Estimation of land surface evapotranspiration over complex terrain based on multi‐spectral remote sensing data

Land surface evapotranspiration (ET) plays an important role in energy and water balances. ET can significantly affect the runoff yield of a basin and the available water resources in mountainous areas. The existing models to estimate ET are typically applicable to plains, and excessive data are required to calculate the surface fluxes accurately. This study established a simple and practical model capable of depicting the surface fluxes, while using relatively less parameters. Considering the complex terrain, solar radiation was corrected by importing a series of topographic factors. The water deficit index, a measure of land surface wetness, was calculated by applying the f_c (vegetation fractional cover)‐T_rad (land surface temperature) framework in the two‐source trapezoid model for evapotranspiration model to mountainous areas after corrections of temperature based on altitude variations. The model was successfully applied to the Kaidu River Basin, a basin with few gauges located in the east Tien Shan Mountains of China. Based on the time scale extensions, ET was analyzed at different time scales from 2000 to 2013. The results demonstrated that the corrected solar radiation and water deficit index were reasonably distributed in space and that this model is applicable to ungauged catchments, such as the Kaidu River Basin.     

Improving evapotranspiration estimation models through quantitative watershed parameter analysis and remote sensing applications

Numerous models had been developed to predict the annual evapotranspiration (ET) in vegetated lands across various spatial scales. Fu's (Scientia Atmospherica Sinica, 5, 23–31) and Zhang's (Water Resources Research, 37, 701–708) ET simulation models have emerged as highly effective and have been widely used. However, both formulas have the non-quantitative parameters (m in Fu's model and w in Zhang's model). Based on the collected 1789 samples from global long-term hydrological studies, this study discovered significant relations between m (or w) and vegetation coverage or greenness in collected catchments. Then, we used these relations to qualify the parameters in both Zhang's and Fu's models. Results show that the ET estimation accuracies of Fu's (or Zhang's) model are significantly improved by about 13.49 mm (or 6.74 mm) for grassland and cropland, 38.52 mm (or 29.84 mm) for forest and shrub land (coverage<40%), 19.74 mm (or 16.17 mm) for mixed land (coverage<40%), respectively. However, Zhang's model shows higher errors compared with Fu's model, especially in regions with high m (or w) values, such as those with dense vegetations or P/E₀ (annual precipitation to annual potential ET) smaller than 1.0. Additionally, this study also reveals that for regions with vegetation cover less than 40%, the annual ET is not only determined by vegetation types, but also relates to the sizes of vegetation-covered areas. Conversely, for regions with vegetation cover more than 40%, the annual ET is mainly determined by the vegetation density rather than vegetation types or vegetation coverage. Thus, linking m (or w) parameters with vegetation greenness allows leveraging remote sensing for forest management in data-scarce areas, safeguarding regional water resources. This study pioneers integrating vegetation-related indices with basin parameters, advocating for their crucial role in more effective hydrological modelling.     

Impact of spatial variations of land surface parameters on regional evaporation: a case study with remote sensing data

Most precipitation in watersheds is consumed by evaporation, thus techniques to appraise regional evaporation are important to assess the availability of water resources. Many algorithms to estimate evaporation from remotely sensed spectral data have been developed in the recent past. In addition to differences in the physical parameterization of surface fluxes, these algorithms have different solutions for describing spatial variations of the parameters in the soil–vegetation–atmosphere–transfer (SVAT) continuum. In this study, the necessity to spatially distinguish SVAT parameters for computing surface heat fluxes is analysed for the Naivasha watershed in the Kenyan Rift Valley. Landsat Thematic Mapper (TM) spectral data have been used to first delineate the watershed into 15 hydrological units using surface temperature, normalized difference vegetation index and surface albedo as attributes. Thereafter, semi‐empirical relationships between these TM‐based parameters and other SVAT parameters have been applied to compute the spatial variation of SVAT parameters and the associated evaporation from the different hydrological units. The impact of using watershed‐constant or watershed‐distributed SVAT parameters on the fluxes is analysed. The determination of watershed averaged evaporation with area‐aggregated SVAT parameters is feasible without significant loss of accuracy. Distributed evaporation in heterogeneous watersheds, however, can be investigated only with remote sensing flux algorithms that can account for spatially variable air temperature, surface roughness, surface albedo and the stability correction of the temperature profile due to buoyancy. Erroneous results can be expected if area‐aggregated SVAT parameters are used to calculate local evaporation. As most of the recently developed remote sensing flux algorithms are based on areal constant SVAT parameters, direct applications in watersheds are still limited. Copyright © 2001 John Wiley & Sons, Ltd.     

