Soil surface moisture estimation over a semi-arid region using ENVISAT ASAR radar data for soil evaporation evaluation

The present paper proposes a method for the evaluation of soil evaporation, using soil moisture estimations based on radar satellite measurements. We present firstly an approach for the estimation and monitoring of soil moisture in a semi-arid region in North Africa, using ENVISAT ASAR images, over two types of vegetation covers. The first mapping process is dedicated solely to the monitoring of moisture variability related to rainfall events, over areas in the "non-irrigated olive tree" class of land use. The developed approach is based on a simple linear relationship between soil moisture and the backscattered radar signal normalised at a reference incidence angle. The second process is proposed over wheat fields, using an analysis of moisture variability due to both rainfall and irrigation. A semi-empirical model, based on the water-cloud model for vegetation correction, is used to retrieve soil moisture from the radar signal. Moisture mapping is carried out over wheat fields, showing high variability between irrigated and non-irrigated wheat covers. This analysis is based on a large database, including both ENVISAT ASAR and simultaneously acquired ground-truth measurements (moisture, vegetation, roughness), during the 2008–2009 vegetation cycle. Finally, a semi-empirical approach is proposed in order to relate surface moisture to the difference between soil evaporation and the climate demand, as defined by the potential evaporation. Mapping of the soil evaporation is proposed

The SPARSE model for the prediction of water stress and evapotranspiration components from thermal infra-red data and its evaluation over irrigated and rainfed wheat

Evapotranspiration is an important component of the water cycle, especially in semi-arid lands. A way to quantify the spatial distribution of evapotranspiration and water stress from remote-sensing data is to exploit the available surface temperature as a signature of the surface energy balance. Remotely sensed energy balance models enable one to estimate stress levels and, in turn, the water status of continental surfaces. Dual-source models are particularly useful since they allow derivation of a rough estimate of the water stress of the vegetation instead of that of a soil–vegetation composite. They either assume that the soil and the vegetation interact almost independently with the atmosphere (patch approach corresponding to a parallel resistance scheme) or are tightly coupled (layer approach corresponding to a series resistance scheme). The water status of both sources is solved simultaneously from a single surface temperature observation based on a realistic underlying assumption which states that, in most cases, the vegetation is unstressed, and that if the vegetation is stressed, evaporation is negligible. In the latter case, if the vegetation stress is not properly accounted for, the resulting evaporation will decrease to unrealistic levels (negative fluxes) in order to maintain the same total surface temperature. This work assesses the retrieval performances of total and component evapotranspiration as well as surface and plant water stress levels by (1) proposing a new dual-source model named Soil Plant Atmosphere and Remote Sensing Evapotranspiration (SPARSE) in two versions (parallel and series resistance networks) based on the TSEB (Two-Source Energy Balance model, Norman et al., 1995) model rationale as well as state-of-the-art formulations of turbulent and radiative exchange, (2) challenging the limits of the underlying hypothesis for those two versions through a synthetic retrieval test and (3) testing the water stress retrievals (vegetation water stress and moisture-limited soil evaporation) against in situ data over contrasted test sites (irrigated and rainfed wheat). We demonstrated with those two data sets that the SPARSE series model is more robust to component stress retrieval for this cover type, that its performance increases by using bounding relationships based on potential conditions (root mean square error lowered by up to 11 W m−2 from values of the order of 50–80 W m−2), and that soil evaporation retrieval is generally consistent with an independent estimate from observed soil moisture evolution

Evapotranspiration and evaporation/transpiration partitioning with dual source energy balance models in agricultural lands

