Accurate estimation of fractional vegetation cover for winter wheat by integrated unmanned aerial systems and satellite images

Songlin Yang; Shanshan Li; Bing Zhang; Ruyi Yu; Cunjun Li; Jinkang Hu; Shengwei Liu; Enhui Cheng; Zihang Lou; Dailiang Peng

doi:10.3389/fpls.2023.1220137

Accurate estimation of fractional vegetation cover for winter wheat by integrated unmanned aerial systems and satellite images

Front Plant Sci. 2023 Sep 27:14:1220137. doi: 10.3389/fpls.2023.1220137. eCollection 2023.

Authors

Songlin Yang^{1

2

3}, Shanshan Li^{3

4}, Bing Zhang^{1

2

3}, Ruyi Yu⁴, Cunjun Li⁵, Jinkang Hu^{1

2

3}, Shengwei Liu¹, Enhui Cheng^{1

2

3}, Zihang Lou^{1

2

3}, Dailiang Peng^{1

2

3}

Affiliations

¹ Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China.
² International Research Center of Big Data for Sustainable Development Goals, Beijing, China.
³ School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China.
⁴ China Remote Sensing Satellite Ground Station, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China.
⁵ Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.

Abstract

Accurate estimation of fractional vegetation cover (FVC) is essential for crop growth monitoring. Currently, satellite remote sensing monitoring remains one of the most effective methods for the estimation of crop FVC. However, due to the significant difference in scale between the coarse resolution of satellite images and the scale of measurable data on the ground, there are significant uncertainties and errors in estimating crop FVC. Here, we adopt a Strategy of Upscaling-Downscaling operations for unmanned aerial systems (UAS) and satellite data collected during 2 growing seasons of winter wheat, respectively, using backpropagation neural networks (BPNN) as support to fully bridge this scale gap using highly accurate the UAS-derived FVC (FVC_UAS) to obtain wheat accurate FVC. Through validation with an independent dataset, the BPNN model predicted FVC with an RMSE of 0.059, which is 11.9% to 25.3% lower than commonly used Long Short-Term Memory (LSTM), Random Forest Regression (RFR), and traditional Normalized Difference Vegetation Index-based method (NDVI-based) models. Moreover, all those models achieved improved estimation accuracy with the Strategy of Upscaling-Downscaling, as compared to only upscaling UAS data. Our results demonstrate that: (1) establishing a nonlinear relationship between FVC_UAS and satellite data enables accurate estimation of FVC over larger regions, with the strong support of machine learning capabilities. (2) Employing the Strategy of Upscaling-Downscaling is an effective strategy that can improve the accuracy of FVC estimation, in the collaborative use of UAS and satellite data, especially in the boundary area of the wheat field. This has significant implications for accurate FVC estimation for winter wheat, providing a reference for the estimation of other surface parameters and the collaborative application of multisource data.

Keywords: UAS; fractional vegetation cover; machine learning; remote sensing; winter wheat.

Grants and funding

The authors declare financial support was received for the research, authorship, and/or publication of this article. The authors acknowledge the financial support by the National Natural Science Foundation of China under Grant 42030111, 42071330, and the National Key R&D Program of China under Grant 2021YFB3900503.