Mapping of sugarcane crop types from multi-date IRS-Resourcesat satellite data by various classification methods and field-level GPS survey

Singh, Ranjeet; Patel, Nitin R.; Danodia, Abhishek

doi:10.1016/j.rsase.2020.100340

Cited by 11 publications

(2 citation statements)

References 19 publications

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“…Moreover, the application of remotely sensed data extends beyond spatial identification, encompassing diverse domains such as crop type characterization [33,34], yield prediction [35,36], estimation of biophysical parameters [37,38], nutritional requirements [39], and disease detection [40].…”

Section: Introductionmentioning

confidence: 99%

In-Depth Analysis and Characterization of a Hazelnut Agro-Industrial Context through the Integration of Multi-Source Satellite Data: A Case Study in the Province of Viterbo, Italy

Lodato,

Pennazza,

Santonico

et al. 2024

Remote Sensing

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The production of “Nocciola Romana” hazelnuts in the province of Viterbo, Italy, has evolved into a highly efficient and profitable agro-industrial system. Our approach is based on a hierarchical framework utilizing aggregated data from multiple temporal data and sources, offering valuable insights into the spatial, temporal, and phenological distributions of hazelnut crops To achieve our goal, we harnessed the power of Google Earth Engine and utilized collections of satellite images from Sentinel-2 and Sentinel-1. By creating a dense stack of multi-temporal images, we precisely mapped hazelnut groves in the area. During the testing phase of our model pipeline, we achieved an F1-score of 99% by employing a Hierarchical Random Forest algorithm and conducting intensive sampling using high-resolution satellite imagery. Additionally, we employed a clustering process to further characterize the identified areas. Through this clustering process, we unveiled distinct regions exhibiting diverse spatial, spectral, and temporal responses. We successfully delineated the actual extent of hazelnut cultivation, totaling 22,780 hectares, in close accordance with national statistics, which reported 23,900 hectares in total and 21,700 hectares in production for the year 2022. In particular, we identified three distinct geographic distribution patterns of hazelnut orchards in the province of Viterbo, confined within the PDO (Protected Designation of Origin)-designated region. The methodology pursued, using three years of aggregate data and one for SAR with a spectral separation clustering hierarchical approach, has effectively allowed the identification of the specific perennial crop, enabling a deeper characterization of various aspects influenced by diverse environmental configurations and agronomic practices.The accurate mapping and characterization of hazelnut crops open opportunities for implementing precision agriculture strategies, thereby promoting sustainability and maximizing yields in this thriving agro-industrial system.

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Section: Introductionmentioning

confidence: 99%

In-Depth Analysis and Characterization of a Hazelnut Agro-Industrial Context through the Integration of Multi-Source Satellite Data: A Case Study in the Province of Viterbo, Italy

Lodato,

Pennazza,

Santonico

et al. 2024

Remote Sensing

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“…Geographically, these studies focused on Brazil [25][26][27][28][29] and China [30][31][32], the first-and third-largest sugarcane producing countries. Recently, a few studies have mapped sugarcane in case-study regions in Uttar Pradesh [33,34], the largest sugarcane producing state located in sub-tropical northern India. However, for Maharashtra, the second-largest sugarcane producing state in tropical central India, there have been only outdated and inaccurate sugarcane maps from global crop mapping studies.…”

Section: Introductionmentioning

confidence: 99%

Mapping Sugarcane in Central India with Smartphone Crowdsourcing

et al. 2022

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In India, the second-largest sugarcane producing country in the world, accurate mapping of sugarcane land is a key to designing targeted agricultural policies. Such a map is not available, however, as it is challenging to reliably identify sugarcane areas using remote sensing due to sugarcane’s phenological characteristics, coupled with a range of cultivation periods for different varieties. To produce a modern sugarcane map for the Bhima Basin in central India, we utilized crowdsourced data and applied supervised machine learning (neural network) and unsupervised classification methods individually and in combination. We highlight four points. First, smartphone crowdsourced data can be used as an alternative ground truth for sugarcane mapping but requires careful correction of potential errors. Second, although the supervised machine learning method performs best for sugarcane mapping, the combined use of both classification methods improves sugarcane mapping precision at the cost of worsening sugarcane recall and missing some actual sugarcane area. Third, machine learning image classification using high-resolution satellite imagery showed significant potential for sugarcane mapping. Fourth, our best estimate of the sugarcane area in the Bhima Basin is twice that shown in government statistics. This study provides useful insights into sugarcane mapping that can improve the approaches taken in other regions.

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AI-Enhanced Remote Sensing Applications in Indian Sugarcane Research: A Comprehensive Review

Vinayaka,

Prasad

2024

Sugar Tech

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Mapping of sugarcane crop types from multi-date IRS-Resourcesat satellite data by various classification methods and field-level GPS survey

Cited by 11 publications

References 19 publications

In-Depth Analysis and Characterization of a Hazelnut Agro-Industrial Context through the Integration of Multi-Source Satellite Data: A Case Study in the Province of Viterbo, Italy

In-Depth Analysis and Characterization of a Hazelnut Agro-Industrial Context through the Integration of Multi-Source Satellite Data: A Case Study in the Province of Viterbo, Italy

Mapping Sugarcane in Central India with Smartphone Crowdsourcing

AI-Enhanced Remote Sensing Applications in Indian Sugarcane Research: A Comprehensive Review

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