Accurate spatio-temporal information about rice growth is an important factor for agronomic management and regional grain yield estimation. In this letter, a unified framework for monitoring and mapping of rice using dense time-series of Sentinel-1 synthetic aperture radar (SAR) images is proposed. A processing chain for such dense time-series Sentinel-1 images is developed with the Google Earth Engine's cloud computing platform. A dense time-series analysis of backscatter response of rice with different management practices is analyzed. Subsequently, the early and late transplanted rice is classified using a clustering algorithm within this platform. The proposed approach is used to monitor different cultivars of rice in three districts in the state of West Bengal, which is one of the major rice growing regions in India. The classification accuracy is assessed across 150 validation points spanning multiple blocks for the 2017 monsoon season. The Sentinel-1 SAR images acquired up to the early vegetative stage for rice have provided satisfactory classification accuracy with an overall accuracy >85% with κ ~ 0.86 across different management practices throughout the region.

In radar polarimetry, incoherent target decomposition techniques help extract scattering information from polarimetric synthetic aperture radar (SAR) data. This is achieved either by fitting appropriate scattering models or by optimizing the received wave intensity through the diagonalization of the coherency (or covariance) matrix. As such, the received wave information depends on the received antenna configuration. Additionally, a polarimetric descriptor that is independent of the received antenna configuration might provide additional information which is missed by the individual elements of the coherency matrix. This implies that existing target characterization techniques might neglect this information. In this regard, we suitably utilize the 2-D and 3-D Barakat degree of polarization which is independent of the received antenna configuration to obtain distinct polarimetric information for target characterization. In this study, we introduce new roll-invariant scattering-type parameters for both full-polarimetric (FP) and compact-polarimetric (CP) SAR data. These new parameters jointly use the information of the 2-D and 3-D Barakat degree of polarization and the elements of the coherency (or covariance) matrix. We use these new scattering-type parameters, which provide equivalent information as the Cloude α for FP SAR data and the ellipticity parameter χ for CP SAR data, to characterize various targets adequately. Additionally, we appropriately utilize these new scattering-type parameters to obtain unique non-model-based three-component scattering power decomposition techniques. We obtain the even-bounce, and the odd-bounce scattering powers by modulating the total polarized power by a proper geometrical factor derived using the new scattering-type parameters for FP and CP SAR data. The diffused scattering power is obtained as the depolarized fraction of the total power. Moreover, due to the nature of its formulation, the decomposition scattering powers are non-negative and roll-invariant while the total power is conserved. The proposed method is both qualitatively and quantitatively assessed utilizing the L-band ALOS-2 and C-band Radarsat-2 FP and the associated simulated CP SAR data.

Crop growth monitoring using compact-pol synthetic aperture radar (CP-SAR) data is gaining attention with the rapid advancements toward operational applications. In this article, we propose a vegetation index for compact polarimetric (CP) SAR data [compact-pol radar vegetation index (CpRVI)]. The CpRVI is derived using the concept of a geodesic distance between the Kennaugh matrices projected on a unit sphere. This distance is utilized to compute a similarity measure between the observed Kennaugh matrix and the Kennaugh matrix of an ideal depolarizer (a realization of vegetation canopy). The similarity measure is then modulated with a scaled quantity derived from the scattering power ratio of the same and opposite sense polarization with respect to the transmitted circular polarization. In this article, we utilize time-series-simulated RADARSAT Constellation Mission (RCM) compact-pol SAR data (RH-RV) obtained from the full-pol RADARSAT-2 observations during the soil moisture active passive (SMAP) validation experiment 2016 (SMAPVEX16-MB) campaign in Manitoba, Canada, to assess the proposed vegetation index. Among the various crops grown in this region, in particular, we analyze the growth stages of wheat and soybean due to their different canopy structures. A temporal analysis of the proposed CpRVI with crop biophysical parameters [the plant area index (PAI) and vegetation water content (VWC)] at different phenological stages confirms the trend of CpRVI with the plant growth. Nevertheless, variations of CpRVI values are apparent with different plant densities for both the crop types. Also, the linear regression analysis confirms that the CpRVI values significantly correlate with PAI (r = 0.72 and 0.85) and VWC (r = 0.62 and 0.75) for both wheat and soybean. We observed good retrieval of PAI and VWC for both wheat and soybean.

<!-- *** Custom HTML *** --> The present state of the art technologies for flood mapping are typically tested on small geographical regions due to limitation of resources, which hinders the implementation of real-time flood management activities. We proposed a unified framework (GEE4FLOOD) for rapid flood mapping in Google Earth Engine (GEE) cloud platform. With the unexpected spells of extreme rainfall in August 2018, many parts of Kerala state in India experienced a major disastrous flood. Therefore, we tested the GEE4FLOOD processing chain on August 2018 Kerala flood event. GEE4FLOOD utilizes multitemporal Sentinel-1 synthetic aperture radar images available in GEE catalog and an automatic Otsu’s thresholding algorithm for flood mapping. It also utilizes other remote sensing datasets available in GEE catalog for permanent water body mask creation and result validation. The ground truth data collected during the Kerala flood indicates promising accuracy with 82% overall accuracy and 78.5% accuracy for flood class alone. In addition, the entire process from data fetching to flood map generation at a varying geographical extent (district to state level) took ∼2 to 4 min.

Feature selection techniques intent to select a subset of features that minimizes redundancy and maximizes relevancy for classification problems in machine learning. Standard methods for feature selection in machine learning seldom take into account the domain knowledge associated with the data. Multitemporal crop classification studies with full-polarimetric synthetic aperture radar (PolSAR) data ought to consider the changes in the scattering mechanisms with their phenological growth stages. Hence, it is desirable to incorporate these changes while determining a feature subset for classification. In this study, a random forest (RF) based feature selection technique is proposed that takes into account the changes in the physical scattering mechanism with crop phenological stages for multitemporal PolSAR classification. The partial probability plot, which is an attribute of RF, provides information about the marginal effect of a polarimetric parameter on the desired crop class. Moreover, it is used to identify the specific range of a parameter where the probability of the presence of a particular crop class is high. The proposed technique identifies features that change significantly with crop phenology. The selected features are the ones whose ranges show maximum separation amongst crop classes. Additionally, the feature subset is refined by eliminating correlated features. The E-SAR L-band dataset of the AgriSAR-2006 campaign over the Demmin test site in Germany is used in this study. The classification accuracy using the novel feature selection technique is 99.12%. This is nominally better than using the features obtained from a standard feature selection method used in RF, such as mean decrease Gini (98.73%) and mean decrease accuracy (98.68%) that do not take into account the information based on crop phenology.