Rice-Fallow Targeting for Cropping Intensification through Geospatial Technologies in the Rice Belt of Northeast India
Abstract
1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data
2.2.1. Ground Data
2.2.2. Earth Observation Data
2.3. Mapping
2.3.1. Rice Area Mapping
2.3.2. Rice-Fallow Mapping
2.3.3. Delineating Suitable Rice-Fallow Areas Based on Soil Moisture
2.4. Targeting Suitable Rice-Fallow Areas
3. Results
3.1. Variables Governing the Existence of Rice-Fallows
3.2. Rice Area Mapping
3.3. Rice-Fallow Mapping
3.4. Soil Moisture Suitability Analysis
3.5. Accuracy Assessment
3.6. Targeting Suitable Rice-Fallow Areas through Agronomic Interventions
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Correction Statement
References
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Data | Resolution (m) | Period of Acquisition | Year | No. of Tiles | Source | Description |
---|---|---|---|---|---|---|
Landsat 8 OLI | 30 | 1st November–30th April | 2018–2019 2019–2020 2020–2021 | 11 | NASA | Cropping systems mapping |
Sentinel-1 | 10 | 15th June–15th December | 2018 2019 2020 | 3 | ESA | Rice area mapping |
SMAP | 9000 | 1st November–30th April | 2018–2019 2019–2020 2020–2021 | 1 | NASA | Soil moisture suitability |
Random Forest (RF) | Neural Network (NN) | Support Vector Machine (SVM) | |||
---|---|---|---|---|---|
Parameter | Value | Parameter | Value | Parameter | Value |
ntree | 500 | Size | 5, 10, 15 | Cost | 0.2, 0,5, 0.1 |
mtry | 1, 2, 5 | Decay | 0.001, 0.01, 0.1 |
Model | Accuracy | Kappa |
---|---|---|
Random Forest (RF) | 0.897 | 0.789 |
Neural Network (NN) | 0.8031 | 0.597 |
Support Vector Machine (SVM) | 0.735 | 0.417 |
Outputs Generated | Year | User’s Accuracy (%) | Producer’s Accuracy (%) | Overall Accuracy (%) | Kappa |
---|---|---|---|---|---|
Rice | 2018–2019 | 95.3 | 81.8 | 91.9 | 0.83 |
2019–2020 | 85.7 | 86.0 | 86.0 | 0.72 | |
2020–2021 | 90.2 | 94.0 | 92.0 | 0.84 | |
Rice-fallow | 2018–2019 | 86.1 | 97.1 | 94.9 | 0.90 |
2019–2020 | 87.7 | 85.8 | 90.0 | 0.80 | |
2020–2021 | 93.0 | 93.8 | 92.9 | 0.85 |
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Srivastava, A.K.; Borah, S.B.; Ghosh Dastidar, P.; Sharma, A.; Gogoi, D.; Goswami, P.; Deka, G.; Khandai, S.; Borgohain, R.; Singh, S.; et al. Rice-Fallow Targeting for Cropping Intensification through Geospatial Technologies in the Rice Belt of Northeast India. Agriculture 2023, 13, 1509. https://doi.org/10.3390/agriculture13081509
Srivastava AK, Borah SB, Ghosh Dastidar P, Sharma A, Gogoi D, Goswami P, Deka G, Khandai S, Borgohain R, Singh S, et al. Rice-Fallow Targeting for Cropping Intensification through Geospatial Technologies in the Rice Belt of Northeast India. Agriculture. 2023; 13(8):1509. https://doi.org/10.3390/agriculture13081509
Chicago/Turabian StyleSrivastava, Amit Kumar, Suranjana Bhaswati Borah, Payel Ghosh Dastidar, Archita Sharma, Debabrat Gogoi, Priyanuz Goswami, Giti Deka, Suryakanta Khandai, Rupam Borgohain, Sudhanshu Singh, and et al. 2023. "Rice-Fallow Targeting for Cropping Intensification through Geospatial Technologies in the Rice Belt of Northeast India" Agriculture 13, no. 8: 1509. https://doi.org/10.3390/agriculture13081509
APA StyleSrivastava, A. K., Borah, S. B., Ghosh Dastidar, P., Sharma, A., Gogoi, D., Goswami, P., Deka, G., Khandai, S., Borgohain, R., Singh, S., & Bhattacharyya, A. (2023). Rice-Fallow Targeting for Cropping Intensification through Geospatial Technologies in the Rice Belt of Northeast India. Agriculture, 13(8), 1509. https://doi.org/10.3390/agriculture13081509