Crop Intensity Mapping Using Dynamic Time Warping and Machine Learning from Multi-Temporal PlanetScope Data

Raihan Rafif, Sandiaga Swahyu Kusuma, Siti Saringatin, Giara Iman Nanda, Pramaditya Wicaksono and Sanjiwana Arjasakusuma
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Raihan Rafif: Department of Geographic Information Science, Faculty of Geography, Universitas Gadjah Mada, Yogyakarta 55223, Indonesia
Sandiaga Swahyu Kusuma: Department of Geographic Information Science, Faculty of Geography, Universitas Gadjah Mada, Yogyakarta 55223, Indonesia
Siti Saringatin: Department of Geographic Information Science, Faculty of Geography, Universitas Gadjah Mada, Yogyakarta 55223, Indonesia
Giara Iman Nanda: Department of Geographic Information Science, Faculty of Geography, Universitas Gadjah Mada, Yogyakarta 55223, Indonesia
Pramaditya Wicaksono: Department of Geographic Information Science, Faculty of Geography, Universitas Gadjah Mada, Yogyakarta 55223, Indonesia
Sanjiwana Arjasakusuma: Department of Geographic Information Science, Faculty of Geography, Universitas Gadjah Mada, Yogyakarta 55223, Indonesia

Land, 2021, vol. 10, issue 12, 1-18

Abstract: Crop intensity information describes the productivity and the sustainability of agricultural land. This information can be used to determine which agricultural lands should be prioritized for intensification or protection. Time-series data from remote sensing can be used to derive the crop intensity information; however, this application is limited when using medium to coarse resolution data. This study aims to use 3.7 m-PlanetScope™ Dove constellation data, which provides daily observations, to map crop intensity information for agricultural land in Magelang District, Indonesia. Two-stage histogram matching, before and after the monthly median composites, is used to normalize the PlanetScope data and to generate monthly data to map crop intensity information. Several methods including Time-Weighted Dynamic Time Warping (TWDTW) and the machine-learning algorithms: Random Forest (RF), Extremely Randomized Trees (ET), and Extreme Gradient Boosting (XGB) are employed in this study, and the results are validated using field survey data. Our results show that XGB generated the highest overall accuracy (OA) (95 ± 4%), followed by RF (92 ± 5%), ET (87 ± 6%), and TWDTW (81 ± 8%), for mapping four-classes of cropping intensity, with the near-infrared (NIR) band being the most important variable for identifying cropping intensity. This study demonstrates the potential of PlanetScope data for the production of cropping intensity maps at detailed resolutions.

Keywords: crop intensity; classification; extreme gradient boosting; random forests; extremely randomized trees (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2021
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