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Article

Monitoring Irrigation Events and Crop Dynamics Using Sentinel-1 and Sentinel-2 Time Series

1
Hydrology, Agriculture and Land Observation Group, Water Desalination and Reuse Center, Division of Biological and Environmental Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
2
Key Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Heihe Remote Sensing Experimental Research Station of Chinese Academy of Sciences, Lanzhou 730000, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2022, 14(5), 1205; https://doi.org/10.3390/rs14051205
Submission received: 9 January 2022 / Revised: 4 February 2022 / Accepted: 6 February 2022 / Published: 1 March 2022
(This article belongs to the Special Issue Remote Sensing for Agricultural Water Management (RSAWM))

Abstract

Capturing and identifying field-based agricultural activities, such as the start, duration and end of irrigation, together with crop sowing/germination, growing period and time of harvest, offer informative metrics that can assist in precision agricultural activities in addition to broader water and food security monitoring efforts. While optically based band-ratios, such as the normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), have been used as descriptors for monitoring crop dynamics, data are not always available due to the influence of clouds and other atmospheric effects on optical sensors. Satellite-based microwave systems, such as the synthetic aperture radar (SAR), offer an all-weather advantage in monitoring soil and crop conditions. In this paper, we leverage the relative strengths of both optical- and microwave-based approaches by combining high resolution Sentinel-1 SAR and Sentinel-2 optical imagery to monitor irrigation events and crop dynamics in a dryland agricultural landscape. A microwave backscatter model was used to analyze the responses of simulated backscatters to soil moisture, NDVI and NDWI (both are correlated with vegetation water content and can be regarded as vegetation descriptors), allowing an empirical relationship between these two platforms. A correlation analysis was also performed using Sentinel-1 SAR and Sentinel-2 optical data over crops of maize, alfalfa, carrot and Rhodes grass in Al Kharj farm of Saudi Arabia to identify an appropriate SAR-based vegetation descriptor. The results illustrate the relationship between SAR and both NDVI and NDWI and demonstrated the relationship between the cross-polarization ratio (VH/VV) and the two optical indices. We explore the capacity of this multi-platform and multi-sensor approach to inform on the spatio-temporal dynamics of a range of agricultural activities, which can be used to facilitate field-based management decisions.
Keywords: synthetic aperture radar; normalized difference vegetation index; normalized difference water index; Sentinel-1; Sentinel-2; irrigation; crop dynamics synthetic aperture radar; normalized difference vegetation index; normalized difference water index; Sentinel-1; Sentinel-2; irrigation; crop dynamics

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MDPI and ACS Style

Ma, C.; Johansen, K.; McCabe, M.F. Monitoring Irrigation Events and Crop Dynamics Using Sentinel-1 and Sentinel-2 Time Series. Remote Sens. 2022, 14, 1205. https://doi.org/10.3390/rs14051205

AMA Style

Ma C, Johansen K, McCabe MF. Monitoring Irrigation Events and Crop Dynamics Using Sentinel-1 and Sentinel-2 Time Series. Remote Sensing. 2022; 14(5):1205. https://doi.org/10.3390/rs14051205

Chicago/Turabian Style

Ma, Chunfeng, Kasper Johansen, and Matthew F. McCabe. 2022. "Monitoring Irrigation Events and Crop Dynamics Using Sentinel-1 and Sentinel-2 Time Series" Remote Sensing 14, no. 5: 1205. https://doi.org/10.3390/rs14051205

APA Style

Ma, C., Johansen, K., & McCabe, M. F. (2022). Monitoring Irrigation Events and Crop Dynamics Using Sentinel-1 and Sentinel-2 Time Series. Remote Sensing, 14(5), 1205. https://doi.org/10.3390/rs14051205

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