Rabi Groundnut Area Estimation using Synthetic Aperture Radar (SAR) in Thiruvannamalai District of Tamil Nadu

2021 ◽  
Author(s):  
S. Thirumeninathan ◽  
S. Pazhanivelan ◽  
N.S. Sudarmanian ◽  
K.P. Ragunath ◽  
A. Gurusamy ◽  
...  
Keyword(s):  
Food Production ◽  
Tamil Nadu ◽  
Growth Period ◽  
Crop Acreage ◽  
Maximum Likelihood Classification ◽  
Legume Crop ◽  
Temporal Features ◽  
Production Distribution ◽  
Multi Temporal ◽  
Food And Feed

Background: Groundnut, commonly known as peanut, is a significant oil, food and feed legume crop grown in all seasons in Tamil Nadu, including kharif, rabi and summer and it is cultivated both under irrigated and rainfed conditions in all the seasons at Thiruvannamlai district. One of the most important applications of remote sensing in agriculture is a Crop Acreage and Production Estimation (CAPE). The CAPE’s main goal is to estimate crop acreage and production of important crops, so that advanced food production, distribution and supply data were achieved. Methods: Multi-temporal Sentinel 1A SAR IW- GRD data with 20 m spatial resolution and 12 days temporal resolution of Vertical - Horizontal (V-H) polarization were downloaded for the period of 4th October 2020 to 8th January 2021 to have the full coverage during the crop growth period in the study area used for this work. Crop backscattering and multi-temporal features were extracted from MAPscape 5.2 automated pre-processing tool and its classified using supervised maximum likelihood classification for groundnut acreage extraction for Thiruvannamalai district. Result: The rabi groundnut area of Thiruvannamalai district of Tamil Nadu was estimated using SAR Sentinel-1A data as 32298 ha with a higher accuracy percentage of 87.4 and kappa coefficient of 0.75.

scholarly journals MULTI-TEMPORAL FEATURE EXTRACTION FOR PRECISE MAIZE AREA MAPPING USING TIME-SERIES SENTINEL 1A SAR DATA

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 169-173
Author(s):  
M. Venkatesan ◽  
S. Pazhanivelan ◽  
N. S. Sudarmanian
Keyword(s):  
Time Series ◽  
Tamil Nadu ◽  
Growth Period ◽  
Maturity Stage ◽  
Kappa Score ◽  
Maximum Increase ◽  
Maize Crop ◽  
Temporal Features ◽  
Multi Temporal ◽  
Sar Data

<p><strong>Abstract.</strong> A research study was conducted to map maize area in Ariyalur and Perambalur districts of Tamil Nadu, India using multi-temporal features extracted from time-series Sentinel 1A SAR data. Multi-temporal Sentinel 1A GRD data at VV and VH polarizations and SLC products were acquired for the study area at 12 days interval and processed using MAPscape-RICE software. Multi-temporal Sentinel 1A data was used to identify the backscattering dB curve of maize crop. Analysis of temporal signatures of the crop showed minimum values at sowing period and maximum during the tasseling stage, which decreased during maturity stage of the crop. The maximum increase in the signature was observed during seedling to vegetative growth period. The signature derived from dB values for maize crop expressed a significant temporal behavior with the range of &amp;minus;21.26 to &amp;minus;13.18 in VH polarization and &amp;minus;14.05 to &amp;minus;6.54 in VV polarization. Considering the accuracy of SAR data to phenological variations of maize growing period, Multi-Temporal Features were extracted from multi-temporal dB images of VV and VH polarization and coherence images. Multi-Temporal Features viz., max, min, mean, max date, min date and span ratio were extracted from VV and VH polarizations of Sentinel 1A GRD and SLC data to classify maize pixels in the study area using parameterized classification approach. The overall classification accuracy was 91 percent with the kappa score of 0.82.</p>

scholarly journals INTEGRATING TIME-SERIES SAR DATA AND ORYZA CROP GROWTH MODEL IN RICE AREA MAPPING AND YIELD ESTIMATION FOR CROP INSURANCES

