Signal level comparison between TerraSAR-X and COSMO-SkyMed SAR Sensors

International audienceSoil and vegetation biophysical parameter retrieval using synthetic-aperture-radar images requires radiometrically well-calibrated sensors. In this letter, a comparison of signal levels between TerraSAR-X (TSX) and the COSMO-SkyMed (CSK) constellation (CSK1, CSK2, CSK3, and CSK4) was carried out in order to analyze the ability to use jointly all current X-band sensors. The analysis of the X-band signal over forest stands showed a stable signal (variation lower than 1 dB) over time for each of the studied sensors, but a significant difference was observed between the different X-band sensors. Differences between radar signals were higher in HH than in HV polarization. TSX and CSK4 showed similar backscatter signals, with signal level differences of 0.6 dB in HH and 1.4 dB in HV. The CSK3 signal was observed to be lower than those from TSX and CSK4 by about 2.1 dB and 1.5 dB in HH against 3.2 dB and 1.8 dB in HV, respectively. Moreover, CSK2 and CSK1 which showed slightly different backscatter signals (within 1.1 dB in HH and 1.9 dB in HV) had signal levels lower than those obtained from TSX (2.2-3.3 dB in HH and 3.2-5.1 dB in HV for about 29° incidence angle). These results show that it is currently difficult to use jointly the available X-band satellites (CSK and TSX) for estimating the biophysical parameters of soil or vegetation. This is due to the significant difference in the radar signal level between some of the analyzed satellites, which will cause a high overor underestimation of biophysical parameters

Estimation of the dynamics and yields of cereals in a semi-arid area using remote sensing and the SAFY growth model

International audienceIn semi-arid areas, a strongly variable climate represents a major risk for food safety. An operational grain yield forecasting system, which could help decision-makers to make early assessments and plan annual imports, is thus needed. It can be challenging to monitor the crop canopy and production capacity of plants, especially cereals. In this context, the aim of the present study is to analyse the characteristics of two types of irrigated and non-irrigated cereals: barley and wheat. Through the use of a rich database, acquired over a period of two years for more than 30 test fields, and from 20 optical satellite SPOT/HRV images, two research approaches are considered. First, statistical analysis is used to characterize the vegetation's dynamics and grain yield, based on remotely sensed (satellite) normalized difference vegetation index (NDVI) measurements. A relationship is established between the NDVI and LAI (leaf area index). Different robust relationships (exponential or linear) are established between the satellite NDVI index acquired from SPOT/HRV images, just before the time of maximum growth (April), and grain and straw, for barley and wheat vegetation covers. Following validation of the proposed empirical approaches, yield maps are produced for the studied site. The second approach is based on the application of a Simple Algorithm for Yield Estimation (SAFY) growth model, developed to simulate the dynamics of the LAI and the grain yield. An inter-comparison between ground yield measurements and SAFY model simulations reveals that yields are underestimated by this model. Finally, the combination of multi-temporal satellite measurements with the SAFY model estimations is also proposed for the purposes of yield mapping. Although the results produced by the SAFY model are found to be reasonably well correlated with those determined by satellite measurements (NDVI), the grain yields are nevertheless underestimated

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°)

CAROLS: A New Airborne L-Band Radiometer for Ocean Surface and Land Observations

The “Cooperative Airborne Radiometer for Ocean and Land Studies” (CAROLS) L-Band radiometer was designed and built as a copy of the EMIRAD II radiometer constructed by the Technical University of Denmark team. It is a fully polarimetric and direct sampling correlation radiometer. It is installed on board a dedicated French ATR42 research aircraft, in conjunction with other airborne instruments (C-Band scatterometer—STORM, the GOLD-RTR GPS system, the infrared CIMEL radiometer and a visible wavelength camera). Following initial laboratory qualifications, three airborne campaigns involving 21 flights were carried out over South West France, the Valencia site and the Bay of Biscay (Atlantic Ocean) in 2007, 2008 and 2009, in coordination with in situ field campaigns. In order to validate the CAROLS data, various aircraft flight patterns and maneuvers were implemented, including straight horizontal flights, circular flights, wing and nose wags over the ocean. Analysis of the first two campaigns in 2007 and 2008 leads us to improve the CAROLS radiometer regarding isolation between channels and filter bandwidth. After implementation of these improvements, results show that the instrument is conforming to specification and is a useful tool for Soil Moisture and Ocean Salinity (SMOS) satellite validation as well as for specific studies on surface soil moisture or ocean salinity

