Crop Classification and Representative Crop Rotation Identifying Using Statistical Features of Time-Series Sentinel-1 GRD Data

Abstract

Compared with a monoculture planting mode, the practice of crop rotations improves fertilizer efficiency and increases crop yield. Large-scale crop rotation monitoring relies on the results of crop classification using remote sensing technology. However, the limited crop classification accuracy cannot satisfy the accurate identification of crop rotation patterns. In this paper, a crop classification and rotation mapping scheme combining the random forest (RF) algorithm and new statistical features extracted from time-series ground range direction (GRD) Sentinel-1 images. First, the synthetic aperture radar (SAR) time-series stacks are established, including VH, VV, and VH/VV channels. Then, new statistical features named the objected generalized gamma distribution (OGΓD) features are introduced to compare with other object-based features for each polarization. The results showed that the OGΓD σVH achieved 96.66% of the overall accuracy (OA) and 95.34% of the Kappa, improving around 4% and 6% compared with the object-based backscatter in VH polarization, respectively. Finally, annual crop-type maps for five consecutive years (2017–2021) are generated using the OGΓD σVH and the RF. By analyzing the five-year crop sequences, the soybean-corn (corn-soybean) is the most representative rotation in the study region, and the soybean-corn-soybean-corn-soybean (together with corn-soybean-corn-soybean-corn) has the highest count with 100 occurrences (25.20% of the total area). This study offers new insights into crop rotation monitoring, giving the basic data for government food planning decision-making.

Early-Season Crop Classification Based on Local Window Attention Transformer with Time-Series RCM and Sentinel-1

“…Here, the early season is defined as the end of May to early July within six to eight weeks after the spring seeding of corn and soybean. In contrast, winter wheat in this study site was planted in October of the previous year and harvested in July of the following year [7,32]. Thus, the early season is only for corn and soybean in this study.…”

“…Although the ESA announced the revisit time of using two satellites is 6 days, only Sentinel-1A was working in our study site, and Sentinel-1B's mission ended on 3 August 2022 due to an anomaly related to the instrument electronics power [35]. Our study area is located in an overlap of two orbits (orbit number 77 with an incidence angle of 31.5 • and orbit number 150 with an incidence angle of 41.5 • ) of Sentinel-1, causing the data access frequency to be less than 12 days [7]. Six scenes of RCM data were provided by the CSA, spanning from 29 May to 12 July 2022.…”

“…Crop monitoring is challenging on regional and national scales due to the uncertainty of meteorological conditions and local land management decisions over growing seasons [1][2][3]. In this context, crop classification based on remote sensing plays a pivotal role in providing foundational data for crop monitoring and diverse agricultural applications [4][5][6][7]. Particularly, the practice of early-season crop mapping, involving the identification of crop types during the initial stages or before harvest, has garnered significant demand from businesses involved in crop insurance, land rental, supply-chain logistics, and commodity markets [8,9].…”

Understanding the Temporal Dynamics of Coherence and Backscattering Using Sentinel-1 Imagery for Crop-Type Mapping

“…In recent years, the utility of SAR in the realm of agriculture has been substantiated, encompassing a spectrum of applications including crop monitoring [12], [13], [14], crop classification [15], [16], [17], crop rotation identification [18], crop change detection [19] and so on. These extensive research efforts and applications indicate that SAR imagery not only provides surface information but also rich internal structural details.…”

Synthetic Aperture Radar Remote Sensing for Crop Classification

“…High resolution images have advanced discrimination capabilities, but obtaining time series datasets that cover the entire growth cycles of crops is challenging [3][4][5][6][7]. This is due to the dependence of optical satellite image acquisition on atmospheric conditions, which often results in contaminated and cloud masked images, particularly in Kharif crops and fruit trees [12][13][14][15][16][26][27][28][29]. Synthetic Aperture Radar data sets offer an advantage in this regard.…”