ASSESSING INTER-SEASONAL VARIATIONS OF VEGETATION COVER AND LAND SURFACE TEMPERATURE IN THE NCR USING MODIS DATA

Abstract. Thermal-based two-source energy balance modeling is very useful for estimating the land evapotranspiration (ET) at a wide range of spatial and temporal scales. However, the land surface temperature (LST) is not sufficient for constraining simultaneously both soil and vegetation flux components in such a way that assumptions (on either the soil or the vegetation fluxes) are commonly required. To avoid such assumptions, a new energy balance model (TSEB-SM) was recently developed in Ait Hssaine et al. (2018a) to integrate the microwave-derived near-surface soil moisture (SM), in addition to the thermal-derived LST and vegetation cover fraction (fc). Whereas, TSEB-SM has been recently tested using in-situ measurements, the objective of this paper is to evaluate the performance of TSEB-SM in real-life using 1 km resolution MODIS (Moderate resolution imaging spectroradiometer) LST and fc data and the 1 km resolution SM data disaggregated from SMOS (Soil Moisture and Ocean Salinity) observations by using DisPATCh. The approach is applied during a four-year period (2014–2018) over a rainfed wheat field in the Tensift basin, central Morocco, during a four-year period (2014–2018). The field was seeded for the 2014–2015 (S1), 2016–2017 (S2) and 2017–2018 (S3) agricultural season, while it was not ploughed (remained as bare soil) during the 2015–2016 (B1) agricultural season. The mean retrieved values of (arss, brss) calculated for the entire study period using satellite data are (7.32, 4.58). The daily calibrated αPT ranges between 0 and 1.38 for both S1 and S2. Its temporal variability is mainly attributed to the rainfall distribution along the agricultural season. For S3, the daily retrieved αPT remains at a mostly constant value (∼ 0.7) throughout the study period, because of the lack of clear sky disaggregated SM and LST observations during this season. Compared to eddy covariance measurements, TSEB driven only by LST and fc data significantly overestimates latent heat fluxes for the four seasons. The overall mean bias values are 119, 94, 128 and 181 W/m2 for S1, S2, S3 and B1 respectively. In contrast, these errors are much reduced when using TSEB-SM (SM and LST combined data) with the mean bias values estimated as 39, 4, 7 and 62 W/m2 for S1, S2, S3 and B1 respectively.

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Role of vegetation in representing land surface temperature in the CHTESSEL (CY45R1) and SURFEX-ISBA (v8.1) land surface models: a case study over Iberia

Geoscientific Model Development ◽

10.5194/gmd-13-3975-2020 ◽

2020 ◽

Vol 13 (9) ◽

pp. 3975-3993 ◽

Cited By ~ 1

Author(s):

Miguel Nogueira ◽

Clément Albergel ◽

Souhail Boussetta ◽

Frederico Johannsen ◽

Isabel F. Trigo ◽

...

