Anna Balenzano

Dense Temporal Series of C- and L-band SAR Data for Soil Moisture Retrieval Over Agricultural Crops

This paper investigates the potential of multi-temporal C- and L-band SAR data, acquired within a short revisiting time (1-2 weeks), to map temporal changes of surface soil moisture content (mv) underneath agricultural crops. The analysed data consist of a new ground and SAR data set acquired on a weekly basis from late April to early August 2006 over the DEMMIN (Durable Environmental Multidisciplinary Monitoring Information Network) agricultural site (Northern Germany) during the European Space Agency 2006 AgriSAR campaign. The paper firstly investigates the main scattering mechanisms characterizing the interaction between the SAR signal and crops, such as winter wheat and rape. Then, the relationship between backscatter and soil moisture content temporal changes as a function of different SAR bands and polarizations is studied. Observations indicate that rationing of the multi-temporal radar backscatter can be a simple and effective way to decouple the effect of vegetation and surface roughness from the effect of soil moisture changes, when volume scattering is not dominant. The study also assesses to which extent changes in the incidence angle between subsequent radar acquisitions may affect the radar sensitivity to soil moisture content. Finally, an algorithm based on the change detection technique retrieving superficial soil moisture content is proposed and assessed both on simulated and experimental data. Results indicate that for crops relatively insensitive to volume scattering in the vegetation canopy (as for instance winter wheat at C-band or winter rape and winter wheat at L-band), mv can be retrieved during the whole growing season, with accuracies ranging between 5% and 6% [m3/m3]. We also show that low incidence angles (e.g., 20-35 ) and HH polarization are generally better suited to mv retrieval than VV polarization and higher incidence angles.

Crop Classification Using Short-Revisit Multitemporal SAR Data

Classification of crops and other land cover types is an important application of both optical/infrared and SAR satellite data. It is already an import application of present satellite systems, as it will be for planned missions, such as the Sentinels. An airborne SAR data set with a short revisit time acquired by the German ESAR system during the ESA-campaign, AgriSAR 2006, has been used to assess the performance of different polarization modes for crop classification. Both C-and L-band SAR data were acquired over the Demmin agricultural test site in North Eastern Germany on a weekly basis during the growing season. Single-and dual-polarization, and fully polarimetric data have been used in the analysis (fully polarimetric data were only available at L-band). The main results of the analysis are, that multitemporal acquisitions are very important for single-and dual-polarization modes, and that cross-polarized backscatter produces the best results, with errors down to 3%-6% at the two frequencies. There is a trade-off between the polarimetric information and the multitemporal information, where the best overall results are obtained using the multitemporal information. If only a few acquisitions are available, the polarimetric mode may perform better than the single-and dual polarization modes.

Optimization of Soil Hydraulic Model Parameters Using Synthetic Aperture Radar Data: An Integrated Multidisciplinary Approach

It is widely recognized that synthetic aperture radar (SAR) data are a very valuable source of information for the modeling of the interactions between the land surface and the atmosphere. During the last couple of decades, most of the research on the use of SAR data in hydrologic applications has been focused on the retrieval of land and biogeophysical parameters (e.g., soil moisture contents). One relatively unexplored issue consists of the optimization of soil hydraulic model parameters, such as, for example, hydraulic conductivity values, through remote sensing. This is due to the fact that no direct relationships between the remote-sensing observations, more specifically radar backscatter values, and the parameter values can be derived. However, land surface models can provide these relationships. The objective of this paper is to retrieve a number of soil physical model parameters through a combination of remote sensing and land surface modeling. Spatially distributed and multitemporal SAR-based soil moisture maps are the basis of the study. The surface soil moisture values are used in a parameter estimation procedure based on the extended Kalman filter equations. In fact, the land surface model is, thus, used to determine the relationship between the soil physical parameters and the remote-sensing data. An analysis is then performed, relating the retrieved soil parameters to the soil texture data available over the study area. The results of the study show that there is a potential to retrieve soil physical model parameters through a combination of land surface modeling and remote sensing.

On the use of temporal series of L-and X-band SAR data for soil moisture retrieval. Capitanata plain case study

Abstract This paper investigates the use of time series of ALOS/PALSAR-1 and COSMO-SkyMed data for the soil moisture retrieval (mv) by means of the SMOSAR algorithm. The application context is the exploitation of mv maps at a moderate spatial and temporal resolution for improving flood/drought monitoring at regional scale. The SAR data were acquired over the Capitanata plain in Southern Italy, over which ground campaigns were carried out in 2007, 2010 and 2011. The analysis shows that the mv retrieval accuracy is 5%-7% m3/m3 at L-and X band, although the latter is restricted to a use over nearly bare soil only.