PPL-138 (BU10038):A bifunctional NOP/mu partial agonist that reduces cocaine self-administration in rats

The search for new and effective treatments for cocaine use disorder (CUD) is a priority. We determined whether PPL-138 (BU10038), a compound with partial agonist activity at both nociceptin opioid peptide (NOP) and mu-opioid receptors, reduces cocaine consumption, reinstatement, and whether the compound itself produces reinforcing effects in rats. Using an intermittent access (IntA) cocaine self-administration procedure, we found that PPL-138 (0.1 and 0.3 mg/kg) effectively decreased the total number of cocaine infusions and burst-like cocaine intake in both male and female rats. Responses for food in an IntA model of food self-administration were not altered for either sex, although locomotor activity was increased in female but not male rats. Blockade of NOP receptors with the selective antagonist J-113397 (5 mg/kg) did not prevent the PPL-138-induced suppression of cocaine self-administration, whereas blockade of mu-opioid receptors by naltrexone (1 mg/kg) reversed such effect. Consistently, treatment with morphine (1, 3, and 10 mg/kg) dose-dependently reduced IntA cocaine self-administration measures. PPL-138 also reduced reinstatement of cocaine seeking at all doses examined. Although an initial treatment with PPL-138 (2.5, 10, and 40 μg/kg/infusion) appeared rewarding, the compound did not maintain self-administration behavior. Animals treated with PPL-138 showed initial suppression of cocaine self-administration, which was eliminated following repeated daily dosing. However, suppression of cocaine self-administration was retained when subsequent PPL-138 treatments were administered 48 h apart. These findings demonstrate that the approach of combining partial NOP/mu-opioid activation successfully reduces cocaine use, but properties of PPL-138 seem to depend on the timing of drug administration.</p

Soil Clay Content Mapping Using a Time Series of Landsat TM Data in Semi-Arid Lands

International audienceClay content (fraction < 2 µm) is one of the most important soil properties. It controls soil hydraulic properties like wilting point, field capacity and saturated hydraulic conductivity, which in turn control the various fluxes of water in the unsaturated zone. In our study site, the Kairouan plain in central Tunisia, existing soil maps are neither exhaustive nor sufficiently precise for water balance modeling or thematic mapping. The aim of this work was to produce a clay-content map at fine spatial resolution over the Kairouan plain using a time series of Landsat Thematic Mapper images and to validate the produced map using independent soil samples, existing soil map and clay content produced by TerraSAR-X radar data. Our study was based on 100 soil samples and on a dataset of four Landsat TM data acquired during the summer season. Relationships between textural indices (MID-Infrared) and topsoil clay content were studied for each selected image and were used to produce clay content maps at a spatial resolution of 30 m. Cokriging was used to fill in the gaps created by green vegetation and crop residues masks and to predict clay content of each pixel of the image at 100 m grid spatial resolution. Results showed that mapping clay content using a OPEN ACCESS Remote Sens. 2015, 7 6060 time series of Landsat TM data is possible and that the produced clay content map presents a reasonable accuracy (R 2 = 0.65, RMSE = 100 g/kg). The produced clay content map is consistent with existing soil map of the studied region. Comparison with clay content map generated from TerraSAR-X radar data on a small area with no calibration point revealed similarities in topsoil clay content over the largest part of this extract, but significant differences for several areas. In-situ observations at those locations showed that the Landsat TM mapping was more consistent with observations than the TerraSAR-X mapping

