Modeling L- and X-band backscattering of wheat and tests over fields of Pampas

A discrete scattering model and a detailed set of ground measurements are used to simulate the backscattering coefficients of wheat fields during the whole growth cycle. Simulations are carried out at L- and X-band, and at HH, VV, and HV polarizations. Wheat fields are located in Pampas (Argentina), and are characterized by low values of plant density. Simulations show that the backscattering coefficient is driven by variations of soil moisture at L-band, particularly for HH polarization, with low vegetation effects. Conversely, the attenuation of vegetation is dominant in producing variations of backscattering coefficients at X-band, particularly for VV polarization. Simulations are compared against experimental data collected over the same Pampas region, using airborne SARAT SAR at L-band and COSMO-SKYMED at X-band. Assuming a surface height standard deviation in a 0.4–0.7 cm range, the simulations generally agree with experimental data, with an RMSE lower than about 2 dB at L-band and X-band, except a limited number of cases. Discrepancies observed in specific conditions are discussed. Overall, the results indicate that a joint use of L- and X-band has a good potential to monitor both soil moisture and vegetation growth

Irrigated grassland monitoring using a time series of terraSAR-X and COSMO-skyMed X-Band SAR Data

[Departement_IRSTEA]Territoires [TR1_IRSTEA]SYNERGIE [Axe_IRSTEA]TETIS-ATTOSInternational audienceThe objective of this study was to analyze the sensitivity of radar signals in the X-band in irrigated grassland conditions. The backscattered radar signals were analyzed according to soil moisture and vegetation parameters using linear regression models. A time series of radar (TerraSAR-X and COSMO-SkyMed) and optical (SPOT and LANDSAT) images was acquired at a high temporal frequency in 2013 over a small agricultural region in southeastern France. Ground measurements were conducted simultaneously with the satellite data acquisitions during several grassland growing cycles to monitor the evolution of the soil and vegetation characteristics. The comparison between the Normalized Difference Vegetation Index (NDVI) computed from optical images and the in situ Leaf Area Index (LAI) showed a logarithmic relationship with a greater scattering for the dates corresponding to vegetation well developed before the harvest. The correlation between the NDVI and the vegetation parameters (LAI, vegetation height, biomass, and vegetation water content) was high at the beginning of the growth cycle. This correlation became insensitive at a certain threshold corresponding to high vegetation (LAI ~2.5 m2/m2). Results showed that the radar signal depends on variations in soil moisture, with a higher sensitivity to soil moisture for biomass lower than 1 kg/m². HH and HV polarizations had approximately similar sensitivities to soil moisture. The penetration depth of the radar wave in the X-band was high, even for dense and high vegetation; flooded areas were visible in the images with higher detection potential in HH polarization than in HV polarization, even for vegetation heights reaching 1 m. Lower sensitivity was observed at the X-band between the radar signal and the vegetation parameters with very limited potential of the X-band to monitor grassland growth. These results showed that it is possible to track gravity irrigation and soil moisture variations from SAR X-band images acquired at high spatial resolution (an incidence angle near 30°)

Soil Moisture Estimation for landslide monitoring: A new approach using multi-temporal Synthetic Aperture RADAR data

This study explores the utility of the Spotlight2 X-band Synthetic Aperture Radar product developed by the Italian Space Agency for use in multi-temporal estimation of soil moisture in a landslide monitoring context, using a time series of monthly images of the Hollin Hill Landslide Observatory – North Yorkshire, UK. The study shows the complexity of surface soil moisture at an active landslide, using high resolution in situ soil moisture data. This in situ data is also used for ground truthing the soil moisture estimations from the SAR data. The study shows the limitations of inter-and intra-sensor calibration within the Cosmo-SkyMed array and contextualises this problem within the current research climate where SAR imagery is increasingly being created using multi-satellite constellation, while being used, increasingly, by environmental scientists rather than remote sensing specialists

Assessment of multi-temporal, multi-sensor radar and ancillary spatial data for grasslands monitoring in Ireland using machine learning approaches

Accurate inventories of grasslands are important for studies of carbon dynamics, biodiversity conservation and agricultural management. For regions with persistent cloud cover the use of multi-temporal synthetic aperture radar (SAR) data provides an attractive solution for generating up-to-date inventories of grasslands. This is even more appealing considering the data that will be available from upcoming missions such as Sentinel-1 and ALOS-2. In this study, the performance of three machine learning algorithms; Random Forests (RF), Support Vector Machines (SVM) and the relatively underused Extremely Randomised Trees (ERT) is evaluated for discriminating between grassland types over two large heterogeneous areas of Ireland using multi-temporal, multi-sensor radar and ancillary spatial datasets. A detailed accuracy assessment shows the efficacy of the three algorithms to classify different types of grasslands. Overall accuracies ≥ 88.7% (with kappa coefficient of 0.87) were achieved for the single frequency classifications and maximum accuracies of 97.9% (kappa coefficient of 0.98) for the combined frequency classifications. For most datasets, the ERT classifier outperforms SVM and RF

An algorithm for operational flood mapping from Synthetic Aperture Radar (SAR) data using fuzzy logic

