This study developed an approach for remote estimation of Vegetation Fraction (VF) and Flower Fraction (FF) in oilseed rape, which is a crop species with conspicuous flowers during reproduction. Canopy reflectance in green, red, red edge and NIR bands was obtained by a camera system mounted on an unmanned aerial vehicle (UAV) when oilseed rape was in the vegetative growth and flowering stage. The relationship of several widely-used Vegetation Indices (VI) vs. VF was tested and found to be different in different phenology stages. At the same VF when oilseed rape was flowering, canopy reflectance increased in all bands, and the tested VI decreased. Therefore, two algorithms to estimate VF were calibrated respectively, one for samples during vegetative growth and the other for samples during flowering stage. The results showed that the Visible Atmospherically Resistant Index (VARI_green) worked most accurately for estimating VF in flower-free samples with an Root Mean Square Error (RMSE) of 3.56%, while the Enhanced Vegetation Index (EVI2) was the best in flower-containing samples with an RMSE of 5.65%. Based on reflectance in green and NIR bands, a technique was developed to identify whether a sample contained flowers and then to choose automatically the appropriate algorithm for its VF estimation. During the flowering season, we also explored the potential of using canopy reflectance or VIs to estimate FF in oilseed rape. No significant correlation was observed between VI and FF when soil was visible in the sensor’s field of view. Reflectance at 550 nm worked well for FF estimation with coefficient of determination (R²) above 0.6. Our model was validated in oilseed rape planted under different nitrogen fertilization applications and in different phenology stages. The results showed that it was able to predict VF and FF accurately in oilseed rape with RMSE below 6%.

This study proposed a colorimetric transformation and spectral features-based oilseed rape extraction algorithm (CSRA) to map oilseed rape at the provincial scale as a first step towards country-scale coverage. Using a stepwise analysis strategy, our method gradually separates vegetation from non-vegetation, crop from non-crop, and oilseed rape from winter wheat. The wide-field view (WFV) images from Chinese Gaofen satellite no. 1 (GF-1) at six continuous flowering stages in Wuxue City, Hubei Province, China are used to extract the unique characteristics of oilseed rape during the flowering period and predict the parameter of the CSRA method. The oilseed rape maps of Hubei Province from 2014 to 2017 are obtained automatically based on the CSRA method using GF-1 WFV images. As a result, the CSRA-derived provincial oilseed rape maps achieved at least 85% overall accuracy of spatial consistency when comparing with local reference oilseed rape maps and lower than 20% absolute error of provincial planting areas when comparing with agricultural census data. The robustness of the CSRA method is also tested on other satellite images including one panchromatic and multispectral image from GF-2 and two RapidEye images. Moreover, the comparison between the CSRA and other previous methods is discussed using the six GF-1 WFV images of Wuxue City, showing the proposed method has better mapping accuracy than other tested methods. These results highlight the potential of our method for accurate extraction and regional mapping capacity for oilseed rape.

Mosaicking of remote sensing images stitches images of different moments or sensors to produce a new image under a uniform geographic coordinate system. In a mosaicking process, the critical enblending operation is divided into color balance, seamline finder, and fusion of overlapping areas, which is still challenging to maintain color consistency and data fidelity. In this article, a new mosaicking framework using spatiotemporal fusion is proposed to solve the enblending issue. Two additional low-resolution reference images are introduced for each mosaicking image. With spatiotemporal fusion methods, all mosaicking images are reconstructed to a uniform time, then the combination of overlapping areas become easy. Furthermore, a new spatiotemporal fusion method is proposed by cascading enhanced deep neural networks to fuse images quickly and effectively. In the validation procedure, the proposed method is compared with eight color harmony methods or tools by mosaicking the red, green, and blue bands of Landsat-8 images with images from the moderate-resolution imaging spectroradiometer as the reference. The digital evaluations and visual comparisons demonstrate that the newly method outweighs majority methods regarding to the radiometric, structural, and spectral fidelity, which proves the feasibility of our new enblending method.

Due to the limitations of hardware technology, satellite sensors cannot obtain images with high temporal, spatial, and spectral resolutions at the same time. Current spatiotemporal fusion methods try to solve the contradiction between temporal resolution and spatial resolution, which cannot achieve good reconstruction accuracy partly because the data sources are from heterogeneous platforms with long chains difficult to be modeled. Different from the crossing-platform fusion, this work proposes to improve the spatial and temporal resolutions on a single platform. For the 2-m panchromatic images, 8-m multispectral images, and 16-m wide-field-view images captured by the Gaofen-1 satellite, our goal is to produce 2-m multispectral images with high temporal resolutions. Two convolutional neural networks are built to solve this spatiotemporal-spectral fusion issue with pansharpening and spatiotemporal fusion in serial. In the validation stage, the 2-m multispectral images are built and evaluated with the panchromatic images and 8-m multispectral images. The digital and visual evaluations show that our method can produce visually acceptable fusion quality, which may enhance the feasibility of the Gaofen-1 data.

The fast and accurate prediction of crop yield at the regional scale is of great significance to food policies or trade. In this study, a new model is developed to predict the yield of oilseed rape from high-resolution remote sensing images. In order to derive this model, the ground experiment and remote sensing data analysis are carried out successively. In the ground experiment, the leaf area index (LAI) of four growing stages are measured, and a regression model is established to predict yield from ground LAI. In the remote sensing analysis, a new model is built to predict ground LAI from Gaofen-1 images where the simple ratio vegetation index at the bolting stage and the VARIgreen vegetation index at the flowering stage are used. The WOFOSTWOrld FOod STudy (WOFOST) crop model is used to generate time-series ground LAI from discontinuous ground LAI, which is calibrated coarsely with the MODerate resolution imaging spectroradiometer LAI product and finely with the ground-measured data. By combining the two conclusive formulas, an estimation model is built from Gaofen-1 images to the yield of oilseed rape. The effectiveness of the proposed model is verified in Wuxue City, Hubei Province from 2014 to 2019, with the pyramid bottleneck residual network to extract oilseed rape planting areas, the proposed model to estimate yields, and the China statistical yearbooks for comparison. The validation shows that the prediction error of the proposed algorithm is less than 5.5%, which highlights the feasibility of our method for accurate prediction of the oilseed rape yield in a large area.