A Phenology-Based Method to Map Cropping Patterns under a Wheat-Maize Rotation Using Remotely Sensed Time-Series Data

Jian-hong, Liu; Zhu, Wenquan; Atzberger, Clement; Zhao, Anzhou; Pan, Yaozhong; Huang, Xin

doi:10.3390/rs10081203

Cited by 40 publications

(39 citation statements)

References 73 publications

(65 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In contrast, satellite-based observation can offer a practical way to acquire crop information over a large area. With excellent spatial coverage and short revisit times, optical remote sensing data sources such as the Advanced Very-High-Resolution Radiometer (AVHRR), Medium-Resolution Imaging Spectrometer Instrument (MERIS), and Moderate-Resolution Imaging Spectroradiometer (MODIS) have been widely used for regional-scale to global-scale vegetation trend analysis [11][12][13]. Meng et al [14] developed a method for detecting the key phenological stages of winter wheat in the North China Plain from MERIS time series.…”

Section: Introductionmentioning

confidence: 99%

Mapping Winter Wheat Planting Area and Monitoring Its Phenology Using Sentinel-1 Backscatter Time Series

Song

Wang

2019

Remote Sensing

View full text Add to dashboard Cite

Crop planting area mapping and phenology monitoring are of great importance to analyzing the impacts of climate change on agricultural production. In this study, crop planting area and phenology were identified based on Sentinel-1 backscatter time series in the test region of the North China Plain, East Asia, which has a stable cropping pattern and similar phenological stages across the region. Ground phenological observations acquired from a typical agro-meteorological station were used as a priori knowledge. A parallelepiped classifier processed VH (vertical transmitting, horizontal receiving) and VV (vertical transmitting, vertical receiving) backscatter signals in order to map the winter wheat planting area. An accuracy assessment showed that the total classification accuracy reached 84% and the Kappa coefficient was 0.77. Both the difference ( σ d ) between VH and VV and its slope were obtained to contrast with a priori knowledge and then used to extract the phenological metrics. Our findings from the analysis of the time series showed that the seedling, tillering, overwintering, jointing, and heading of winter wheat may be closely related to σ d and its slope. Overall, this study presents a generalizable methodology for mapping the winter wheat planting area and monitoring phenology using Sentinel-1 backscatter time series, especially in areas lacking optical remote sensing data. Our results suggest that the main change in Sentinel-1 backscatter is dominated by the vegetation canopy structure, which is different from the established methods using optical remote sensing data, and it is available for phenological metrics extraction.

show abstract

Section: Introductionmentioning

confidence: 99%

Mapping Winter Wheat Planting Area and Monitoring Its Phenology Using Sentinel-1 Backscatter Time Series

Song

Wang

2019

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Optical remote sensing is one of the most attractive options; in particular, Landsat series data have potential in land characterization applications due to their spatial, spectral, and radiometric qualities [2][3][4][5]. Furthermore, the Sentinel-2 satellites have contributed to create greater opportunities for monitoring plant constituents, such as pigments, leaf water contents, and biochemicals [6,7], and the vegetation indices calculated from Sentinel-2 Multispectral Instrument (MSI) data were useful to identify the specific crop types [8,9].…”

Section: Introductionmentioning

confidence: 99%

Parcel-Based Crop Classification Using Multi-Temporal TerraSAR-X Dual Polarimetric Data

Sonobe

2019

Remote Sensing

View full text Add to dashboard Cite

Cropland maps are useful for the management of agricultural fields and the estimation of harvest yield. Some local governments have documented field properties, including crop type and location, based on site investigations. This process, which is generally done manually, is labor-intensive, and remote-sensing techniques can be used as alternatives. In this study, eight crop types (beans, beetroot, grass, maize, potatoes, squash, winter wheat, and yams) were identified using gamma naught values and polarimetric parameters calculated from TerraSAR-X (or TanDEM-X) dual-polarimetric (HH/VV) data. Three indices (difference (D-type), simple ratio (SR), and normalized difference (ND)) were calculated using gamma naught values and m-chi decomposition parameters and were evaluated in terms of crop classification. We also evaluated the classification accuracy of four widely used machine-learning algorithms (kernel-based extreme learning machine, support vector machine, multilayer feedforward neural network (FNN), and random forest) and two multiple-kernel methods (multiple kernel extreme learning machine (MKELM) and multiple kernel learning (MKL)). MKL performed best, achieving an overall accuracy of 92.1%, and proved useful for the identification of crops with small sample sizes. The difference (raw or normalized) between double-bounce scattering and odd-bounce scattering helped to improve the identification of squash and yams fields.

show abstract

“…Winter wheat overwinters from late December to the next February and greens up in late February. In spring, winter wheat starts growing rapidly and is generally harvested in mid-June [46]. 1 Green color represents sowing, light green represents the development of winter wheat, gray represents over-winter, and orange represents harvesting.…”

Section: Winter Wheat Crop Calendarsmentioning

confidence: 99%

An Improved Approach Considering Intraclass Variability for Mapping Winter Wheat Using Multitemporal MODIS EVI Images

et al. 2019

View full text Add to dashboard Cite

Winter wheat is one of the major cereal crops in the world. Monitoring and mapping its spatial distribution has significant implications for agriculture management, water resources utilization, and food security. Generally, winter wheat has distinguished phenological stages during the growing season, which form a unique EVI (Enhanced Vegetation Index) time series curve and differ considerably from other crop types and natural vegetation. Since early 2000, the MODIS EVI product has become the primary dataset for satellite-based crop monitoring at large scales due to its high temporal resolution, huge observation scope, and timely availability. However, the intraclass variability of winter wheat caused by field conditions and agricultural practices might lower the mapping accuracy, which has received little attention in previous studies. Here, we present a winter wheat mapping approach that integrates the variables derived from the MODIS EVI time series taking into account intraclass variability. We applied this approach to two winter wheat concentration areas, the state of Kansas in the U.S. and the North China Plain region (NCP). The results were evaluated against crop-specific maps or statistical data at the state/regional level, county level, and site level. Compared with statistical data, the accuracies in Kansas and the NCP were 95.1% and 92.9% at the state/regional level with R2 (Coefficient of Determination) values of 0.96 and 0.71 at the county level, respectively. Overall accuracies in confusion matrix were evaluated by validation samples in both Kansas (90.3%) and the NCP (85.0%) at the site level. Comparisons with methods without considering intraclass variability demonstrated that winter wheat mapping accuracies were improved by 17% in Kansas and 15% in the NCP using the improved approach. Further analysis indicated that our approach performed better in areas with lower landscape fragmentation, which may partly explain the relatively higher accuracy of winter wheat mapping in Kansas. This study provides a new perspective for generating multiple subclasses as training inputs to decrease the intraclass differences for crop type detection based on the MODIS EVI time series. This approach provides a flexible framework with few variables and fewer training samples that could facilitate its application to multiple-crop-type mapping at large scales.

show abstract

A Phenology-Based Method to Map Cropping Patterns under a Wheat-Maize Rotation Using Remotely Sensed Time-Series Data

Cited by 40 publications

References 73 publications

Mapping Winter Wheat Planting Area and Monitoring Its Phenology Using Sentinel-1 Backscatter Time Series

Mapping Winter Wheat Planting Area and Monitoring Its Phenology Using Sentinel-1 Backscatter Time Series

Parcel-Based Crop Classification Using Multi-Temporal TerraSAR-X Dual Polarimetric Data

An Improved Approach Considering Intraclass Variability for Mapping Winter Wheat Using Multitemporal MODIS EVI Images

Contact Info

Product

Resources

About