as Tool for Monitoring of the Common Agricultural Policy: Calasparra Rice as a Case Study

Agronomy ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 621
Author(s):  
Francisco Javier López-Andreu ◽  
Manuel Erena ◽  
Jose Antonio Dominguez-Gómez ◽  
Juan Antonio López-Morales
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Agricultural Policy ◽  
Common Agricultural Policy ◽  
New Technologies ◽  
European Space Agency ◽  
Statistical Treatment ◽  
Rice Crop ◽  
Suitable Model ◽  
Sentinel 2

The European Commission introduces the Control by Monitoring through new technologies to manage Common Agricultural Policy funds through the Regulation 2018/746. The advances in remote sensing have been considered one of these new technologies, mainly since the European Space Agency designed the Copernicus Programme. The Sentinel-1 (radar range) and Sentinel-2 (optical range) satellites have been designed for monitoring agricultural problems based on the characteristics they provide. The data provided by the Sentinel 2 missions, together with the emergence of different scientific disciplines in artificial intelligence —especially machine learning— offer the perfect basis for identifying and classifying any crop and its phenological state. Our research is based on developing and evaluating a pixel-based supervised classification scheme to produce accurate rice crop mapping in a smallholder agricultural zone in Calasparra, Murcia, Spain. Several models are considered to obtain the most suitable model for each element of the time series used; pixel-based classification is performed and finished with a statistical treatment. The highly accurate results obtained, especially across the most significant vegetative development dates, indicate the benefits of using Sentinel-2 data combined with Machine Learning techniques to identify rice crops. It should be noted that it was possible to locate rice crop areas with an overall accuracy of 94% and standard deviation of 1%, which could be increased to 96% (±1%) if we focus on the months of the crop’s highest development state. Thanks to the proposed methodology, the on-site inspections carried out, 5% of the files, have been replaced by remote sensing evaluations of 100% of the analyzed season files. Besides, by adjusting the model input data, it is possible to detect unproductive or abandoned plots.

scholarly journals KNOWLEDGE BASED CLASSIFIER BASED ON BACKSCATTERING COEFFICIENT FOR MONITORING THE CROP GROWTH ANALYSIS USING MULTI-TEMPORAL IMAGES OF SPACE-BORNE SYNTHETIC APERTURE RADAR (SAR) SENSORS

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 643-647
Author(s):  
A. K. Verma ◽  
R. Nandan ◽  
A. Verma
Keyword(s):  
Remote Sensing ◽  
Synthetic Aperture Radar ◽  
Crop Growth ◽  
Microwave Spectrum ◽  
Rice Crop ◽  
Synthetic Aperture ◽  
Earth Surface ◽  
Knowledge Based ◽  
The Earth ◽  
Multi Temporal

<p><strong>Abstract.</strong> Space-based observation of crops and agro-system on the Earth surface is one of the most important applications of remote sensing using the sensors in optical and microwave spectrum to assess the crop growth for decision making for developing crop information and management system. Remote sensing technology provides scalable and reliable information in respect of rice crop grown area, its crop growth and prediction of crop yield due to acquisition of satellite imagery during the revisit of the orbit by space-borne sensors in optical and microwave spectrum. Synthetic Aperture Radar has the advantages of all-weather, day and night imaging, canopy penetration, and high-resolution capabilities, which makes Space-borne SAR sensors as an effective system for monitoring crop growth, crop classification and mapping of crop area based on the crop canopy interaction of SAR signals due to backscattering coefficients of the earth surface. SAR data from ERS-1/2 SAR, ENVISAT ASAR, ALOS-1/2 PALSAR, Radarsat-1/2 SAR, TerraSAR, COSMO-SkyMed, and Sentinel-1 have been used by various researchers for identification and analysis of rice crop growth based on the backscattering values in different regions of Asia and European region, where backscattered image depends of various earth surface and SAR sensors parameters. In this paper, knowledge based classifier using SAR images of existing space-borne-SAR sensors have been developed based on modeling of SAR backscattering coefficients in C-band and X-band for monitoring the rice crop growth and its analysis using multi-temporal and multi-frequency- SAR sensors data.</p>

Comparison of various modelling approaches for water deficit stress monitoring in rice crop through hyperspectral remote sensing

2019 ◽  
Vol 213 ◽  
pp. 231-244 ◽  
Author(s):  
Gopal Krishna ◽  
Rabi N. Sahoo ◽  
Prafull Singh ◽  
Vaishangi Bajpai ◽  
Himesh Patra ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Deficit ◽  
Hyperspectral Remote Sensing ◽  
Rice Crop ◽  
Water Deficit Stress ◽  
Stress Monitoring ◽  
Modelling Approaches
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