Development of a 10 year (2001–2010) 0.1° dataset of land-surface energy balance for mainland China

Abstract

Abstract. In the absence of high resolution estimates of the components of surface energy balance for China, we developed an algorithm based on the surface energy balance system (SEBS) to generate a dataset of land-surface energy and water fluxes on a monthly time scale from 2001 to 2010 at a 0.1° × 0.1° spatial resolution by using multi-satellite and meteorological forcing data. A remote-sensing-based method was developed to estimate canopy height, which was used to calculate roughness length and flux dynamics. The land-surface flux dataset was validated against "ground-truth" observations from 11 flux tower stations in China. The estimated fluxes correlate well with the stations' measurements for different vegetation types and climatic conditions (average bias = 15.3 W m−2, RMSE = 26.4 W m−2). The quality of the data product was also assessed against the GLDAS dataset. The results show that our method is efficient for producing a high-resolution dataset of surface energy flux for the Chinese landmass from satellite data. The validation results demonstrate that more accurate downward long-wave radiation datasets are needed to be able to accurately estimate turbulent fluxes and evapotranspiration when using the surface energy balance model. Trend analysis of land-surface radiation and energy exchange fluxes revealed that the Tibetan Plateau has undergone relatively stronger climatic change than other parts of China during the last 10 years. The capability of the dataset to provide spatial and temporal information on water-cycle and land–atmosphere interactions for the Chinese landmass is examined. The product is free to download for studies of the water cycle and environmental change in China.

Evolución de la evapotranspiración y el estrés hídrico de la vegetación de dehesa en la Península Ibérica (2001-2015)

“…En ambas componentes las estimaciones presentan coeficientes de determinación R 2 >0,7. Estos resultados son similares a los obtenidos en aplicaciones previas de este modelo en condiciones parecidas a las de este trabajo: escala mensual, datos de entrada procedentes de sensores remotos y reanálisis; pero en cubiertas predominantemente agrícolas (Chen et al, 2014). Por otro lado, los resultados obtenidos para H y λE mejoran los presentados a escala global por Vinukollu et al (2011) sobre este y otros ecosistemas, y el RMSE de λE es inferior al promedio, de 50 W·m -2 , calculado por Kalma et al (2008) compilando los resultados de 30 validaciones de estimaciones de λE publicadas usando diferentes métodos y tipos de cubierta.…”

“…La integración de estos datos en modelos de balance de energía en superficie permite estimar con bastante precisión y de manera robusta los componentes del balance de energía. Estos modelos utilizan la temperatura radiométri-ca como magnitud primaria para derivar el calor latente o ET (Kustas y Norman, 1996) y en los últimos veinte años se han desarrollado diversas aproximaciones con diferente nivel de empirismo (Norman et al, 1995, Bastiaanssen et al, 1998, Su, 2002 y, en general, buenos resultados en la estimación de la evapotranspiración tanto en cultivos homogéneos como en cubiertas de vegetación dispersa (González-Dugo et al, 2009, Chen et al, 2014, Andreu et al, 2015.…”

Application of a Remote Sensing Method for Estimating Monthly Blue Water Evapotranspiration in Irrigated Agriculture

“…Remote sensing ET actual estimates were obtained from two sources: the Meteosat Second Generation products provided by the Land Surface Analysis-Satellite Applications Facility (LSA-SAF) [35] for the region of Africa (period 2010-2012) and the dataset produced by Chen et al [36,37] over China during the years 2001 till 2010. The periods of study and areas were (partially) determined by the availability of data at the moment of writing this paper.…”

Irrigation assessment via remote sensing and land surface model data

“…The inputs based on remote sensing are LSA-SAF products of albedo, and downwelling short and longwave radiation fluxes (Gellens-Meulenberghs et al, 2007;Ghilain et al, 2011).The dataset from Chen et al (2014) is based on the Surface Energy Balance System (SEBS) (Su, 2002), which uses multi-sensor remote sensing based NDVI, albedo, surface emissivity and temperature.…”

“…Table 1 describes the main characteristics of the datasets used in the present work which are detailed in the following paragraphs. Remote sensing ET estimates were obtained from two sources, the Meteosat Second Generation products provided by the Land Surface Analysis -Satellite Applications Facility (LSA-SAF) (Ghilain et al, 2011) for the region of Africa (period 2010-2012) and the dataset produced by Chen et al (2013Chen et al ( , 2014 over China during the years 2001 till 2010. The periods of study and areas were (partially) determined by the availability of data at the moment of writing this chapter.…”