Sentinel-1 SAR Backscatter Response to Agricultural Drought in The Netherlands

Abstract

Drought is a major natural hazard that impacts agriculture, the environment, and socio-economic conditions. In 2018 and 2019, Europe experienced a severe drought due to below average precipitation and high temperatures. Drought stress affects the moisture content and structure of agricultural crops and can result in lower yields. Synthetic Aperture Radar (SAR) observations are sensitive to the dielectric and geometric characteristics of crops and underlying soils. This study uses data from ESA’s Sentinel-1 SAR satellite to investigate the influence of drought stress on major arable crops of the Netherlands, its regional variability and the impact of water management decisions on crop development. Sentinel-1 VV, VH and VH/VV backscatter data are used to quantify the variability in the spatio-temporal dynamics of agricultural crop parcels in response to drought. Results show that VV and VH backscatter values are 1 to 2 dB lower for crop parcels during the 2018 drought compared to values in 2017. In addition, the growth season indicated by the cross-ratio (CR, VH/VV) for maize and onion is shorter during the drought year. Differences due to irrigation restrictions are observed in backscatter response from maize parcels. Lower CR values in 2019 indicate the impact of drought on the start of the growing season. Results demonstrate that Sentinel-1 can detect changes in the seasonal cycle of arable crops in response to agricultural drought.

Modeling SAR Observables by Combining a Crop-Growth Model With Machine Learning

“…The 2018 drought influenced the groundwa-462 ter levels, and hence crop production [65]. The influence of 463 the drought on Sentinel-1 SAR observables was discussed by 464 [30].…”

“…It is planted be-140 tween mid-April and the beginning of May, and the emergence 141 is in mid-May. It is left to ripen in the field, and is harvested 142 in September [30]. Silage maize is grown in approximately 143 20% (about 20000#) of parcels in Noord-Brabant.…”

Microwave remote sensing for agricultural drought monitoring: Recent developments and challenges

“…Working with SAR data at high resolution, the structural effect can influence the signal. Shorachi et al (2022) looked at Sentinel-1 backscatter and CR per orbit, but noted that, when a dense time series from Sentinel-1 is needed, the incidence angle effect on the backscatter from Sentinel-1 can play a role. Particularly when monitoring crops, it is suggested that this is problematic as geometry affects backscatter differently at different incidence angles.…”

Exploring the Potential Use of Sentinel-1 and 2 Satellite Imagery for Monitoring Winter Wheat Growth under Agricultural Drought Conditions in North-Western Poland

“…However, radar data require different interpretation than optical data, which are influenced by various factors such as moisture and the physical structure of the plant [14], so many ongoing studies are exploring the possibility of interpreting the Agriculture 2023, 13, 1798 2 of 17 results obtained from S-1 using S-2 data. Therefore, S-1 may be useful for, e.g., monitoring phenological phases of plants [15], where the authors of the study, using neural networks, obtained an average accuracy of 93.5% for the separation of rice phenological phases, and the average error between the calculated and actual phenological date was 3.08 days; crop classification based on temporal signatures with a supervised approach [16], where the author achieved an overall accuracy higher than 70%, or crop classification using the Random Forest method [17], where the 48 crop groups could be classified with an overall accuracy of 93.4%; monitoring crop height [18], where the authors showed a strong relationship between maize height and SAR parameters, with the coefficient of determination for VV + VH (R 2 = 0.82), VV (R 2 = 0.81), and VH (R 2 = 0.80); selection of the optimal machinery type for sugarcane field cultivation [19], where authors developed a mathematical model and received an accuracy of 83.6% and 81.2% for the training and testing models, respectively; monitoring plant development [20][21][22], where authors showed a high sensitivity of the indicators provided by S-1 to the detection of phenological growth stages for different crops; or testing sensitivity to agricultural drought [23,24], where authors found a correlation between backscatter as well as interferometric data and crop water stress. On the other hand, S-2 is more suitable for yield prediction [25,26] since authors reported a strong correlation between the obtained yield and vegetation indices with R 2 values ranging from 0.6 to 0.9; precision nitrogen fertilization [27], where authors showed that NDVI data can be used for field-scale optimal nitrogen management models; or detailing the soil-agricultural map [28].…”