Synergy of Sentinel-2A and Near-proximal sensor data for deriving biochemical parameters of paddy at different growth stages

Parida, Bikash Ranjan; Kushwaha, Amit; Ranjan, Avinash Kumar

doi:10.1007/s10668-021-01482-1

Cited by 12 publications

(2 citation statements)

References 48 publications

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“…Sentinel-2 A was the first optical Earth Observing Satellite launched in 2015 by the European Space Agency (ESA) as part of the European Copernicus Programme. It has been widely used to monitor vegetation and agricultural studies (Parida et al 2022 ). Sentinel-2 A consists of high-resolution bands, such as red edge bands, that enable to distinguish early changes from plant health and are utilised for analysing biochemical and biophysical compositions (Parida and Kumari 2021b ).…”

Section: Methodsmentioning

confidence: 99%

Monitoring tea plantations during 1990–2022 using multi-temporal satellite data in Assam (India)

2023

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Background Tea is a valuable economic plant grown extensively in several Asian countries. The accurate mapping of tea plantations is critical for the growth and development of the tea industry. In eastern India, tea plantations have a significant role in its economy. Sonitpur, Jorhat, Sibsagar, Dibrugarh, and Tinsukia are major tea-producing districts in Assam. Due to the rapid increase in tea plantations and the burgeoning population, a detailed mapping and regular monitoring of tea plantations are imperative for understanding land use alteration. Objectives The present study aims to analyse the dynamics of tea plantations from 1990 to 2022 at a decadal scale, using satellite data, such as Landsat-5 and Sentinel-2. Methods A supervised classifier called Random Forest (RF) was deployed in the Google Earth Engine (GEE) platform to classify tea plantations. Results The results showed significant growth in tea plantations in the district of Dibrugarh (112%), whereas the remaining districts had a growth rate of 45–89%. During 32 years (1990–2022), about 1280.47 km 2 (78.71%) of areas of tea plantations expanded across five districts of Assam. Precision and recall were used to measure the accuracy of tea plantations classification, which exhibited considerably high F1 scores (0.80 to 0.96). Conclusions This study helps to demonstrate the application of remote sensing techniques to evaluate the dynamics of tea plantations which can help policymakers to manage the tea estates and underlying changes in land cover.

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Section: Methodsmentioning

confidence: 99%

Monitoring tea plantations during 1990–2022 using multi-temporal satellite data in Assam (India)

2023

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“…Analyzing the crop parameters is critical in choosing soil texture for planting. So, Parida et al 22 have designed the parameter index to measure the biochemical features in the paddy. Several biological parameters were calculated from the trained satellite image database.…”

Section: Related Workmentioning

confidence: 99%

An optimized deep network based soil texture prediction and vegetation analysis using satellite image database

Anand

Jain

Shukla

2023

Concurrency and Computation

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Summary The usage of satellite images has grown rapidly in digital applications. Predicting features in the satellite images using conventional techniques is not an easy task as the images captured from satellites are more complex with highly noisy features. Hence, traditional imaging algorithms cannot analyze the image features and specify the soil textures. In this work, the Tarakeswar satellite images were taken to estimate the soil textures and crops yielding. Hence, to find the soil's chemical properties and suitable crops, the novel bat‐based U‐Net feature prediction system (BUFPS) was developed with the required features. The imported satellite images were initially filtered and entered into the classification phase to forecast the present chemical features and suitable crops in specific soils. After detecting the chemical features, soil textures like sandy, silt, and clay were categorized; the crops like jute, rice, lentil, and potato were considered. Finally, the planned model is executed in the MATLAB environment and has gained outstanding results by achieving the lowest error rate of 8% and a high prediction score of 92%. The proposed model has achieved exact soil texture analysis for complex satellite images. The gained texture analysis outcome is quite better than other existing models.

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Conservation of native tree species in the agroforest of rice-based agroecosystems will contribute to sustainable agriculture

Lopus,

Kushwaha,

Reshma

et al. 2023

Biodivers Conserv

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Traditional agriculture relies on ecosystem services for sustainable food production and is also identi ed as a climatesmart approach. The present study analyses the agroforests associated with the rice farming system of three different agricultural practices for biodiversity richness by comparing two parameters: plants and birds. Out of the 9 study sites, 3 sites were traditional farms maintained by Kurichiya tribal communities, 3 were natural farms, and the other 3 farms were modern. A total of 45 families, 104 genera, 128 species of plants, and 101 bird species belonged to 48 families, and 17 orders were identi ed from the study sites. The sample-size-based rarefaction and extrapolation (R/E) method was adopted to identify estimated biodiversity indices. Renyi pro le was used to understand the native tree diversity pro le of the selected sites. The result of this study indicates that bird diversity is positively correlated with native tree diversity and NDVI of May and October. Conserving more native trees in the farmland could be one of the reasons for the sustainable agriculture system of the Kurichiya tribal community as it attracts more bird species and contributes to the biological control of pests. Thus, the conservation of native tree species in the agroforest of rice-based agroecosystems will contribute to the sustainable agriculture system.

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Synergy of Sentinel-2A and Near-proximal sensor data for deriving biochemical parameters of paddy at different growth stages

Cited by 12 publications

References 48 publications

Monitoring tea plantations during 1990–2022 using multi-temporal satellite data in Assam (India)

Monitoring tea plantations during 1990–2022 using multi-temporal satellite data in Assam (India)

An optimized deep network based soil texture prediction and vegetation analysis using satellite image database

Conservation of native tree species in the agroforest of rice-based agroecosystems will contribute to sustainable agriculture

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