Agriculture is of huge economic significance in The Netherlands where the provision of real-time, reliable information on crop development is essential to support the transition towards precision agriculture. Optical imagery can provide invaluable insights into crop growth and development but is severely hampered by cloud cover. This case study in the Flevopolder illustrates the potential value of Sentinel-1 for monitoring five key crops in The Netherlands, namely sugar beet, potato, maize, wheat and English rye grass. Time series of radar backscatter from the European Space Agency’s Sentinel-1 Mission are analyzed and compared to ground measurements including phenological stage and height. Temporal variations in backscatter data reflect changes in water content and structure associated with phenological development. Emergence and closure dates are estimated from the backscatter time series and validated against a photo archive. Coherence data are compared to Normalized Difference Vegetation Index (NDVI) and ground data, illustrating that the sudden increase in coherence is a useful indicator of harvest. The results presented here demonstrate that Sentinel-1 data have significant potential value to monitor growth and development of key Dutch crops. Furthermore, the guaranteed availability of Sentinel-1 imagery in clouded conditions ensures the reliability of data to meet the monitoring needs of farmers, food producers and regulatory bodies.

Synthetic aperture radar (SAR) acquisitions are mainly deemed suitable for mapping dynamic land-cover and land-use scenarios due to their timeliness and reliability. This particularly applies to Sentinel-1 imagery. Nevertheless, the accurate mapping of regions characterized by a mixture of crops and grasses can still represent a challenge. Radar time-series have to date mainly been exploited through backscatter intensities, whereas only fewer contributions have focused on analyzing the potential of interferometric information, intuitively enhanced by the short revisit. In this paper, we evaluate, as primary objective, the added value of short-temporal baseline coherences over a complex agricultural area in the São Paulo state, cultivated with heterogeneously (asynchronously) managed annual crops, grasses for pasture and sugarcane plantations. We also investigated the sensitivity of the radar information to the classification methods as well as to the data preparation and sampling practices. Two supervised machine learning methods—namely support vector machine (SVM) and random forest (RF)—were applied to the Sentinel-1 time-series at the pixel and field levels. The results highlight that an improvement of 10 percentage points (p.p.) in the classification accuracy can be achieved by using the coherence in addition to the backscatter intensity and by combining co-polarized (VV) and cross-polarized (VH) information. It is shown that the largest contribution in class discrimination is brought during winter, when dry vegetation and bare soils can be expected. One of the added values of coherence was indeed identified in the enhanced sensitivity to harvest events in a small but significant number of cases.

Space-based remote sensing imagery can provide a valuable and cost-effective set of observations for mapping crop-productivity differences. The effectiveness of such signals is dependent on several conditions that are related to crop and sensor characteristics. In this paper, we present the dynamic behavior of signals from five Synthetic Aperture Radar (SAR) sensors and optical sensors with growing sugarcane, focusing on saturation effects and the influence of precipitation events. In addition, we analyzed the level of agreement within and between these spaceborne datasets over space and time. As a result, we produced a list of conditions during which the acquisition of satellite imagery is most effective for sugarcane productivity monitoring. For this, we analyzed remote sensing data from two C-band SAR (Sentinel-1 and Radarsat-2), one L-band SAR (ALOS-2), and two optical sensors (Landsat-8 and WorldView-2), in conjunction with detailed ground-reference data acquired over several sugarcane fields in the state of São Paulo, Brazil. We conclude that satellite imagery from L-band SAR and optical sensors is preferred for monitoring sugarcane biomass growth in time and space. Additionally, C-band SAR imagery offers the potential for mapping spatial variations during specific time windows and may be further exploited for its precipitation sensitivity.

Current space-based SAR offers unique opportunities to classify vegetation types and to monitor vegetation growth due to its frequent acquisitions and its sensitivity to vegetation geometry. However, SAR signals also experience frequent temporal fluctuations caused by precipitation events, complicating the mapping and monitoring of vegetation. In this paper, we show that the influence of a priori known precipitation events on the signals can be used advantageously for the classification of vegetation conditions. For this, we exploit the change in Sentinel-1 backscatter response between consecutive acquisitions under varying wetness conditions, which we show is dependent on the state of vegetation. The performance further improves when a priori information on the soil type is taken into account.

After publication of the research paper

This work is focused on the possibility to enhance the observation capabilities of the forthcoming Synthetic Aperture Radar (SAR) ROSE-L (which stands for Radar Observation System for Europe at L-band) mission [1], [2], supported by the European Space Agency (ESA) as part of the Copernicus Expansion Programme.Specifically, we propose a solution aimed at enabling the currently designed ROSE-L system for a two-look ScanSAR modeconfiguration, without impairing key parameters, namely, the azimuth resolution and the range swath, of the original system, which is instead designed to basically achieve only a one-look ScanSAR mode configuration. In particular, following the analysis presented in [3], we propose to properly shape the radiated azimuth beam, doubling its width, without upsetting the original design of the ROSE-L radar antenna and taking advantage of the degrees of freedom offered by its current layout.The proposed ROSE-L two-look ScanSAR mode configuration presents several valuable advantages in different applications, among which we focus on the possibility to retrieve, at global scale and without azimuth gaps, the North-South deformation components of the displacement phenomena occurred on the ground through the so called Burst overlap interferometry technique [4]. [1] M. Zimmermanns and C. Roemer, “Copernicus HPCM: ROSE-L SAR Instrument and Performance Overview,” in EUSAR 2022; 14th European Conference on Synthetic Aperture Radar, Leipzig, Germany, 2022, pp. 1-6.[2] M. Davidson and R. Furnell, “ROSE-L: Copernicus L-Band SAR Mission,” in IGARSS 2021; IEEE International Geoscience and Remote Sensing Symposium, Brussels, Belgium, 2021, pp. 872-873.[3] S. Perna, F. Longo, S. Zoffoli, M. Davidson, L. Lannini and R. Lanari, “ A conceptual performance study on a two-look ScanSAR mode configuration for the forthcoming ROSE-L mission,” in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2023.3344537.[4] R. Grandin, E. Klein, M. Métois, C. Vigny, “Three-dimensional displacement field of the 2015 8.3 Illapel earthquake (Chile) from across- and along-track Sentinel-1 TOPS interferometry,” in Geophys.Res. Lett., vol. 43, pp. 2552-2561, 2016.