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Article

Using Sentinel-1, Sentinel-2, and Planet Imagery to Map Crop Type of Smallholder Farms

1
School for Environment and Sustainability, University of Michigan, Ann Arbor, MI 48109, USA
2
IRRI South Asia Regional Centre (ISARC), NSRTC Campus, Varanasi 221006, India
3
International Maize and Wheat Improvement Center (CIMMYT)-India Office, New Delhi 110012, India
4
Department of Earth System Science and Center on Food Security and the Environment, Stanford University, Stanford, CA 94305, USA
*
Author to whom correspondence should be addressed.
Shared first authorship.
Academic Editors: Krishna Prasad Vadrevu, Chris Justice and Garik Gutman
Remote Sens. 2021, 13(10), 1870; https://doi.org/10.3390/rs13101870
Received: 31 March 2021 / Revised: 4 May 2021 / Accepted: 5 May 2021 / Published: 11 May 2021
Remote sensing offers a way to map crop types across large spatio-temporal scales at low costs. However, mapping crop types is challenging in heterogeneous, smallholder farming systems, such as those in India, where field sizes are often smaller than the resolution of historically available imagery. In this study, we examined the potential of relatively new, high-resolution imagery (Sentinel-1, Sentinel-2, and PlanetScope) to identify four major crop types (maize, mustard, tobacco, and wheat) in eastern India using support vector machine (SVM). We found that a trained SVM model that included all three sensors led to the highest classification accuracy (85%), and the inclusion of Planet data was particularly helpful for classifying crop types for the smallest farms (<600 m2). This was likely because its higher spatial resolution (3 m) could better account for field-level variations in smallholder systems. We also examined the impact of image timing on the classification accuracy, and we found that early-season images did little to improve our models. Overall, we found that readily available Sentinel-1, Sentinel-2, and Planet imagery were able to map crop types at the field-scale with high accuracy in Indian smallholder systems. The findings from this study have important implications for the identification of the most effective ways to map crop types in smallholder systems. View Full-Text
Keywords: smallholder farms; Planet; Sentinel-1; Sentinel-2; crop type; support vector machines (SVM) smallholder farms; Planet; Sentinel-1; Sentinel-2; crop type; support vector machines (SVM)
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MDPI and ACS Style

Rao, P.; Zhou, W.; Bhattarai, N.; Srivastava, A.K.; Singh, B.; Poonia, S.; Lobell, D.B.; Jain, M. Using Sentinel-1, Sentinel-2, and Planet Imagery to Map Crop Type of Smallholder Farms. Remote Sens. 2021, 13, 1870. https://doi.org/10.3390/rs13101870

AMA Style

Rao P, Zhou W, Bhattarai N, Srivastava AK, Singh B, Poonia S, Lobell DB, Jain M. Using Sentinel-1, Sentinel-2, and Planet Imagery to Map Crop Type of Smallholder Farms. Remote Sensing. 2021; 13(10):1870. https://doi.org/10.3390/rs13101870

Chicago/Turabian Style

Rao, Preeti, Weiqi Zhou, Nishan Bhattarai, Amit K. Srivastava, Balwinder Singh, Shishpal Poonia, David B. Lobell, and Meha Jain. 2021. "Using Sentinel-1, Sentinel-2, and Planet Imagery to Map Crop Type of Smallholder Farms" Remote Sensing 13, no. 10: 1870. https://doi.org/10.3390/rs13101870

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