Use of remote sensing derived parameters in a crop model for biomass prediction of hay crop

International audiencePre-harvest yield forecasting is a critical challenge for producers, especially for large agricultural areas. During previous decades, numerous crop models were developed to predict crop growth and yield at daily time, most often for wheat or maize, and also for grasslands. Crop models require several input parameters that describe soil properties (e.g. field capacity), plant characteristics (e.g. maximal rooting depth) and management options (e.g. sowing dates, irrigation and harvest dates), which are referred to as the soil, plant and management families of parameters. Remote sensing technology has been extensively applied to identify spatially distributed values of some of the accessible parameters in the soil, plant and management families. The aim of this study was to address the feasibility, merits and limitations of forcing remote-sensing-derived parameters (LAI values, harvest and irrigation dates) in the PILOTE crop model, targeting the Total Dry Matter (TDM) of hay crops. Results show that optical images are suitable to feed PILOTE with LAI values without inducing significant errors on the predicted Total Dry Matter (TDM) values (Root Mean Square Error "RMSE" = 0.41 t/ha and Mean Absolute Percentage Error "MAPE" = 22%). Moreover, optical images with revisit times lower than 16 days are adequate to feed PILOTE with remotely sensed harvest dates (RMSE < 0.44 t/ha, MAPE < 10.8%). Finally, feeding PILOTE with noisy irrigation dates that were estimated from SAR images also enabled reliable model predictions, at least when attaching a random uncertainty of "only" 3 days to the real known irrigation dates. The case of one or several undetected irrigations has also been explored, with the expected conclusion that undetected irrigations significantly affect model predictions only in dry periods. For the tested soil properties and climatic conditions, a maximum underestimation of TDM of approximately 1.55 t/ha (reference TDM of 3.43 t/ha) was observed in the second crop growth cycle when ignoring two irrigations out of four in this same cycle

Integration of remote sensing derived parameters in crop models: application to the PILOTE model for hay production

International audienceThe aim of this study is to assess the effects and interests of integrating remote-sensing-derived param-eters (LAI, harvest and irrigation dates) in a crop model (PILOTE) that simulates vegetation growth forhay crops. The target variable is the prediction of Total Dry Matter (TDM) production in each of the threegrowth cycles.Two scenarios are employed to process the available remotely sensed LAI values, predicting TDM valueswhen forcing in PILOTE either the initial and maximal optical LAI-values, or the initial, maximal and dailyinterpolated LAI values. The predictions show low deviations compared with the in situ TDM values (RMSEof 0.44 t/ha, MAPE of 23%).The feasibility of using harvest dates that are derived from optical data is examined by feeding themodel with randomly perturbed harvest dates. The magnitude of the perturbations is equal to the revisittimes of the current optical sensors. Optical images with revisit times lower than 16 days are adequateto feed PILOTE with remotely sensed harvest dates.Emphasis is placed on the forcing of 'uncertain' irrigation dates, derived from Synthetic Aperture Radarimages either replacing all true irrigation dates by randomly perturbed dates (using 3-day perturbationmagnitudes) or hypothesizing one or several irrigations are 'missed' (undetected). The results shownegligible errors for the TDM predictions when noisy irrigation dates are used (RMSE of 0.17 t/ha andMAPE of 4.2%). Disregarding one or two irrigations within a period with important rainfalls does notinduce significant errors for the predicted TDM values; however, it causes noticeable underestimationsin drier periods (maximum of 1.55 t/ha, reference TDM of 3.43 t/ha).This study enables the identification of a series of conditions in which remote-sensing-derived param-eters are suitable to feed the PILOTE model without endangering the reliability of its predictions

Integration of remote sensing derived parameters in a crop model: case of hay

International audienceThe aim of this study is to assess the interests of integrating remote-sensing-derived parameters (LAI, harvest and irrigation dates) in a crop model (PILOTE) that simulates vegetation growth for hay crop