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

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.

New Generation Hyperspectral Data From DESIS Compared to High Spatial Resolution PlanetScope Data for Crop Type Classification

“…There are ways to correct for these inconsistencies. For example, Rafif et al [51] used histogram matching (with R [52] packages "raster" [53] and "RStoolbox" [54]) to correct for varying radiometric ranges across PlanetScope images. Images can also be radiometrically corrected through a linear shift from comparison with Landsat or Sentinel data, with the Multivariate Alteration Detection (MAD), or the Cubesat Enabled Spatio-Temporal Enhancement Method (CESTEM) [49].…”

“…The high temporal resolution of PlanetScope data also allowed for monitoring of algal bloom events and small changes in water quality [55]. In addition, PlanetScope images have been used for several agricultural applications, including monitoring crop growth, management, health, and productivity [48], [50], [51], [56]- [62].…”

Evaluating the Potential of 8 Band Planetscope Dataset for Crop Classification Using Random Forest and Gradient Tree Boosting by Google Earth Engine

“…A study has been carried out to compare PlanetScope dataset with Sentinel-2 dataset by (Mudereri et al 2019) for mapping Striga weed in Kenya and results shows that PlanetScope dataset is more accurate in mapping as compared to Sentinel-2 dataset. Further, PlanetScope dataset has a great potential for producing the crops intensity maps at detailed resolutions (Rafif et al 2021). The study used the 4 band data for mapping four different types of crop classes and found that NIR band to be most extensive for analysing and evaluating the cropping intensity.…”

A Light-Weight Cropland Mapping Model Using Satellite Imagery

“…Croplands mapping and monitoring is a widely used application of remote sensing given its evident advantages of spatial coverage, cost effectiveness, monitoring ability for its revisiting frequency, and its ability to detect crops stress [ 7 , 8 , 9 , 10 , 11 , 12 , 13 ]. Crop intensity mapping, on the other hand, provides important information on the changes in and productivity of agricultural land [ 14 ]. Specifically, crop intensity mapping refers to the segmentation of agricultural land according to the number of crop planting cycles it exhibits, with cycle numbers ranging from 0 to 3, where 0 refers to no crops occurring, and 3 refers to the case in which the same land experienced 3 complete growth cycles of its crops in one year [ 15 ], [ 16 ].…”

“…Specifically, crop intensity mapping refers to the segmentation of agricultural land according to the number of crop planting cycles it exhibits, with cycle numbers ranging from 0 to 3, where 0 refers to no crops occurring, and 3 refers to the case in which the same land experienced 3 complete growth cycles of its crops in one year [ 15 ], [ 16 ]. Such a task plays an important role in improving food production, and agricultural planning, through surveying cropland changes [ 14 , 17 ].…”