Remote Crop Mapping at Scale: Using Satellite Imagery and UAV-Acquired Data as Ground Truth
Timely and accurate agricultural information is needed to inform resource allocation and sustainable practices to improve food security in the developing world. Obtaining this information through traditional surveys is time consuming and labor intensive, making it difficult to collect data at the fr...
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doaj-bdd1413a88c84a26b47bfd7fdcad56082020-11-25T03:14:47ZengMDPI AGRemote Sensing2072-42922020-06-01121984198410.3390/rs12121984Remote Crop Mapping at Scale: Using Satellite Imagery and UAV-Acquired Data as Ground TruthMeghan Hegarty-Craver0Jason Polly1Margaret O’Neil2Noel Ujeneza3James Rineer4Robert H. Beach5Daniel Lapidus6Dorota S. Temple7RTI International, Research Triangle Park, NC 27709, USARTI International, Research Triangle Park, NC 27709, USARTI International, Research Triangle Park, NC 27709, USAIndependent Agri-Consultant, Kigali 20093, RwandaRTI International, Research Triangle Park, NC 27709, USARTI International, Research Triangle Park, NC 27709, USARTI International, Research Triangle Park, NC 27709, USARTI International, Research Triangle Park, NC 27709, USATimely and accurate agricultural information is needed to inform resource allocation and sustainable practices to improve food security in the developing world. Obtaining this information through traditional surveys is time consuming and labor intensive, making it difficult to collect data at the frequency and resolution needed to accurately estimate the planted areas of key crops and their distribution during the growing season. Remote sensing technologies can be leveraged to provide consistent, cost-effective, and spatially disaggregated data at high temporal frequency. In this study, we used imagery acquired from unmanned aerial vehicles to create a high-fidelity ground-truth dataset that included examples of large mono-cropped fields, small intercropped fields, and natural vegetation. The imagery was acquired in three rounds of flights at six sites in different agro-ecological zones to capture growing conditions. This dataset was used to train and test a random forest model that was implemented in Google Earth Engine for classifying cropped land using freely available Sentinel-1 and -2 data. This model achieved an overall accuracy of 83%, and a 91% accuracy for maize specifically. The model results were compared with Rwanda’s Seasonal Agricultural Survey, which highlighted biases in the dataset including a lack of examples of mixed land cover.https://www.mdpi.com/2072-4292/12/12/1984Sentinel-1Sentinel-2UAVGoogle Earth Enginesub-Saharan Africa |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Meghan Hegarty-Craver Jason Polly Margaret O’Neil Noel Ujeneza James Rineer Robert H. Beach Daniel Lapidus Dorota S. Temple |
spellingShingle |
Meghan Hegarty-Craver Jason Polly Margaret O’Neil Noel Ujeneza James Rineer Robert H. Beach Daniel Lapidus Dorota S. Temple Remote Crop Mapping at Scale: Using Satellite Imagery and UAV-Acquired Data as Ground Truth Remote Sensing Sentinel-1 Sentinel-2 UAV Google Earth Engine sub-Saharan Africa |
author_facet |
Meghan Hegarty-Craver Jason Polly Margaret O’Neil Noel Ujeneza James Rineer Robert H. Beach Daniel Lapidus Dorota S. Temple |
author_sort |
Meghan Hegarty-Craver |
title |
Remote Crop Mapping at Scale: Using Satellite Imagery and UAV-Acquired Data as Ground Truth |
title_short |
Remote Crop Mapping at Scale: Using Satellite Imagery and UAV-Acquired Data as Ground Truth |
title_full |
Remote Crop Mapping at Scale: Using Satellite Imagery and UAV-Acquired Data as Ground Truth |
title_fullStr |
Remote Crop Mapping at Scale: Using Satellite Imagery and UAV-Acquired Data as Ground Truth |
title_full_unstemmed |
Remote Crop Mapping at Scale: Using Satellite Imagery and UAV-Acquired Data as Ground Truth |
title_sort |
remote crop mapping at scale: using satellite imagery and uav-acquired data as ground truth |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2020-06-01 |
description |
Timely and accurate agricultural information is needed to inform resource allocation and sustainable practices to improve food security in the developing world. Obtaining this information through traditional surveys is time consuming and labor intensive, making it difficult to collect data at the frequency and resolution needed to accurately estimate the planted areas of key crops and their distribution during the growing season. Remote sensing technologies can be leveraged to provide consistent, cost-effective, and spatially disaggregated data at high temporal frequency. In this study, we used imagery acquired from unmanned aerial vehicles to create a high-fidelity ground-truth dataset that included examples of large mono-cropped fields, small intercropped fields, and natural vegetation. The imagery was acquired in three rounds of flights at six sites in different agro-ecological zones to capture growing conditions. This dataset was used to train and test a random forest model that was implemented in Google Earth Engine for classifying cropped land using freely available Sentinel-1 and -2 data. This model achieved an overall accuracy of 83%, and a 91% accuracy for maize specifically. The model results were compared with Rwanda’s Seasonal Agricultural Survey, which highlighted biases in the dataset including a lack of examples of mixed land cover. |
topic |
Sentinel-1 Sentinel-2 UAV Google Earth Engine sub-Saharan Africa |
url |
https://www.mdpi.com/2072-4292/12/12/1984 |
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