Using Sentinel-1, Sentinel-2, and Planet imagery to map crop type of smallholder farms
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.
Main Authors: | , , , , , , , |
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Format: | Article biblioteca |
Language: | English |
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MDPI
2021
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Subjects: | AGRICULTURAL SCIENCES AND BIOTECHNOLOGY, Smallholder Farms, Planet, Sentinel-1, Sentinel-2, Crop Type, Support Vector Machines, SMALLHOLDERS, SATELLITE IMAGERY, CROPS, |
Online Access: | https://hdl.handle.net/10883/21554 |
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dig-cimmyt-10883-215542024-01-09T16:25:22Z Using Sentinel-1, Sentinel-2, and Planet imagery to map crop type of smallholder farms Preeti Rao Weiqi Zhou Bhattarai, N. Srivastava, A.K. Singh, B. Poonia, S.P. Lobell, D.B. Meha Jain AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Smallholder Farms Planet Sentinel-1 Sentinel-2 Crop Type Support Vector Machines SMALLHOLDERS SATELLITE IMAGERY CROPS 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. 2021-06-18T00:25:14Z 2021-06-18T00:25:14Z 2021 Article Published Version https://hdl.handle.net/10883/21554 10.3390/rs13101870 English https://www.mdpi.com/2072-4292/13/10/1870#supplementary CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose Open Access Basel (Switzerland) MDPI 10 13 2072-4292 Remote Sensing 1870 |
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AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Smallholder Farms Planet Sentinel-1 Sentinel-2 Crop Type Support Vector Machines SMALLHOLDERS SATELLITE IMAGERY CROPS AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Smallholder Farms Planet Sentinel-1 Sentinel-2 Crop Type Support Vector Machines SMALLHOLDERS SATELLITE IMAGERY CROPS |
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AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Smallholder Farms Planet Sentinel-1 Sentinel-2 Crop Type Support Vector Machines SMALLHOLDERS SATELLITE IMAGERY CROPS AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Smallholder Farms Planet Sentinel-1 Sentinel-2 Crop Type Support Vector Machines SMALLHOLDERS SATELLITE IMAGERY CROPS Preeti Rao Weiqi Zhou Bhattarai, N. Srivastava, A.K. Singh, B. Poonia, S.P. Lobell, D.B. Meha Jain Using Sentinel-1, Sentinel-2, and Planet imagery to map crop type of smallholder farms |
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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. |
format |
Article |
topic_facet |
AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Smallholder Farms Planet Sentinel-1 Sentinel-2 Crop Type Support Vector Machines SMALLHOLDERS SATELLITE IMAGERY CROPS |
author |
Preeti Rao Weiqi Zhou Bhattarai, N. Srivastava, A.K. Singh, B. Poonia, S.P. Lobell, D.B. Meha Jain |
author_facet |
Preeti Rao Weiqi Zhou Bhattarai, N. Srivastava, A.K. Singh, B. Poonia, S.P. Lobell, D.B. Meha Jain |
author_sort |
Preeti Rao |
title |
Using Sentinel-1, Sentinel-2, and Planet imagery to map crop type of smallholder farms |
title_short |
Using Sentinel-1, Sentinel-2, and Planet imagery to map crop type of smallholder farms |
title_full |
Using Sentinel-1, Sentinel-2, and Planet imagery to map crop type of smallholder farms |
title_fullStr |
Using Sentinel-1, Sentinel-2, and Planet imagery to map crop type of smallholder farms |
title_full_unstemmed |
Using Sentinel-1, Sentinel-2, and Planet imagery to map crop type of smallholder farms |
title_sort |
using sentinel-1, sentinel-2, and planet imagery to map crop type of smallholder farms |
publisher |
MDPI |
publishDate |
2021 |
url |
https://hdl.handle.net/10883/21554 |
work_keys_str_mv |
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