Inter-comparison of hydrological model simulations with time series of SAR-derived soil moisture maps

Abstract A comparison between superficial soil moisture content, m, values predicted by the DREAM hydrologic model and those retrieved from time-series of ALOS/PALSAR and COSMO-SkyMed SAR data acquired in 2007 and 2010–2011 is presented. The area investigated is part of the Celone at Ponte Foggia-S. Severo river basin, which is a tributary of the Candelaro river, downstream of the S. Giusto Dam, in Puglia (Southern Italy). Results show a good agreement in terms of bias and rmse between the hydrologic modeled and SAR-retrieved mv-values, and open new opportunities for the use of SAR-derived mv-values to calibrate/validate hydrologic models in semi-arid areas.

SMOSAR algorithm for soil moisture retrieval using Sentinel-1 data

This paper describes and assesses the quality of the algorithm, “Soil MOisture retrieval from multi-temporal SAR data” (SMOSAR), developed in view of the forthcoming European Space Agency (ESA) Sentinel-1 (S-1) mission. SMOSAR retrieves soil moisture (mv) products at high spatial resolution (i.e. less than 1km) from dense time series of either single (i.e. HH or VV) or dual polarized (i.e. HH+HV or VV+VH) S-1 data. The assessment of the algorithm performance is based on time series of ENVISAT/ASAR data collected over the DEMMIN site (Germany) in 2006 and over the Matera site (Italy) in 2003 and 2005 and RADARSAT-2 data acquired over the Flevoland site (The Netherlands) in 2009. Results indicate that mv can be retrieved with an accuracy of 5% at HH polarization, whereas at VV polarization more investigations are required to provide reliable figure for the retrievable accuracy.

On the use of multi-temporal series of COSMO-SkyMed data for LANDcover classification and surface parameter retrieval over agricultural sites

The objective of this paper is to report on the activities carried out during the first year of the Italian project “Use of COSMO-SkyMed data for LANDcover classification and surface parameters retrieval over agricultural sites” (COSMOLAND), funded by the Italian Space Agency. The project intends to contribute to the COSMO-SkyMed mission objectives in the agriculture and hydrology application domains.

COSMO-SkyMed multi-temporal data for land cover classification and soil moisture retrieval over an agricultural site in Southern Australia

This paper uses a time-series of COSMO-SkyMed SAR images for land cover classification and soil moisture retrieval over an agricultural area located in Southern Australia. The SAR products analyzed are 11 StripMap Ping Pong images, at HH and HV polarizations, acquired at 21° incidence angle and with a revisiting time of either 8 or 16 days. The classification accuracy has been assessed as a function of the polarization and the number of images analyzed. Results confirm that the temporal information is crucial to improve the classification results. An overall accuracy of approximately 82% was achieved for 10 classes. Moreover, soil moisture (mv) maps over bare or sparsely vegetated areas have been retrieved by means of the SMOSAR-X (“Soil MOisture retrieval from multi-temporal SAR data”) algorithm, developed in view of the forthcoming Sentinel-1 data and then adapted to X-band SAR data. The SMOSAR-X algorithm is shown to produce mv maps with an rmse of 6.6% v/v.

Assimilation of COSMO-SkyMed-derived LAI maps into the AQUATER crop growth simulation model. Capitanata (Southern Italy) case study

Abstract AQUATER is a Decision Support System (DSS) developed to drive crop management decisions at district level in a Mediterranean area; it integrates information from soil and climatic databases with a crop growth simulation model and provides estimates of crop yield at regional scale. AQUATER can assimilate LAI maps derived from Earth observation data in order to mitigate the risk of erroneous model predictions over large areas. In this study, time-series of LAI maps derived from COSMO-SkyMed SAR images, acquired over the Capitanata plain (Puglia region) in 2010 and 2011, have been assimilated by a forcing procedure in AQUATER and the improvements of its predictions have been assessed. Results indicate that the LAI assimilation leads to significant improvements in the yield forecast of sugar beet and tomato crops, whereas in the case of wheat the improvements are marginal.

Time series of COSMO-SkyMed data for landcover classification and surface parameter retrieval over agricultural sites

This paper reports on the results of an Italian project aimed at investigating the use of X-band COSMO-SkyMed (CSK) SAR data for applications in agriculture and hydrology. Existing classification and retrieval algorithms have been tailored to CSK data and time series of crop, leaf area index and soil moisture maps have been retrieved and assessed through the comparison with in situ data collected over three agricultural sites. In addition, the CSK-derived surface parameters have been integrated into crop growth and hydrologic models and the resulting improvements have been assessed. Results indicate that multi-temporal dual-polarized CSK data are very well-suited for agricultural crop classification and that the integration of maps of SAR-derived surface parameters into crop growth and/or hydrologic models, in general, leads to significant improvements in the model performances.