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 239-243
Author(s):  
S. Pazhanivelan ◽  
K. P. Ragunath ◽  
N. S. Sudarmanian ◽  
R. Kumaraperumal ◽  
T. Setiyono ◽  
...  
Keyword(s):  
Time Series ◽  
Tamil Nadu ◽  
Crop Insurance ◽  
Crop Growth ◽  
Linear Diffusion ◽  
Temporal Features ◽  
Rice Area ◽  
Multi Temporal ◽  
Sar Data ◽  
Sar Imagery

<p><strong>Abstract.</strong> Lowland rice in tropical and subtropical regions can be detected precisely and its crop growth can be tracked effectively through Synthetic Aperture Radar (SAR) imagery, especially where cloud cover restricts the use of optical imagery. Parameterised classification with multi-temporal features derived from regularly acquired, C-band, VV and VH polarized Sentinel-1A SAR imagery was used for mapping rice area. A fully automated processing chain in MAPscape-Rice software was used to convert the multi-temporal SAR data into terrain-geocoded &amp;sigma;<sup>0</sup> values, which included strip mosaicking, co-registration of images acquired with the same observation geometry and mode, time-series speckle filtering, terrain geocoding, radiometric calibration and normalization. Further Anisotropic non-linear diffusion (ANLD) filtering was done to smoothen homogeneous targets, while enhancing the difference between neighbouring areas. Multi-Temporal Features viz., max, min, mean, max date, min date and span ratio were extracted from VV and VH polarizations to classify rice pixels. Rice detection was based on the analysis of temporal signature from SAR backscatter in relation to crop stages. About sixty images across four footprints covering 16 <i>samba</i> (<i>Rabi</i>) rice growing districts of Tamil Nadu, India were obtained between August 2017 and January 2018. In-season site visits were conducted across 280 monitoring locations in the footprints for classification purposes and more than 1665 field observations were made for accuracy assessment. A total rice area of 1.07 million ha was mapped with classification accuracy from 90.3 to 94.2 per cent with Kappa values ranging from 0.81 to 0.88. Using ORYZA2000, a weather driven process based crop growth simulation model developed by IRRI, yield estimates were made by integrating remote sensing products viz., seasonal rice area, start of season and backscatter time series. By generating average backscatter for each time series and dB stack for each SoS, LAI values were estimated. The model has generated rice yield estimate for each hectare which were aggregated at administrative boundary level and compared against CCE yield. Yield Simulation accuracy of more than 86&amp;ndash;91% at district level and 82&amp;ndash;97% at block level from the study indicates the suitability of these products for policy decisions. SAR products and yield information were used to meet the requirements of PMFBY crop insurance scheme in Tamil Nadu and helped in identifying or invoking prevented/failed sowing in 529 villages and total crop failure in 821 villages. In total 303703 farmers were benefitted by this technology in getting payouts of INR 9.94 billion through crop insurance. The satellite technology as an operational service has helped in getting quicker payouts.</p>

scholarly journals A social innovation perspective on business model improvement in food related industries

2020 ◽  
Vol 23 (5) ◽  
pp. 767-772
Author(s):  
Xiao-Wei Wen ◽  
Sang-Luo Sun ◽  
Zhao-Hui Yang
Keyword(s):  
Food Safety ◽  
Food Production ◽  
Social Innovation ◽  
Social Forces ◽  
Food Sector ◽  
Talent Pool ◽  
The Social ◽  
Production Distribution ◽  
Production And Distribution ◽  
Model Improvement

Food sector sustainability should not be discussed solely through an economic lens. On the contrary, social forces are critical in motivating and practicing high-quality food production and distribution in the supply chain context. This opinion addresses food sector imperatives from the social innovation perspective to preliminarily comment on social innovation’s potential influences on food production, distribution, and safety. Preliminary though, the purpose and contribution of this opinion paper are both stimulating future imagination in theory and practices for utilizing social innovation for food safety and sustainability. The main opinions are the employment of resources, sustainable development of resources, generation of finances and diversifying the talent pool for social innovative initiatives that promote food safety.

scholarly journals Practices and Changes in Public Nutrition in Slovakia During the Period of Construction of Socialism

2015 ◽  
Vol 15 (2) ◽  
pp. 19-29
Author(s):  
Rastislava Stoličná
Keyword(s):  
Food Production ◽  
Eating Habits ◽  
The State ◽  
Production Distribution

Abstract The study aims to describe the changes in public nutrition during the period of socialism in Slovakia. It explains the essence of the state Communist ideology’s involvement in people’s eating habits and the reality of food production, distribution and consumption.