Study on NDVI-T_s space by combining LAI and evapotranspiration

Vegetation index and land surface temperature (Ts) are important parameters for land surface process modeling. The remotely sensed data in visible/near infrared and thermal infrared wavelengths have proven to be well suited to monitoring vegetation status, soil surface moisture conditions, drought and crop yield. More useful information can be created by integrated analyses of these two kinds of data, which will help us to study main principles of the temporal and spatial variations of land su…     

Estimating growing-season root zone soil moisture from vegetation index-based evapotranspiration fraction and soil properties in the Northwest Mountain region,USA

A soil moisture retrieval method is proposed, in the absence of ground-based auxiliary measurements, by deriving the soil moisture content relationship from the satellite vegetation index-based evapotranspiration fraction and soil moisture physical properties of a soil type. A temperature–vegetation dryness index threshold value is also proposed to identify water bodies and underlying saturated areas. Verification of the retrieved growing season soil moisture was performed by comparative analysis of soil moisture obtained by observed conventional in situ point measurements at the 239-km² Reynolds Creek Experimental Watershed, Idaho, USA (2006–2009), and at the US Climate Reference Network (USCRN) soil moisture measurement sites in Sundance, Wyoming (2012–2015), and Lewistown, Montana (2014–2015). The proposed method best represented the effective root zone soil moisture condition, at a depth between 50 and 100 cm, with an overall average R² value of 0.72 and average root mean square error (RMSE) of 0.042.     

Estimation of annual actual evapotranspiration from nonsaturated land surfaces with conventional meteorological data

Land surface evapotranspiration is an important component both in earth surface heat and water bal-ance, on whose budgets weather and climate depend, to a great extent, for their changes are responsible for the formation and variation of vegetation features on the globe. Besides, the evapotranspiration is an im-portant topic of short-term flood forecasting and the estimation of runoff from mountainous sides. As a result, the problem as to the evapotranspiration has been one of the concerns in …     

Study on NDVI- T s space by combining LAI and evapotranspiration

This paper focuses on interpreting the different spatial relationships between NDVI and T _s, a triangular or a trapezoid, and on analyzing transformation conditions, the physical and ecological meanings of the vegetation index-surface temperature space as well. Further, we use the Temperature-Vegetation Dryness Index (TVDI) to explain the existent meaning of a triangular space after NDVI reaches its saturated state by employing the relationships between NDVI, LAI and evapotranspiration. The specific relations between NDVI and T _s are useful for describing, validating and updating land surface models.     

Comparison of three remote sensing based models for the estimation of latent heat flux over India

The aim of this work is to compare three remote sensing based models: two contextual and one physically-based single-pixel model for the estimation of daytime integrated latent heat flux without the use of any ground measurements over Indian ecosystems. Satellite datasets from the MODIS sensors aboard the Terra and the Aqua satellites were used. The latent heat flux estimated from the remote sensing models was compared with that estimated from Bowen ratio energy balance towers at five sites in India. The root mean square error (RMSE) of the latent heat flux estimated from the contextual and the physically-based models was found to be in the order of 40 and 70 W m^?2, respectively. The relatively inferior performance of the more complex physically-based model in comparison with the contextual models was found to be largely due to inaccurate parameterizations estimated only from remote sensing datasets without any ground data.     

