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

Chunfeng Ma,Matthew F Mccabe,Kasper Johansen

doi:10.3390/rs14051205

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.

Highlights

The correlation analysis between synthetic aperture radar (SAR) backscatters and both normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) demonstrated that the VH- against VV-polarized backscatters (VH/VV) ratio and VH-polarized backscatter had relatively high correlation with the optical indices (NDVI, NDWI), indicating that the VH/VV ratio and VH-polarized backscatter can potentially be used for vegetation water content (VWC) estimation, as NDVI and NDWI have previously been proven to be able to estimate VWC [6] and monitor the crop dynamics [11–13]
The forward modeling established the links between SAR backscatters and optical NDVI and NDWI indices, and revealed the relation of backscatters to soil moisture, NDVI and NDWI, providing a theoretical foundation for the time series analysis
These observations demonstrated the feasibility and reliability of monitoring irrigation events and vegetation dynamics using a combination of Sentinel-1 and Sentinel-2 data

Summary

Introduction

As such, quantifying water use for agriculture and monitoring crop status represent critical sources of information for optimizing water allocation and improving agricultural productivity [3,4]. It is important to develop the capacity to monitor and characterize field-scale events, including activities, such as irrigation scheduling (e.g., start, duration and end of irrigation) and related crop dynamics, capturing activities including the sowing date, growing period and time of harvest. In many cases, these activities can be identified through time series

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Conclusion