Polarimetric characteristics of early-stage deforestation areas were examined with L-band synthetic aperture radar (SAR) to develop a forest early warning system. Time series of PALSAR-2/ScanSAR data and Landsat data were used to examine the differences in detection timing of deforestation in the most active deforestation sites in Peru and Brazil. The γ ⁰ HH value increased by 0.8 dB on average for areas undergoing early-stages of deforestation, in which felled trees were left on the ground. The detection timing was almost the same as that of using the optical sensor. On the other hand, the γ ⁰ HV value does not show significant γ ⁰ change at this early-stage of deforestation. The γ ⁰ HV value started to decrease 1-1.5 months after the deforestation was detected by Landsat. The γ ⁰ HV value decreased by 1.5-1.6 dB, 4-5 months after the deforestation. To understand the radar backscattering mechanism at the early-stage deforestation sites, field experiments were carried out 2-16 days after the PALSAR-2/fully-polarimetric observations. The early-stage deforestation sites revealed 1.1-2.5 dB and 2.9-4.0 dB increases for σ ⁰ HH and σ ⁰ surface, respectively. This can be explained by the direct (single bounce) scattering from felled trees left on the ground. The sites in which felled trees were removed and the surfaces were flattened showed 5.2-5.3 dB and 5.3-5.5 dB decreases for σ ⁰ HV and σ ⁰ volume, respectively. This can be explained by the lower sensitivity of the HV polarization to both the branches remaining on the ground and the surface roughness, along with its increased sensitivity to the forest biomass. We conclude that an increase in L-band γ ⁰ HH is a good indicator for detecting early-stage deforestation sites, where felled trees are left on the ground, while γ ⁰ HV may be useful for detecting later-stage deforestation sites.

At radar frequencies below 2 GHz, the mismatch between the 5 to 15 cm sensing depth of classical time domain reflectometry (TDR) probe soil moisture measurements and the radar penetration depth can easily lead to unreliable in situ data. Accurate quantitative measurements of soil water contents at various depths by classical methods are cumbersome and usually highly invasive. We propose an improved method for the estimation of vertical soil moisture profiles from multi-offset ground penetrating radar (GPR) data. A semi-automated data acquisition technique allows for very fast and robust measurements in the field. Advanced common mid-point (CMP) processing is applied to obtain quantitative estimates of the permittivity and depth of the reflecting soil layers. The method is validated against TDR measurements using data acquired in different environments. Depth and soil moisture contents of the reflecting layers were estimated with root mean square errors (RMSE) on the order of 5 cm and 1.9 Vol.-%, respectively. Application of the proposed technique for the validation of synthetic aperture radar (SAR) soil moisture estimates is demonstrated based on a case study using airborne L-band data and ground-based P-band data. For the L-band case we found good agreement between the near-surface GPR estimates and extended integral equation model (I²EM) based SAR retrievals, comparable to those obtained by TDR. At the P-band, the GPR based method significantly outperformed the TDR method when using soil moisture estimates at depths below 30 cm.

With the upcoming L-band Synthetic Aperture Radar (SAR) satellite mission Radar Observing System for Europe L-band SAR (ROSE-L) and its integration into existing C-band satellite missions such as Sentinel-1, multi-frequency SAR observations with high temporal and spatial resolution will become available. The SARSense campaign was conducted between June and August 2019 to investigate the potential for estimating soil and plant parameters at the agricultural test site in Selhausen (Germany). It included C- and L-band air- and space-borne observations accompanied by extensive in situ soil and plant sampling as well as unmanned aerial system (UAS) based multispectral and thermal infrared measurements. In this regard, we introduce a new publicly available SAR data set and present the first analysis of C- and L-band co- and cross-polarized backscattering signals regarding their sensitivity to soil and plant parameters. Results indicate that a multi-frequency approach is relevant to disentangle soil and plant contributions to the SAR signal and to identify specific scattering mechanisms associated with the characteristics of different crop type, especially for root crops and cereals.

Global-scale forest/non-forest (FNF) maps are of crucial importance for applications like biomass estimation and deforestation monitoring. Global FNF maps based on optical remote sensing data have been produced by the wall-to-wall satellite image analyses or sampling strategies. The German Aerospace Center (DLR) and the Japan Aerospace Exploration Agency (JAXA) also made available their global FNF maps based on synthetic aperture radar (SAR) data. This paper attempted to answer the following scientific question: how comparable are the FNF products derived from optical and SAR data? As test sites we selected the Amazon (tropical rainforest) and Cerrado (tropical savanna) biomes, the two largest Brazilian biomes. Forest estimations from 2015 derived from TanDEM-X (X band; HH polarization) and ALOS-2 (L band; HV polarization) SAR data, as well as forest cover information derived from Landsat 8 optical data were compared with each other at the municipality and image sampling levels. The optical-based forest estimations considered in this study were derived from the MapBiomas project, a Brazilian multi-institutional project to map land use and land cover (LULC) classes of an entire country based on historical time series of Landsat data. In addition to the existing forest maps, a set of 1619 Landsat 8 RGB color composites was used to generate new independent comparison data composed of circular areas with 5-km diameter, which were visually interpreted after image segmentation. The Spearman rank correlation estimated the correlation among the data sets and the paired Mann–Whitney–Wilcoxon tested the hypothesis that the data sets are statistically equal. Results showed that forest maps derived from SAR and optical satellites are statistically different regardless of biome or scale of study (municipality or image sampling), except for the Cerrado´s forest estimations derived from TanDEM-X and ALOS-2. Nevertheless, the percentage of pixels classified as forest or non-forest by both SAR sensors were 90% and 80% for the Amazon and Cerrado biome, respectively, indicating an overall good agreement.

A design of a wideband small spiral antenna for SAR is proposed. First, to miniaturize the spiral size, characteristics of input impedance and axial ratio for equiangular and Archimedean spiral antennas are analyzed regarding the effects of substrate and spiral turn. It is found that the miniaturized 6-turn Archimedean spiral antenna on a FR-4 substrate which is 4.4 cm in diameter has the good characteristics over a frequency range of 3 GHz to 20 GHz. Second, by experimentally observing time domain radar characteristics, it is confirmed that a cavity 6.1 cm deep sufficiently filled with an EM absorber is effective to obtain clear radar signals. It is also found that leaving a space between Tx and Rx antennas is important to improve the power ratio of a reflected wave to a direct wave.