依兰-伊通断裂带地表温度变化分析

GPS结果显示,2011年3月11日日本MW9.0大地震引起了我国东北地区的拉张活动增强,其中,依兰——伊通断裂带最显著．这个变化在温度场中是否也有所响应是一个值得探讨的问题,也是一个利用地表温度进行现今构造活动探索的机会．本文利用2000——2011年的中分辨率成像光谱仪(MODIS)地表温度产品对东北地区地表温度进行了分析,以去除稳定年周期变化的年变残差作为研究对象, 通过空间和时间分析,排除地形、纬度、气象等干扰因素的影响,寻找与构造活动相关的热信息．结果表明, 2001年初和2010年初依兰——伊通断裂北段曾经出现了显著的降温现象,且降温过程持续约两个月．通过气象资料分析,初步认为上述现象并非由气象因素引起．这与GPS远场同震位移结果所显示的依兰——伊通断裂带在日本地震后出现相对比较明显的张性应变相吻合. 初步认为上述降温现象与依兰——伊通断裂的拉张增强有关．      

Trend analysis of reference evapotranspiration in Northwest China: The roles of changing wind speed and surface air temperature

Reference evapotranspiration (ET₀) is an important element in the water cycle that integrates atmospheric demands and surface conditions, and analysis of changes in ET₀ is of great significance for understanding climate change and its impacts on hydrology. As ET₀ is an integrated effect of climate variables, increases in air temperature should lead to increases in ET₀. However, this effect could be offset by decreases in vapor pressure deficit, wind speed, and solar radiation which lead to the decrease in ET₀. In this study, trends in the Penman–Monteith ET₀ at 80 meteorological stations during 1960–2010 in the driest region of China (Northwest China) were examined. The results show that there was a change point for ET₀ series around the year 1993 based on the Pettitt's test. For the region average, ET₀ decreased from 1960 to 1993 by ?2.34 mm yr^?2, while ET₀ began to increase since 1994 by 4.80 mm yr^?2. A differential equation method based on the Food and Agriculture Organization Penman–Monteith formula was used to attribute the change in ET₀. The attribution results show that the significant decrease in wind speed dominated the change in ET₀, which offset the effect of increasing air temperature and led to the decrease in ET₀ from 1960 to 1993. However, wind speed began to increase, and the amplitude of increase in air temperature also rose significantly since the mid‐1990s. Increases in air temperature and wind speed together reversed the trend in ET₀ and led to the increase in ET₀ since 1994. Copyright © 2012 John Wiley & Sons, Ltd.     

A new algorithm for microwave radiometer remote sensing of sea surface salinity and temperature

The microwave radiation of the sea surface, which is denoted by the sea surface brightness temperature, is not only related with sea surface salinity (SSS) and temperature (SST), but also influenced by sea surface wind. The errors of wind detected by satellite sensor have significant influences on the accuracy of SSS and SST retrieval. The effects of sea surface wind on sea surface brightness temperature, i.e. △Th,v, and the relations among △Th,v, wind speed, sea surface tempera- ture, sea surface salinity and incidence angle of observation are investigated. Based on the investi- gations, a new algorithm depending on the design of a single radiometer with double polarizations and multi-incidence angles is proposed. The algorithm excludes the influence of sea surface wind on SSS and SST retrieval, and provides a new method for remote sensing of SSS and SST.     

Effect of spatial resolution of satellite images on estimating the greenness and evapotranspiration of urban green spaces

