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Open AccessArticle

Continuous Monitoring of Cotton Stem Water Potential using Sentinel-2 Imagery

1
Department of Geosciences, Texas Tech University, Lubbock, TX 79409, USA
2
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
3
Department, University of Chinese Academy of Sciences, Beijing 100049, China
4
Department of Natural Resources and the Environment, University of Connecticut, Storrs, CT 06269, USA
5
Department of Plant and Soil Science, Texas Tech University, Lubbock, TX 79409, USA
6
Department of Soil and Crop Sciences, Texas A&M AgriLife Research, Lubbock, TX 79403, USA
7
The Climate Corporation, San Francisco, CA 94103, USA
8
The Climate Corporation, Saint Louis, MO 63141, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(7), 1176; https://doi.org/10.3390/rs12071176
Received: 19 February 2020 / Revised: 27 March 2020 / Accepted: 2 April 2020 / Published: 6 April 2020
(This article belongs to the Special Issue Smart Farming and Land Management Enabled by Remotely Sensed Big Data)
Monitoring cotton status during the growing season is critical in increasing production efficiency. The water status in cotton is a key factor for yield and cotton quality. Stem water potential (SWP) is a precise indicator for assessing cotton water status. Satellite remote sensing is an effective approach for monitoring cotton growth at a large scale. The aim of this study is to estimate cotton water stress at a high temporal frequency and at a large scale. In this study, we measured midday SWP samples according to the acquisition dates of Sentinel-2 images and used them to build linear-regression-based and machine-learning-based models to estimate cotton water stress during the growing season (June to August, 2018). For the linear-regression-based method, we estimated SWP based on different Sentinel-2 spectral bands and vegetation indices, where the normalized difference index 45 (NDI45) achieved the best performance (R2 = 0.6269; RMSE = 3.6802 (-1*swp (bars))). For the machine-learning-based method, we used random forest regression to estimate SWP and received even better results (R2 = 0.6709; RMSE = 3.3742 (-1*swp (bars))). To find the best selection of input variables for the machine-learning-based approach, we tried three different data input datasets, including (1) 9 original spectral bands (e.g., blue, green, red, red edge, near infrared (NIR), and shortwave infrared (SWIR)), (2) 21 vegetation indices, and (3) a combination of original Sentinel-2 spectral bands and vegetation indices. The highest accuracy was achieved when only the original spectral bands were used. We also found the SWIR and red edge band were the most important spectral bands, and the vegetation indices based on red edge and NIR bands were particularly helpful. Finally, we applied the best approach for the linear-regression-based and the machine-learning-based methods to generate cotton water potential maps at a large scale and high temporal frequency. Results suggests that the methods developed here has the potential for continuous monitoring of SWP at large scales and the machine-learning-based method is preferred. View Full-Text
Keywords: cotton stem water potential; linear regression; vegetation indices; machine learning; random forest; Sentinel-2 cotton stem water potential; linear regression; vegetation indices; machine learning; random forest; Sentinel-2
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MDPI and ACS Style

Lin, Y.; Zhu, Z.; Guo, W.; Sun, Y.; Yang, X.; Kovalskyy, V. Continuous Monitoring of Cotton Stem Water Potential using Sentinel-2 Imagery. Remote Sens. 2020, 12, 1176.

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