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Vegetation Cover ◽

Land Surface ◽

Added Value ◽

Vegetation Coverage ◽

Daily Maximum ◽

Cold Bias ◽

Weather Forecasts ◽

Surface Models

Abstract. Earth observations were used to evaluate the representation of land surface temperature (LST) and vegetation coverage over Iberia in two state-of-the-art land surface models (LSMs) – the European Centre for Medium-Range Weather Forecasts (ECMWF) Carbon-Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (CHTESSEL) and the Météo-France Interaction between Soil Biosphere and Atmosphere model (ISBA) within the SURface EXternalisée modeling platform (SURFEX-ISBA) for the 2004–2015 period. The results showed that the daily maximum LST simulated by CHTESSEL over Iberia was affected by a large cold bias during summer months when compared against the Satellite Application Facility on Land Surface Analysis (LSA-SAF), reaching magnitudes larger than 10 ∘C over wide portions of central and southwestern Iberia. This error was shown to be tightly linked to a misrepresentation of the vegetation cover. In contrast, SURFEX simulations did not display such a cold bias. We show that this was due to the better representation of vegetation cover in SURFEX, which uses an updated land cover dataset (ECOCLIMAP-II) and an interactive vegetation evolution, representing seasonality. The representation of vegetation over Iberia in CHTESSEL was improved by combining information from the European Space Agency Climate Change Initiative (ESA-CCI) land cover dataset with the Copernicus Global Land Service (CGLS) leaf area index (LAI) and fraction of vegetation coverage (FCOVER). The proposed improvement in vegetation also included a clumping approach that introduces seasonality to the vegetation cover. The results showed significant added value, removing the daily maximum LST summer cold bias completely, without reducing the accuracy of the simulated LST, regardless of season or time of the day. The striking performance differences between SURFEX and CHTESSEL were fundamental to guiding the developments in CHTESSEL highlighting the importance of using different models. This work has important implications: first, it takes advantage of LST, a key variable in surface–atmosphere energy and water exchanges, which is closely related to satellite top-of-atmosphere observations, to improve the model's representation of land surface processes. Second, CHTESSEL is the land surface model employed by ECMWF in the production of their weather forecasts and reanalysis; hence systematic errors in land surface variables and fluxes are then propagated into those products. Indeed, we showed that the summer daily maximum LST cold bias over Iberia in CHTESSEL is present in the widely used ECMWF fifth-generation reanalysis (ERA5). Finally, our results provided hints about the interaction between vegetation land–atmosphere exchanges, highlighting the relevance of the vegetation cover and respective seasonality in representing land surface temperature in both CHTESSEL and SURFEX. As a whole, this work demonstrated the added value of using multiple earth observation products for constraining and improving weather and climate simulations.

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Land surface temperature and vegetation cover changes and their relationships in Taiwan from 2000 to 2020

Remote Sensing Applications Society and Environment ◽

10.1016/j.rsase.2021.100636 ◽

2021 ◽

pp. 100636

Author(s):

Sahidan Abdulmana ◽

Apiradee Lim ◽

Sangdao Wongsai ◽

Noppachai Wongsai

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Vegetation Cover ◽

Land Surface

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Evaluation of A Physically-Based Passive Microwave Land Surface Temperature Retrieval Algorithm Using MODIS Data

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2019.8900145 ◽

2019 ◽

Author(s):

Cheng Huang ◽

Xiaoping Zhang ◽

Caixia Gao ◽

Si-Bo Duan ◽

Xiao-Guang Jiang ◽

...

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Passive Microwave ◽

Retrieval Algorithm ◽

Modis Data ◽

Physically Based ◽

Temperature Retrieval

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Evaluating the Temperature Difference Parameter in the SSEBop Model with Satellite-Observed Land Surface Temperature Data

Remote Sensing ◽

10.3390/rs11161947 ◽

2019 ◽

Vol 11 (16) ◽

pp. 1947 ◽

Cited By ~ 2

Author(s):

Lei Ji ◽

Gabriel B. Senay ◽

Naga M. Velpuri ◽

Stefanie Kagone

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Temperature Difference ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Weather Data ◽

Climate Data ◽

Modis Data ◽

South Central

The Operational Simplified Surface Energy Balance (SSEBop) model uses the principle of satellite psychrometry to produce spatially explicit actual evapotranspiration (ETa) with remotely sensed and weather data. The temperature difference (dT) in the model is a predefined parameter quantifying the difference between surface temperature at bare soil and air temperature at canopy level. Because dT is derived from the average-sky net radiation based primarily on climate data, validation of the dT estimation is critical for assuring a high-quality ETa product. We used the Moderate Resolution Imaging Spectroradiometer (MODIS) data to evaluate the SSEBop dT estimation for the conterminous United States. MODIS data (2008–2017) were processed to compute the 10-year average land surface temperature (LST) and normalized difference vegetation index (NDVI) at 1 km resolution and 8-day interval. The observed dT (dTo) was computed from the LST difference between hot (NDVI < 0.25) and cold (NDVI > 0.7) pixels within each 2° × 2° sampling block. There were enough hot and cold pixels within each block to create dTo timeseries in the West Coast and South-Central regions. The comparison of dTo and modeled dT (dTm) showed high agreement, with a bias of 0.8 K and a correlation coefficient of 0.88 on average. This study concludes that the dTm estimation from the SSEBop model is reliable, which further assures the accuracy of the ETa estimation.

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