Développement d'un modèle physique pour la réponse radar cohérente et polarimétrique de la végétation sahélienne, application à l'estimation de paramètres biophysiques

TOULOUSE3-BU Sciences (315552104) / SudocSudocFranceF

Microwave electromagnetic modelling of Sahelian-grassland

In this paper radar scattering models based on coherent and incoherent formulations for an African grassland (Sahelian) are examined. The coherent model is used to account for the structure of the grass plants and the results are compared with the same model assuming random placement and orientation of scatters, and the Radiative Transfer model. The validity of the three models applied to grass vegetation is determined by comparing the model predictions with ENVISAT ASAR data gathered in 2005 over Sahelian grassland. The Agoufou site, as defined in AMMA project, is selected as the test target and a set of ground data were collected during 2004 and 2005. Through a comprehensive data comparison, it is shown that the coherent scattering model with a generator considering botanical information is the best model to predict the backscattering data that matches ENVISAT measurements well (correlation = 0.92). At low incidence angles (<30°), the radar backscatter shows a strong dependence to soil moisture variations. The analysis of the different contributions leads to study the main scattering mechanisms. For high incidence angles, backscattering coefficient at HH polarization shows a marked seasonal variation associated to grass presence

Radar Remote Sensing of Agricultural Canopies: A Review

Observations from spaceborne radar contain considerable information about vegetation dynamics.The ability to extract this information could lead to improved soil moisture retrievals and the increased capacity to monitor vegetation phenology and water stress using radar data.The purpose of this review paper is to provide an overview of the current state of knowledge with respect to backscatter from vegetated (agricultural) landscapes and to identify opportunities and challenges in this domain.Much of our understanding of vegetation backscatter from agricultural canopies stems from SAR studies to perform field-scale classification and monitoring.Hence, SAR applications, theory, and applications are considered here too.An overview will be provided of the knowledge generated from ground-based and airborne experimental campaigns that contributed to the development of crop classification, crop monitoring, and soil moisture monitoring applications.A description of the current vegetation modeling approaches will be given.A review of current applications of spaceborne radar will be used to illustrate the current state of the art in terms of data utilization.Finally, emerging applications, opportunities and challenges will be identified and discussed.Improved representation of vegetation phenology and water dynamics will be identified as essential to improve soil moisture retrievals, crop monitoring, and for the development of emerging drought/water stress applications.Water Resource

Response of sub-daily L-band backscatter to internal and surface canopy water dynamics

The latest developments in radar mission concepts suggest that subdaily synthetic aperture radar will become available in the next decades. The goal of this study was to demonstrate the potential value of subdaily spaceborne radar for monitoring vegetation water dynamics, which is essential to understand the role of vegetation in the climate system. In particular, we aimed to quantify fluctuations of internal and surface canopy water (SCW) and understand their effect on subdaily patterns of L-band backscatter. An intensive field campaign was conducted in north-central Florida, USA, in 2018. A truck-mounted polarimetric L-band scatterometer was used to scan a sweet corn field multiple times per day, from sowing to harvest. SCW (dew, interception), soil moisture, and plant and soil hydraulics were monitored every 15 min. In addition, regular destructive sampling was conducted to measure seasonal and diurnal variations of internal vegetation water content. The results showed that backscatter was sensitive to both transient rainfall interception events, and slower daily cycles of internal canopy water and dew. On late-season days without rainfall, maximum diurnal backscatter variations of &gt;2 dB due to internal and SCW were observed in all polarizations. These results demonstrate a potentially valuable application for the next generation of spaceborne radar missions.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Water Resource

Estimation of nonfluctuating reservoir inflow from water level observations using methods based on flow continuity

The accurate estimation of true reservoir inflow is important for hydrological forecasting and efficient operation of reservoirs. However, reservoir inflow estimated using the conventional simple water balance method is not always accurate because the estimation is very sensitive to errors in reservoir water level observations and uncertainty in the stage-storage relationship. An analytical method (AM) and a method using the ensemble Kalman filter (EnKF) are proposed to determine nonfluctuating reservoir inflow based on the concept of inflow continuity; that is, that inflow should not change much within a short time period. The AM is developed based on the simultaneous minimization of both the estimated reservoir water level error and the inflow variation. The EnKF, which is built on state equations (inflow continuity and water balance equations) and an observation equation (the reservoir stage-storage relationship), is used to update inflow states by assimilating water level observations. The two proposed methods are evaluated using a synthetic experiment with various conditions including water level observation error, reservoir stage-storage relationship error, and the influence of water surface slope. The AM outperforms the EnKF under all conditions. Case studies of the Gaobazhou and Danjiangkou Reservoirs in China demonstrate that both of the proposed methods can derive an hourly inflow without fluctuations. The results indicate that the AM and the EnKF method can improve reservoir inflow estimation compared with conventional methods

The Estimation of Regional Crop Yield Using Ensemble-Based Four-Dimensional Variational Data Assimilation

To improve crop model performance for regional crop yield estimates, a new four-dimensional variational algorithm (POD4DVar) merging the Monte Carlo and proper orthogonal decomposition techniques was introduced to develop a data assimilation strategy using the Crop Environment Resource Synthesis (CERES)-Wheat model. Two winter wheat yield estimation procedures were conducted on a field plot and regional scale to test the feasibility and potential of the POD4DVar-based strategy. Winter wheat yield forecasts for the field plots showed a coefficient of determination (R2) of 0.73, a root mean square error (RMSE) of 319 kg/ha, and a relative error (RE) of 3.49%. An acceptable yield at the regional scale was estimated with an R2 of 0.997, RMSE of 7346 tons, and RE of 3.81%. The POD4DVar-based strategy was more accurate and efficient than the EnKF-based strategy. In addition to crop yield, other critical crop variables such as the biomass, harvest index, evapotranspiration, and soil organic carbon may also be estimated. The present study thus introduces a promising approach for operationally monitoring regional crop growth and predicting yield. Successful application of this assimilation model at regional scales must focus on uncertainties derived from the crop model, model inputs, data assimilation algorithm, and assimilated observations