EvapoTranspiration (ET) is an important component of the water cycle, especially in semi-arid lands. Its quantification is crucial for a sustainable management of scarce water resources. A way to quantify ET is to exploit the available surface temperature data from remote sensing as a signature of the surface energy balance, including the latent heat flux. Remotely sensed energy balance models enable to estimate stress levels and, in turn, the water status of most continental surfaces. The evaporation and transpiration components of ET are also just as important in agricultural water management and ecosystem health monitoring. Single temperatures can be used with dual source energy balance models but rely on specific assumptions on raw levels of plant water stress to get both components out of a single source of information. Additional information from remote sensing data are thus required, either something specifically related to evaporation (such as surface water content) or transpiration (such as PRI or fluorescence). This works evaluates the SPARSE dual source energy balance model ability to compute not only total ET, but also water stress and transpiration/evaporation components. First, the theoretical limits of the ET component retrieval are assessed through a simulation experiment using both retrieval and prescribed modes of SPARSE with the sole surface temperature. A similar work is performed with an additional constraint, the topsoil surface soil moisture level, showing the significant improvement on the retrieval. Then, a flux dataset acquired over rainfed wheat is used to check the robustness of both stress levels and ET retrievals. In particular, retrieval of the evaporation and transpiration components is assessed in both conditions (forcing by the sole temperature or the combination of temperature and soil moisture). In our example, there is no significant difference in the performance of the total ET retrieval, since the evaporation rate retrieved from the sole surface temperature is already fairly close to the one we can reconstruct from observed surface soil moisture time series, but current work is underway to test it over other plots.</p

Global Reverse Supply Chain Redesign for Household Plastic Waste under the Emission Trading Scheme

With increasing global resource scarcity, waste becomes a resource that can be managed globally. A reverse supply chain network for waste recycling needs to process all the waste with minimum costs and environmental impact. As re-processing of waste is one of the major sources of pollution in the recycling processes, a mechanism is needed to control and reduce the emission impact in the re-processing as a key to facilitate the globalized reverse supply chain and avoid spreading pollutants overseas. Emission Trading Schemes (ETS) can function as policy instruments for controlling emissions. The ETS introduces a trade-off between the economic efficiency and the environmental impacts. ETS has been implemented in Europe and is developing rapidly in China too. The aim of the research is to re-design a reverse supply chain from a global angle based on a case study conducted on household plastic waste distributed from Europe to China. Emission trading restrictions are set on the processing plants in both Europe and China. We modeled a network optimization problem using integer programing approach, allowing the re-allocation of intermediate processing plants under emission trading restrictions. Optimization results show that global relocation of re-processors leads to both a reduction of total costs and total transportation emission. ETS applied to re-processors further helps to reduce emissions from both re-processing and transportation sectors. Carbon cap should be carefully set in order to be effective. With a given carbon cap, the model also shows the effective carbon price range. These results give an insight into the feasibility of building a global reverse supply chain for household plastic waste recycling and demonstrate the impact of ETS on the network design

Forecasting of cereal yields in a semi-arid area using the Simple Algorithm for Yield Estimation (SAFY) agro-meteorological model combined with optical SPOT/HRV images

In semi-arid areas characterized by frequent drought events, there is often a strong need for an operational grain yield forecasting system, to help decision-makers with the planning of annual imports. However, monitoring the crop canopy and production capacity of plants, especially for cereals, can be challenging. In this paper, a new approach to yield estimation by combining data from the Simple Algorithm for Yield estimation (SAFY) agro-meteorological model with optical SPOT/High Visible Resolution (HRV) satellite data is proposed. Grain yields are then statistically estimated as a function of Leaf Area Index (LAI) during the maximum growth period between 25 March and 5 April. The LAI is retrieved from the SAFY model, and calibrated using SPOT/HRV data. This study is based on the analysis of a rich database, which was acquired over a period of two years (2010-2011, 2012-2013) at the Merguellil site in central Tunisia (North Africa) from more than 60 test fields and 20 optical satellite SPOT/HRV images. The validation and calibration of this methodology is presented, on the basis of two subsets of observations derived from the experimental database. Finally, an inversion technique is applied to estimate the overall yield of the entire studied site

Potential of X-Band TerraSAR-X and COSMO-SkyMed SAR Data for the Assessment of Physical Soil Parameters