Abstract. An algorithm developed to map flooded areas from synthetic aperture radar imagery is presented in this paper. It is conceived to be inserted in the operational flood management system of the Italian Civil Protection and can be used in an almost automatic mode or in an interactive mode, depending on the user's needs. The approach is based on the fuzzy logic that is used to integrate theoretical knowledge about the radar return from inundated areas taken into account by means of three electromagnetic scattering models, with simple hydraulic considerations and contextual information. This integration aims at allowing a user to cope with situations, such as the presence of vegetation in the flooded area, in which inundation mapping from satellite radars represents a difficult task. The algorithm is designed to work with radar data at L, C, and X frequency bands and employs also ancillary data, such as a land cover map and a digital elevation model. The flood mapping procedure is tested on an inundation that occurred in Albania on January 2010 using COSMO-SkyMed very high resolution X-band SAR data

Polarimetric SAR for the monitoring of agricultural crops

The monitoring of agricultural crops is a matter of great importance. Remote sensing has been unanimously recognized as one of the most important techniques for agricultural crops monitoring. Within the framework of active remote sensing, the capabilities of the Synthetic Aperture Radar (SAR) to provide fine spatial resolution and a wide area coverage, both in day and night time and almost under all weather conditions, make it a key tool for agricultural applications, including the monitoring and the estimation of phenological stages of crops. The monitoring of crop phenology is fundamental for the planning and the triggering of cultivation practices, since they require timely information about the crop conditions along the cultivation cycle. Due to the sensitivity of polarization of microwaves to crop structure and dielectric properties of the canopy, which in turn depend on the crop type, retrieval of phenology of agricultural crops by means of polarimetric SAR measurements is a promising application of this technology, especially after the launch of a number of polarimetric satellite sensors. In this thesis C-band polarimetric SAR measurements are used to estimate pheno- logical stages of agricultural crops. The behavior of polarimetric SAR observables at different growth stages is analyzed and then estimation procedures, aimed at the retrieval of such stages, are defined. The second topic on which this thesis is focused on is the land cover types discrimi- nation by means of X-band multi-polarization SAR data

Spaceborne L-Band Synthetic Aperture Radar Data for Geoscientific Analyses in Coastal Land Applications: A Review

The coastal zone offers among the world’s most productive and valuable ecosystems and is experiencing increasing pressure from anthropogenic impacts: human settlements, agriculture, aquaculture, trade, industrial activities, oil and gas exploitation and tourism. Earth observation has great capability to deliver valuable data at the local, regional and global scales and can support the assessment and monitoring of land‐ and water‐related applications in coastal zones. Compared to optical satellites, cloud‐cover does not limit the timeliness of data acquisition with spaceborne Synthetic Aperture Radar (SAR) sensors, which have all‐weather, day and night capabilities. Hence, active radar systems demonstrate great potential for continuous mapping and monitoring of coastal regions, particularly in cloud‐prone tropical and sub‐tropical climates. The canopy penetration capability with long radar wavelength enables L‐band SAR data to be used for coastal terrestrial environments and has been widely applied and investigated for the following geoscientific topics: mapping and monitoring of flooded vegetation and inundated areas; the retrieval of aboveground biomass; and the estimation of soil moisture. Human activities, global population growth, urban sprawl and climate change‐induced impacts are leading to increased pressure on coastal ecosystems causing land degradation, deforestation and land use change. This review presents a comprehensive overview of existing research articles that apply spaceborne L‐band SAR data for geoscientific analyses that are relevant for coastal land applications

Polarimetric SAR for the monitoring of agricultural crops

The monitoring of agricultural crops is a matter of great importance. Remote sensing has been unanimously recognized as one of the most important techniques for agricultural crops monitoring. Within the framework of active remote sensing, the capabilities of the Synthetic Aperture Radar (SAR) to provide fine spatial resolution and a wide area coverage, both in day and night time and almost under all weather conditions, make it a key tool for agricultural applications, including the monitoring and the estimation of phenological stages of crops. The monitoring of crop phenology is fundamental for the planning and the triggering of cultivation practices, since they require timely information about the crop conditions along the cultivation cycle. Due to the sensitivity of polarization of microwaves to crop structure and dielectric properties of the canopy, which in turn depend on the crop type, retrieval of phenology of agricultural crops by means of polarimetric SAR measurements is a promising application of this technology, especially after the launch of a number of polarimetric satellite sensors. In this thesis C-band polarimetric SAR measurements are used to estimate pheno- logical stages of agricultural crops. The behavior of polarimetric SAR observables at different growth stages is analyzed and then estimation procedures, aimed at the retrieval of such stages, are defined. The second topic on which this thesis is focused on is the land cover types discrimi- nation by means of X-band multi-polarization SAR data

A Qualitative Study on Microwave Remote Sensing and Challenges Faced in India

Over the past few decades remote sensing has expanded its limits with exponential rise in technology that facilitates accurate data fetching in real time. In view of some of the major problems faced by developing nations, particularly India with its recent advancement in space technology, remote sensing has a vital role to play in resolving many such problems. In the light of recent Global Space Programs where several satellites have been launched for large area mapping using microwave sensors, microwave remote sensing can play a vital role as India experiences a large number of disasters every year. Also, majority of Indian population relies on farming for their livelihood. Microwave remote sensing can have significant effects in both these two scenarios as opposed to its conventional counterpart, optical remote sensing under diverse conditions and facilitate better results in terms of disaster management, prediction and increasing crop yield. The current paper brings out the various details on the work done by using active microwave remote sensing, with specific illustrative examples, for disaster management support, crop management techniques and the challenges associated on carrying out such researches in a diverse terrain like India