Analysis of sentinel-1 data for regional crop classification: a  multi-data approach for rabi crops of district Hisar (Haryana)

Ahlawat, Isha; Sheoran, Hardeep Singh; Roohi, .; Dahiya, Garima; Sihag, Parveen

doi:10.31018/jans.vi.2263

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…High temporal resolution microwave sensors such as RADARSAT-2, Sentinel-1, ERS and SkyMed have been used to map single cropping both independently and integrated with optical sensors [70][71][72][73][74][75][76][77][78]. Sentinel-1 sensors have also been used for regional mapping of paddy rice in Asia [79][80][81], while Ramadhani et al [82] integrated microwave data with Sentinel-2 and MODIS MOD13Q1 to map rice in Africa regionally.…”

Section: Single Cropping Mappingmentioning

confidence: 99%

Cropping Patterns of Annual Crops: A Remote Sensing Review

2022

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Cropping patterns are defined as the sequence and spatial arrangement of annual crops on a piece of land. Knowledge of cropping patterns is crucial for crop production and land-use intensity. While cropping patterns are related to crop production and land use intensity, they are rarely reported in agricultural statistics, especially those relating to small farms in developing countries. Remote sensing has enabled mapping cropping patterns by monitoring crops’ spatial and temporal dynamics. In this paper, we reviewed remote sensing studies of single, sequential and intercropping patterns of annual crops practiced at local and regional scales. A total of 89 studies were selected from 753 publications based on their cropping pattern types and relevance to the scope of this review. The review found that despite the increase in single cropping pattern studies due to the Sentinel missions, studies on intercropping patterns are rare, suggesting that mapping intercropping is still challenging. More so, microwave remote sensing for mapping intercropping has not been fully explored. Given the complexities in mapping intercropping, our review highlights how less frequently used vegetation indices (VIs) that benefit from red-edge and SWIR spectral bands may improve intercropping mapping.

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Section: Single Cropping Mappingmentioning

confidence: 99%

Cropping Patterns of Annual Crops: A Remote Sensing Review

2022

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Machine Learning Techniques for Phenology Assessment of Sugarcane Using Conjunctive SAR and Optical Data

et al. 2022

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Crop phenology monitoring is a necessary action for precision agriculture. Sentinel-1 and Sentinel-2 satellites provide us with the opportunity to monitor crop phenology at a high spatial resolution with high accuracy. The main objective of this study was to examine the potential of the Sentinel-1 and Sentinel-2 data and their combination for monitoring sugarcane phenological stages and evaluate the temporal behaviour of Sentinel-1 parameters and Sentinel-2 indices. Seven machine learning models, namely logistic regression, decision tree, random forest, artificial neural network, support vector machine, naïve Bayes, and fuzzy rule based systems, were implemented, and their predictive performance was compared. Accuracy, precision, specificity, sensitivity or recall, F score, area under curve of receiver operating characteristic and kappa value were used as performance metrics. The research was carried out in the Indo-Gangetic alluvial plains in the districts of Hisar and Jind, Haryana, India. The Sentinel-1 backscatters and parameters VV, alpha and anisotropy and, among Sentinel-2 indices, normalized difference vegetation index and weighted difference vegetation index were found to be the most important features for predicting sugarcane phenology. The accuracy of models ranged from 40 to 60%, 56 to 84% and 76 to 88% for Sentinel-1 data, Sentinel-2 data and combined data, respectively. Area under the ROC curve and kappa values also supported the supremacy of the combined use of Sentinel-1 and Sentinel-2 data. This study infers that combined Sentinel-1 and Sentinel-2 data are more efficient in predicting sugarcane phenology than Sentinel-1 and Sentinel-2 alone.

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Analysis of sentinel-1 data for regional crop classification: a multi-data approach for rabi crops of district Hisar (Haryana)

Cited by 2 publications

References 8 publications

Cropping Patterns of Annual Crops: A Remote Sensing Review

Cropping Patterns of Annual Crops: A Remote Sensing Review

Machine Learning Techniques for Phenology Assessment of Sugarcane Using Conjunctive SAR and Optical Data

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