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Dutta, Sujay
- Crop Type Discrimination and Health Assessment using Hyperspectral Imaging
Abstract Views :227 |
PDF Views:83
Authors
Rahul Nigam
1,
Rojalin Tripathy
1,
Sujay Dutta
1,
Nita Bhagia
1,
Rohit Nagori
1,
K. Chandrasekar
2,
Rajsi Kot
3,
Bimal K. Bhattacharya
1,
Susan Ustin
4
Affiliations
1 Agriculture and Land Eco-system Division, Earth, Ocean, Atmosphere, Planetary Sciences and Applications Area, Space Applications Centre (ISRO), Ahmedabad 380 015, IN
2 National Remote Sensing Centre (ISRO), Hyderabad 500 037, IN
3 M.G. Science Institute, Ahmedabad 380 009, IN
4 Environmental and Resource Sciences, University of California, Davis, CA 95616, US
1 Agriculture and Land Eco-system Division, Earth, Ocean, Atmosphere, Planetary Sciences and Applications Area, Space Applications Centre (ISRO), Ahmedabad 380 015, IN
2 National Remote Sensing Centre (ISRO), Hyderabad 500 037, IN
3 M.G. Science Institute, Ahmedabad 380 009, IN
4 Environmental and Resource Sciences, University of California, Davis, CA 95616, US
Source
Current Science, Vol 116, No 7 (2019), Pagination: 1108-1123Abstract
Advancements in hyperspectral remote sensing technology have opened new avenues to explore innovative ways to map crops in terms of area and health. To study precise mapping of agriculture and horticulture crops along with biophysical and biochemical constituents at field scale, an airborne AVIRIS-NG hyperspectral imaging has been conducted in various agro-climatic regions representing diverse agricultural types of India. Crop classification with available and developed algorithms has been applied over homogeneous and heterogeneous agriculture and horticulture cropped areas. The spectral angle mapper and maximum likelihood algorithms showed classification accuracy of 77%–94% for AVIRI-NG and 42%–55% for LISS IV. The customized deep neural network and maximum noise function (MNF)-based classification schemes showed an accuracy of 93% and 86% for mapping of agriculture and horticulture crops respectively. The forward and inversion of canopy radiative transfer model protocol was developed for retrieval of crop parameters such as leaf area index (LAI) and chlorophyll content (Cab) using AVIRIS-NG narrow bands. The retrieved LAI and Cab showed 19%–27% and 23%–29% deviation from measured mean for homogeneous and heterogeneous agricultural areas respectively. Red edge position index-based empirical model and multivariate linear regression of multiple indices showed maximum correlation of 0.62 and 0.93 respectively, to map leaf nitrogen content. Water condition index was developed using vegetation and water indices to distinguish crop water-based abiotic stress. Wheat yellow rust disease has been identified at field scale using absorption band depth analysis at 662–702 and 2155–2175 nm, and further applied to AVIRIS-NG data to detect biotic stress at spatial scale. This study establishes that such missions have the potential to boost accurate mapping of economically valuable minor crops and generate health indicators to distinguish biotic and abiotic stresses at field scale.Keywords
Assessment, Biotic and Abiotic Stress, Crop Classification, Health, Hyperspectral Imaging.Full Text
References
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- Utilizing Machine Learning Algorithm, Cloud Computing Platform and Remote Sensing Satellite Data for Impact Assessment of Flood on Agriculture Land
Abstract Views :46 |
PDF Views:41
Authors
Affiliations
1 ICAR-National Dairy Research Institute, Karnal 132 001, IN
2 Lovely Professional University, Phagwara 144 001, IN
3 Space Applications Centre, Indian Space Research Organizations, Ahmedabad 380 015, IN
4 Commissionerate of Rural Development, Government of Gujarat, Gandhinagar 382 010, IN
1 ICAR-National Dairy Research Institute, Karnal 132 001, IN
2 Lovely Professional University, Phagwara 144 001, IN
3 Space Applications Centre, Indian Space Research Organizations, Ahmedabad 380 015, IN
4 Commissionerate of Rural Development, Government of Gujarat, Gandhinagar 382 010, IN
Source
Current Science, Vol 125, No 8 (2023), Pagination: 886-895Abstract
Floods are one of the most devastating natural disasters that cause immense damage to life, property and agriculture worldwide. Recurring floods in Bihar (a state in eastern India) during the monsoon season impact the agro-based economy, destroying crops and making it difficult for farmers to prepare for the next season. To mitigate the impact of floods on the agricultural sector, there is a need for early warning systems. Nowadays, remote sensing technology is used extensively for monitoring and managing flood events, which is also used in the present study. The random forest (RF) machine learning (ML) algorithm has also been used for land-use classification, and its output is used as an input for flood impact assessment. Here, we have analysed the flood extents and their impact on agriculture using Sentinel-1 SAR, Sentinel-2 and Planet Scope optical imageries on the Google Earth Engine (GEE) cloud computing platform. The present study shows that floods severely impacted a large part of Bihar during the monsoon seasons of 2020 and 2021. About 701,967 ha of land (614,706 ha agricultural land) in 2020 and 955,897 ha (851,663 ha agricultural land) in 2021 were severely flooded. An inundation maps and area statistics have been generated to visualise the results, which can help the government authorities prioritize relief and rescue operations.Keywords
Agriculture, Cloud Computing Platforms, Floods, Machine Learning Algorithm, Remote Sensing Data.Full Text
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