Abstract
Crop type information is essential for many practical applications, yet its mapping is often constrained by inherent characteristics of most farming areas, such as fragmentation and small farm plots, changes in crop morphology across the season, and cloud cover. This study investigated whether these limitations could be overcome by using time-series of Synthetic Aperture Radar (SAR) and crop phenological information combined with different Dynamic Time Warping implementation strategies. Focusing on a fragmented landscape with small farm plots in the Netherlands, we used Sentinel-1 dual polarimetry (VV + VH) and TerraSAR-X single polarimetry (HH) images and the Dutch Basic Registration of Crop Plots (BRP) dataset for training and validation. Image pre-processing was followed by the generation of radar vegetation indices and polarimetric decomposition. Crop-specific responses to incident radar signal were analyzed, as well as the accuracy of the crop classification by Time-Weighted Dynamic Time Warping (twDTW) using either backscatter bands only or backscatter bands in combination with derived indices and polarimetric decomposition features. In addition, two further modified Dynamic Time Warping strategies, namely Variable Time Weight Dynamic Time Warping (vtwDTW) and Angular Metric for Shape Similarity (AMSS), were tested for their performance. Furthermore, we investigated the accuracy performance of a decision level fusion of TerraSAR-X and Sentinel-1 classification outputs. Results show that even in a fragmented landscape with relatively small plots (around 0.08 ha), crop types can be successfully mapped by using decision level fusion of the twDTW results of both sensors, reaching an accuracy of 77.1%. When using twDTW on Sentinel-1 (VV + VH) only, including Ratio, MRVI, and DPSVI indices, overall accuracy reached 69.5%; without those indices, accuracy was slightly lower (67.5%). Merging six different grain crops with similar leaf geometry into winter grains and summer grains improved classification accuracy to 80.6%. Our findings demonstrate that twDTW on SAR imagery allows to map crop types in fragmented landscapes with relatively small farm plots, offering potential for crop type mapping in areas with smallholder farming.
Highlights
Accurate information on crop production is essential for many ap plications
Even in a fragmented landscape with relatively small plots, crop types can be effectively mapped using Time-Weighted Dynamic Time Warping (twDTW) on Sentinel-1 (VV and VH) data and on the fused outputs of Sentinel-1 (VV and VH) and TerraSAR-X (HH), achieving accuracies of up to 77.1%
Our results for twDTW on the fused Synthetic Aperture Radar (SAR) datasets show lower accuracies than those reported by previous studies using twDTW on optical data, we consider twDTW on SAR output a viable alternative for areas with persistent cloud cover
Summary
Accurate information on crop production is essential for many ap plications. For example, annual information on crop types and areas grown is needed for decision making in food production and food se curity (Samberg et al, 2016), characterization of cropping intensity (Jain et al, 2013), soil and water resources research, especially erosion modelling (Panagos et al, 2015), generation of cost-effective informa tion about agricultural production (Carfagna and Gallego, 2005) and estimation of crop damage for insurance payout (ESA, 2017).Mapping this information based on single-date satellite imagery can be unsatisfactory, especially in complex farming areas (e.g. Delrue et al., 2013). Annual information on crop types and areas grown is needed for decision making in food production and food se curity (Samberg et al, 2016), characterization of cropping intensity (Jain et al, 2013), soil and water resources research, especially erosion modelling (Panagos et al, 2015), generation of cost-effective informa tion about agricultural production (Carfagna and Gallego, 2005) and estimation of crop damage for insurance payout (ESA, 2017) Mapping this information based on single-date satellite imagery can be unsatisfactory, especially in complex farming areas In the last few years, the domain of optical remote sensing has made good progress towards time-series analysis, including the formulation of automatic information retrieval procedures in smallholder farming areas (Stratoulias et al, 2017), parcel boundary delineation in complex farming systems (Per sello et al, 2019), extraction of cropland extent (Oliphant et al, 2019; Useya et al, 2019; Mohammed et al, 2020), crop types mapping (Belgiu and Csillik, 2018) and parcel level classification of crops (Aguilar et al, 2018; Sonobe et al, 2018).
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: ISPRS Journal of Photogrammetry and Remote Sensing
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