International audienceThe aim of this paper is to analyze the potential of X-band SAR measurements (COSMO-SkyMed and TerraSAR-X) made over bare soils for the estimation of soil moisture and surface geometry parameters at a semi-arid site in Tunisia (North Africa). Radar signals acquired with different configurations (HH and VV polarizations, incidence angles of 26° and 36°) are statistically compared with ground measurements (soil moisture and roughness parameters). The radar measurements are found to be highly sensitive to the various soil parameters of interest. A linear relationship is determined for the radar signals as a function of volumetric soil moisture, and a logarithmic correlation is observed between the radar signals and three surface roughness parameters: the root mean square height (Hrms), the parameter Zs = Hrms2/l (where l is the correlation length) and the parameter Zg = Hrms × (Hrms/l)alpha (where alpha is the power of the surface height correlation function). The highest dynamic sensitivity is observed for Zg at high incidence angles. Finally, the performance of different physical and semi-empirical backscattering models (IEM, Baghdadi-calibrated IEM and Dubois models) is compared with SAR measurements.The results provide an indication of the limits of validity of the IEM and Dubois models, for various radar configurations and roughness conditions. Considerable improvements in the IEM model performance are observed using the Baghdadi-calibrated version of this model

Global Reverse Supply Chain Redesign for Household Plastic Waste under the Emission Trading Scheme

With increasing global resource scarcity, waste becomes a resource that can be managed globally. A reverse supply chain network for waste recycling needs to process all the waste with minimum costs and environmental impact. As re-processing of waste is one of the major sources of pollution in the recycling processes, a mechanism is needed to control and reduce the emission impact in the re-processing as a key to facilitate the globalized reverse supply chain and avoid spreading pollutants overseas. Emission Trading Schemes (ETS) can function as policy instruments for controlling emissions. The ETS introduces a trade-off between the economic efficiency and the environmental impacts. ETS has been implemented in Europe and is developing rapidly in China too. The aim of the research is to re-design a reverse supply chain from a global angle based on a case study conducted on household plastic waste distributed from Europe to China. Emission trading restrictions are set on the processing plants in both Europe and China. We modeled a network optimization problem using integer programing approach, allowing the re-allocation of intermediate processing plants under emission trading restrictions. Optimization results show that global relocation of re-processors leads to both a reduction of total costs and total transportation emission. ETS applied to re-processors further helps to reduce emissions from both re-processing and transportation sectors. Carbon cap should be carefully set in order to be effective. With a given carbon cap, the model also shows the effective carbon price range. These results give an insight into the feasibility of building a global reverse supply chain for household plastic waste recycling and demonstrate the impact of ETS on the network design

Retrieval of both soil moisture and texture using TerraSAR-X images

The aim of this paper is to propose a methodology combing multi-temporal X-band SAR images (TerraSAR-X) with continuous ground thetaprobe measurements, for the retrieval of surface soil moisture and texture at a high spatial resolution. Our analysis is based on seven radar images acquired at a 36 degrees incidence angle in the HH polarization, over a semi-arid site in Tunisia (North Africa). The soil moisture estimations are based on an empirical change detection approach using TerraSAR-X data and ground auxiliary thetaprobe network measurements. Two assumptions were tested: (1) roughness variations during the three-month radar acquisition campaigns were not accounted for; (2) a simple correction for temporal variations in roughness was included. The results reveal a small improvement in the estimation of soil moisture when a correction for temporal variations in roughness is introduced. By considering the estimated temporal dynamics of soil moisture, a methodology is proposed for the retrieval of clay and sand content (expressed as percentages) in soil. Two empirical relationships were established between the mean moisture values retrieved from the seven acquired radar images and the two soil texture components over 36 test fields. Validation of the proposed approach was carried out over a second set of 34 fields, showing that highly accurate clay estimations can be achieved. Maps of soil moisture, clay and sand percentages at the studied site are derived

Preliminary Results on the Use of Leather Chrome Shavings for Air Passive Sampling

A new passive sampler based on low-density polyethylene (LDPE) layflat tube filled with chrome shavings from tannery waste residues was evaluated to determine volatile organic compounds (VOCs) in indoor and outdoor areas. VOCs were directly determined by head space-gas chromatography-mass spectrometry (HS-GC-MS) without any pretreatment of the sampler and avoiding the use of solvents. Limit of detection values ranging from 20 to 75 ng sampler−1 and good repeatability values were obtained for VOCs under study with relative standard deviation values from 2.8 to 9.6% except for carbon disulfide for which it was 22.5%. The effect of the amount of chrome shavings per sampler was studied and results were compared with those obtained using empty LDPE tubes, to demonstrate the capacity of chrome shavings to adsorb VOCs