Urban green spaces (UGS), like most managed land covers, are getting progressively affected by water scarcity and drought. Preserving, restoring and expanding UGS require sustainable management of green and blue water resources to fulfil evapotranspiration (ET) demand for green plant cover. The heterogeneity of UGS with high variation in their microclimates and irrigation practices builds up the complexity of ET estimation. In oversized UGS, areas too large to be measured with in situ ET methods, remote sensing (RS) approaches of ET measurement have the potential to estimate the actual ET. Often in situ approaches are not feasible or too expensive. We studied the effects of spatial resolution using different satellite images, with high-, medium- and coarse-spatial resolutions, on the greenness and ET of UGS using Vegetation Indices (VIs) and VI-based ET, over a 780-ha urban park in Adelaide, Australia. We validated ET with the ground-based ET method of Soil Water Balance. Three sets of imagery from WorldView2, Landsat and MODIS, and three VIs including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Enhanced Vegetation Index 2 (EVI2), were used to assess long-term changes of VIs and ET calculated from the different imagery acquired for this study (2011–2018). We found high correspondence between ET-MODIS and ET-Landsat (R² > 0.99 for all VIs). Landsat-VIs captured the seasonal changes of greenness better than MODIS-VIs. We used artificial neural network (ANN) to relate the RS-ET and ground data, and ET-MODIS (EVI2) showed the highest correlation (R² = 0.95 and MSE =0.01 for validation). We found a strong relationship between RS-ET and in situ measurements, even though it was not explicable by simple regressions; black box models helped us to explore their correlation. The methodology used in this research makes a strong case for the value of remote sensing in estimating and managing ET of green spaces in water-limited cities.     

A simple temperature method for the estimation of evapotranspiration

Accurate estimation of evapotranspiration (ET) is essential in water resources management and hydrological practices. Estimation of ET in areas, where adequate meteorological data are not available, is one of the challenges faced by water resource managers. Hence, a simplified approach, which is less data intensive, is crucial. The FAO‐56 Penman–Monteith (FAO‐56 PM) is a sole global standard method, but it requires numerous weather data for the estimation of reference ET. A new simple temperature method is developed, which uses only maximum temperature data to estimate ET. Ten class I weather stations data were collected from the National Meteorological Agency of Ethiopia. This method was compared with the global standard PM method, the observed Piche evaporimeter data, and the well‐known Hargreaves (HAR) temperature method. The coefficient of determination (R²) of the new method was as high as 0.74, 0.75, and 0.91, when compared with that of PM reference evapotranspiration (ET_o), Piche evaporimeter data, and HAR methods, respectively. The annual average R² over the ten stations when compared with PM, Piche, and HAR methods were 0.65, 0.67, and 0.84, respectively. The Nash–Sutcliff efficiency of the new method compared with that of PM was as high as 0.67. The method was able to estimate daily ET with an average root mean square error and an average absolute mean error of 0.59 and 0.47 mm, respectively, from the PM ET_o method. The method was also tested in dry and wet seasons and found to perform well in both seasons. The average R² of the new method with the HAR method was 0.82 and 0.84 in dry and wet seasons, respectively. During validation, the average R² and Nash–Sutcliff values when compared with Piche evaporation were 0.67 and 0.51, respectively. The method could be used for the estimation of daily ET_o where there are insufficient data. Copyright © 2013 John Wiley & Sons, Ltd.     

Monitoring of water surface temperature of Eurasian large lakes using MODIS land surface temperature product

In this study, data from MODIS land surface temperature product level 3 (MOD11A2) were used to investigate the spatiotemporal variation of Eurasian lakes water surface temperature (LSWT) from 2001 to 2015, and to examine the most influencing factors of that variation. The temperature of most lakes in the dry climate zone and in the equatorial climatic zone varied from 17 to 31°C and from 23 to 27°C, respectively. LSWTs in the warm temperate and cold climatic zones were in the range of 20 to 27°C and −0.6 and 17°C, respectively. The average day time LSWT in the polar climate zone was −0.71°C in the summer. Lakes in high latitude and in the Tibetan Plateau displayed low LSWT, ranging from −11 to 26°C during the night time. Large spatial variations of diurnal temperature difference (DTD) were observed in lakes across Eurasia. However, variations in DTDs were small in lakes located in high latitude and in tropical rainforest regions. The shallow lakes showed a rapid response of LSWT to solar and atmospheric forcing, while in the large and deep lakes, that response was sluggish. Results of this study demonstrated the applicability of remote sensing and MODIS LST products to capture the spatial–temporal variability of LSWT across continental scales, in particular for the vast wilderness areas and protected environment in high latitude regions of the world. The approach can be used in future studies examining processes and factors controlling large scale variability of LSWT.     