Food and feed security

2009 ◽  
Vol 2009 ◽  
pp. 238-238
Author(s):  
M Raymond
Keyword(s):  
Climate Change ◽  
Food Security ◽  
Water Availability ◽  
Food Production ◽  
General Acceptance ◽  
Global Issue ◽  
The World ◽  
Increase Food Production ◽  
Food And Feed

Food security is a global issue. General acceptance of the UN prediction that the world must increase food production by at least 50% in the next 20 years, and at least 100% in the next 40. Climate change and water availability will make this extremely challenging.

scholarly journals SPECTRAL CHARACTERISTIZATION OF RICE FIELD USING MULTITEMPORAL LANDSAT ETM+ DATA

2010 ◽  
Vol 2 (-) ◽  
Author(s):  
I WAYAN NUARSA ◽  
SUSUMU KANNO ◽  
YASUHIRO SUGIMORI ◽  
FUMIHIKO NlSHIO
Keyword(s):  
Spectral Characteristic ◽  
Rice Plant ◽  
Rice Field ◽  
Linear Regression Analysis ◽  
Growth Period ◽  
Blue Band ◽  
Multi Temporal ◽  
Study Characteristics ◽  
Differentiation Period

The preliminary study using Landsat ETM+ to estimate the rice production in Regency of Tabanan, Bali Province was conducted. The objectives of this study were to know spectral characteristic of rice plant in three importance growth periods of rice, and to develop a model to identify the distribution of rice. Landsat ETM+ in two acquisition dates (March 21st, 2003 and May 24*, 2003) were used in this study. Characteristics of rice were analyzed using radiance value of Landsat ETM+ obtained from converting digital number of Landsat data. Multi-variable linear regression analysis was developed to classify the rice in its growth period. The result showed that the rice plant has different reflectance in seedling-development period, ear differentiation period and maturation period. It is expressed by the radiance value of Landsat ETM+. However, spectral characteristic of rice in each band of Landsat ETM+ is similar to the green vegetations in general, except in blue band (Bl). Based on statistical analysis, the classification of rice in each its growth period can be classified. Key words: Rice field, Landsat ETM+, Spectral Characteristic, Multi-temporal.

scholarly journals Extraction of crop acreage based on multi-temporal and dual-polarization SAR data

2020 ◽  
Vol 46 (7) ◽  
pp. 1099-1111
Author(s):  
me-alternatives Na ◽  
me-alternatives Na ◽  
ISAK Gulnur ◽  
Chun-Yue MA ◽  
SAWUT Mamat
Keyword(s):  
Dual Polarization ◽  
Crop Acreage ◽  
Multi Temporal ◽  
Sar Data

scholarly journals The Use of Microbial Inoculants in Crop Production for Food Security Sustainability

2021 ◽  
Vol 10 (1) ◽  
pp. 33-40
Author(s):  
Gbenga Oluwayomi Agbowuro ◽  
Morolake Elizabeth Ayeyo ◽  
Tejiri Sophia Emecho
Keyword(s):  
Environmental Sustainability ◽  
Food Production ◽  
Crop Production ◽  
Microbial Inoculants ◽  
Negative Effects ◽  
Political Unrest ◽  
Food And Feed ◽  
Livestock Health ◽  
Available Information ◽  
Increase Crop Yield

Increasing human population, war, climate change, herdsmen-farmers clashes, banditry, terrorism, political unrest affected crop production negatively. These factors widen the gap between food production supply and its demand. In an attempt to fill this gap, agrochemicals were used to increase crop yield to meet the food demand of the ever-increasing population. Agrochemical’s introduction was accepted initially due to their quick and nonspecific actions. Decades later, these agrochemicals begin to pose threats to human and livestock health, causing land degradation, ecosystem imbalance, reduction in soil fertility and productivity. To avert the negative effects of agrochemicals on food and feed products, soil, water quality, and the environment. The use of a safe and eco-friendly alternative was developed. Microbial inoculants serve to be the best substitute for agrochemicals with substantial benefits in sustainable crop production and environmental sustainability. This review aims at updating available information on the benefits of using microbial inoculants in boosting crop production and the strategies to adopt for its effectiveness.

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