The MISTIGRI thermal infrared project: scientific objectives and mission specifications

Lagouarde, Jean‐Pierre; Bach, Michel; Sobrino, José A.; Boulet, G.; Briottet, Xavier; Cherchali, S.; Coudert, Bruno; Dadou, Isabelle; Dedieu, Gérard; Gamet, Philippe; Hagolle, Olivier; Jacob, Frédéric; Nerry, Françoise; Olioso, Albert; Ottlé, Catherine; Roujean, Jean‐Louis; Fargant, G.

doi:10.1080/01431161.2012.716921

Cited by 60 publications

(63 citation statements)

References 112 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As expected, surface stress is much higher for the rainfed than for the irrigated wheat field. The scatter is quite large, therefore showing the intrinsic limit of stress retrieval from naturally noisy TIR data, as already pointed out by numerous studies (Gentine et al, 2010;Katul et al, 1998;Lagouarde et al, 2013Lagouarde et al, , 2015 Some points with little to no evaporation attest to the difficulty in representing accurately the conditions close to the potential levels and might be related to the theoretical limit of the model for small vegetation stress values illustrated in Fig. 3, especially at low evaporation efficiencies.…”

Section: Water Stress Estimatesmentioning

confidence: 59%

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

Boulet

Mougenot

Lhomme

et al. 2015

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract.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.

show abstract

Section: Water Stress Estimatesmentioning

confidence: 59%

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

Boulet

Mougenot

Lhomme

et al. 2015

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…However, the availability of ASTER and Landsat data over time (16 days) makes the use of the disaggregated soil moisture in agriculture questionable. In fact, more frequent (few days) disaggregated soil moisture estimates are required in order to monitor the water availability for plants and, hence, to schedule the time and water amount of irrigation [26]. The C-band backscatter data acquired by the synthetic aperture radar (SAR) sensor aboard Sentinel-1 satellite is a potential candidate for disaggregating the 1 km DISPATCH products to 100 m resolution due to the shorter revisit time and the free availability to public users.…”

Section: Introductionmentioning

confidence: 99%

Disaggregation of SMOS Soil Moisture to 100 m Resolution Using MODIS Optical/Thermal and Sentinel-1 Radar Data: Evaluation over a Bare Soil Site in Morocco

et al. 2017

View full text Add to dashboard Cite

Abstract:The 40 km resolution SMOS (Soil Moisture and Ocean Salinity) soil moisture, previously disaggregated at a 1 km resolution using the DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) method based on MODIS optical/thermal data, is further disaggregated to 100 m resolution using Sentinel-1 backscattering coefficient (σ • ). For this purpose, three distinct radar-based disaggregation methods are tested by linking the spatio-temporal variability of σ • and soil moisture data at the 1 km and 100 m resolution. The three methods are: (1) the weight method, which estimates soil moisture at 100 m resolution at a certain time as a function of σ • ratio (100 m to 1 km resolution) and the 1 km DISPATCH products of the same time; (2) the regression method which estimates soil moisture as a function of σ • where the regression parameters (e.g., intercept and slope) vary in space and time; and (3) the Cumulative Distribution Function (CDF) method, which estimates 100 m resolution soil moisture from the cumulative probability of 100 m resolution backscatter and the maximum to minimum 1 km resolution (DISPATCH) soil moisture difference. In each case, disaggregation results are evaluated against in situ measurements collected between 1 January 2016 and 11 October 2016 over a bare soil site in central Morocco. The determination coefficient (R 2 ) between 1 km resolution DISPATCH and localized in situ soil moisture is 0.31. The regression and CDF methods have marginal effect on improving the DISPATCH accuracy at the station scale with a R 2 between remotely sensed and in situ soil moisture of 0.29 and 0.34, respectively. By contrast, the weight method significantly improves the correlation between remotely sensed and in situ soil moisture with a R 2 of 0.52. Likewise, the soil moisture estimates show low root mean square difference with in situ measurements (RMSD = 0.032 m 3 m −3 ).

show abstract

“…Before the advent of thermal infrared missions with shorter revisit cycles (Lagouarde et al, 2013), several techniques could be used to disaggregate low resolution (e.g., MODIS) temperature data at high temporal resolution (e.g., Merlin et al, 2010Merlin et al, , 2012a prior to running SEB-1S at high spatiotemporal resolution.…”

Section: Discussionmentioning

confidence: 99%

An original interpretation of the wet edge of the surface temperature–albedo space to estimate crop evapotranspiration (SEB-1S), and its validation over an irrigated area in northwestern Mexico

Merlin

2013

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. The space defined by the pair surface temperature (T ) and surface albedo (α), and the space defined by the pair T and fractional green vegetation cover (f vg ) have been extensively used to estimate evaporative fraction (EF) from solar/thermal remote sensing data. In both space-based approaches, evapotranspiration (ET) is estimated as remotely sensed EF times the available energy. For a given data point in the T − α space or in the T − f vg space, EF is derived as the ratio of the distance separating the point from the line identified as the dry edge to the distance separating the dry edge and the line identified as the wet edge. The dry and wet edges are classically defined as the upper and lower limit of the spaces, respectively. When investigating side by side the T −α and the T −f vg spaces, one observes that the range covered by T values on the (classically determined) wet edge is different for both spaces. In addition, when extending the wet and dry lines of the T − α space, both lines cross at α ≈ 0.4 although the wet and dry edges of the T − f vg space never cross for 0 ≤ f vg < 1. In this paper, a new ET (EF) model (SEB-1S) is derived by revisiting the classical physical interpretation of the T − α space to make its wet edge consistent with that of the T − f vg space. SEB-1S is tested over a 16 km by 10 km irrigated area in northwestern Mexico during the 2007-2008 agricultural season. The classical T − α space-based model is implemented as benchmark to evaluate the performance of SEB-1S. Input data are composed of ASTER (Advanced Spaceborne Thermal Emission and Reflection radiometer) thermal infrared, Formosat-2 shortwave, and station-based meteorological data. The fluxes simulated by SEB-1S and the classical T − α space-based model are compared on seven ASTER overpass dates with the in situ measurements collected at six locations within the study domain. The ET simulated by SEB-1S is significantly more accurate and robust than that predicted by the classical T − α space-based model. The correlation coefficient and slope of the linear regression between simulated and observed ET is improved from 0.82 to 0.93, and from 0.63 to 0.90, respectively. Moreover, constraining the wet edge using air temperature data improves the slope of the linear regression between simulated and observed ET.

show abstract

The MISTIGRI thermal infrared project: scientific objectives and mission specifications

Cited by 60 publications

References 112 publications

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

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

Disaggregation of SMOS Soil Moisture to 100 m Resolution Using MODIS Optical/Thermal and Sentinel-1 Radar Data: Evaluation over a Bare Soil Site in Morocco

An original interpretation of the wet edge of the surface temperature–albedo space to estimate crop evapotranspiration (SEB-1S), and its validation over an irrigated area in northwestern Mexico

Contact Info

Product

Resources

About