Performance evaluation of satellite-based approaches for the estimation of daily air temperature and reference evapotranspiration

Different satellite-based radiation (Makkink) and temperature (Hargreaves-Samani, Penman-Monteith temperature, PMT) reference evapotranspiration (ET_o) models were compared with the FAO56-PM method over the Cauvery basin, India. Maximum air temperature (T_max) required in the ET_o models was estimated using the temperature–vegetation index (TVX) and an advanced statistical approach (ASA), and evaluated with observed T_max obtained from automatic weather stations. Minimum air temperature (T_min) was estimated using ASA. Land surface temperature was employed in the ET_o models in place of air temperature (T_a) to check the potency of its applicability. The results suggest that the PMT model with T_a as input performed better than the other ET_o models, with correlation coefficient (r), averaged root mean square error (RMSE) and mean bias error (MBE) of 0.77, 0.80 mm d^?1 and ?0.69 for all land cover classes. The ASA yielded better T_max and T_min values (r and RMSE of 0.87 and 2.17°C, and 0.87 and 2.27°C, respectively).     

High-resolution remote sensing mapping of global land water

Land water, one of the important components of land cover, is the indispensable and important basic information for climate change studies, ecological environment assessment, macro-control analysis, etc. This article describes the overall study on land water in the program of global land cover remote sensing mapping. Through collection and processing of Landsat TM/ETM+, China’s HJ-1 satellite image, etc., the program achieves an effective overlay of global multi-spectral image of 30 m resolution for two base years, namely, 2000 and 2010, with the image rectification accuracy meeting the requirements of 1:200000 mapping and the error in registration of images for the two periods being controlled within 1 pixel. The indexes were designed and selected reasonably based on spectral features and geometric shapes of water on the scale of 30 m resolution, the water information was extracted in an elaborate way by combining a simple and easy operation through pixel-based classification method with a comprehensive utilization of various rules and knowledge through the object-oriented classification method, and finally the classification results were further optimized and improved by the human-computer interaction, thus realizing high-resolution remote sensing mapping of global water. The completed global land water data results, including Global Land 30-water 2000 and Global Land 30-water 2010, are the classification results featuring the highest resolution on a global scale, and the overall accuracy of self-assessment is 96%. These data are the important basic data for developing relevant studies, such as analyzing spatial distribution pattern of global land water, revealing regional difference, studying space-time fluctuation law, and diagnosing health of ecological environment.     

基于遥感特征指数的地表水体自动提取技术研究

为了从海量遥感数据中有效地提取地表水体信息,并提高自动化提取效率,提出了一种基于遥感特征指数的地表水体自动提取方法.该方法选取归一化植被指数(NDVI)、归一化建筑指数(NDBI)和修正归一化水体指数(MNDWI)作为遥感特征指数集,并根据这些指数构建了遥感特征指数曲线.通过分析,发现地表水体在特征曲线中单调上升,植被在特征曲线中单调下降,而其它地物并无此特征.因此,根据这一规律,利用ERDAS IMAGINE软件建立了自动化提取模型.通过与其他方法对比,表明所建立的模型在精度和自动化方面都明显优于其他方法,可用于海量数据地表水体的自动提取.最后,在ARCGIS环境下,并通过决策树模型初步实现了地表水体的自动分类.     

山东省地表温度变化与地震活动关系探讨

使用遥感数据探讨山东省地表温度变化与地震活动的关系。选取2000—2016年MODIS地表温度数据,计算山东省地表温度变异系数(CV)并绘图,与活动断裂、地震震中等图层进行空间分析。结果表明,活动断裂与震中附近地表温度的变异系数明显升高,空间标准差降低,说明地震活动较强区域地表温度变化幅度与普遍性均高于其他地区,地表温度变化与地震活动存在一定相关关系。胶东半岛地区的地表温度变化高于鲁西鲁中地区,可能预示着未来该地区地震活动性增强。