Estimation of spatially distributed surface energy fluxes using remotely‐sensed data for agricultural fields

Land surface energy fluxes are required in many environmental studies, including hydrology, agronomy and meteorology. Surface energy balance models simulate microscale energy exchange processes between the ground surface and the atmospheric layer near ground level. Spatial variability of energy fluxes limits point measurements to be used for larger areas. Remote sensing provides the basis for spatial mapping of energy fluxes. Remote‐sensing‐based surface energy flux‐mapping was conducted using seven Landsat images from 1997 to 2002 at four contiguous crop fields located in Polk County, northwestern Minnesota. Spatially distributed surface energy fluxes were estimated and mapped at 30 m pixel level from Landsat Thematic Mapper and Enhanced Thematic Mapper images and weather information. Net radiation was determined using the surface energy balance algorithm for land (SEBAL) procedure. Applying the two‐source energy balance (TSEB) model, the surface temperature and the latent and sensible heat fluxes were partitioned into vegetation and soil components and estimated at the pixel level. Yield data for wheat and soybean from 1997 to 2002 were mapped and compared with latent heat (evapotranspiration) for four of the fields at pixel level. The spatial distribution and the relation of latent heat flux and Bowen ratio (ratio of sensible heat to latent heat) to crop yield were studied. The root‐mean‐square error and the mean absolute percentage of error between the observed and predicted energy fluxes were between 7 and 22 W m⁻² and 12 and 24% respectively. Results show that latent heat flux and Bowen ratio were correlated (positive and negative) to the yield data. Wheat and soybean yields were predicted using latent heat flux with mean R² = 0·67 and 0·70 respectively, average residual means of −4·2 bushels/acre and 0·11 bushels/acre respectively, and average residual standard deviations of 16·2 bushels/acre and 16·6 bushels/acre respectively (1 bushel/acre ≈ 0·087 m³ ha⁻¹). The flux estimation procedure from the SEBAL‐TSEB model was useful and applicable to agricultural fields. Copyright © 2005 John Wiley & Sons, Ltd.     

Lysimetric evaluation of SEBAL using high resolution airborne imagery from BEAREX08

In this study, Surface Energy Balance Algorithm for Land (SEBAL) was evaluated for its ability to derive aerodynamic components and surface energy fluxes from very high resolution airborne remote sensing data acquired during the Bushland Evapotranspiration and Agricultural Remote Sensing Experiment 2008 (BEAREX08) in Texas, USA. Issues related to hot and cold pixel selection and the underlying assumptions of difference between air and surface temperature (dT) being linearly related to the surface temperature were also addressed. Estimated instantaneous evapotranspiration (ET) and other components of the surface energy balance were compared with measured data from four large precision weighing lysimeter fields, two each managed under irrigation and dryland conditions. Instantaneous ET was estimated with overall mean bias error and root mean square error (RMSE) of 0.13 and 0.15 mm h⁻¹ (23.8 and 28.2%) respectively, where relatively large RMSE was contributed by dryland field. Sensitivity analysis of the hot and cold pixel selection indicated that up to 20% of the variability in ET estimates could be attributed to differences in the surface energy balance and roughness properties of the anchor pixels. Adoption of an excess resistance to heat transfer parameter model into SEBAL significantly improved the instantaneous ET estimates.     

基于遥感和SEBAL模型的塔里木河干流区蒸散发估算

运用1985 年、2000 年和2010 年遥感资料与SEBAL 模型估算了塔里木河干流区蒸散发。结果表明：该区蒸散发量较大,介于0~5.11 mm/d之间;靠近河道区蒸散发明显大于远离河道区;各土地利用/覆被类型蒸散发大小依次为：水体> 耕地> 林地> 草地> 未利用地> 居工地,主要与其植被覆盖度和水分供给条件有关;而日总蒸散发大小顺序为：草地> 未利用地> 耕地> 林地> 水体> 居工地,这与各土地利用/覆被类型面积密切相关。在1985-2010 年间,塔里木河干流区日总蒸散发量先减小后增大;上游平均日总蒸散发量为中游和下游的1.27 倍和1.42 倍。2000 年塔里木河干流区日总蒸散发比1985 年减少了6.80×10⁴ m³,原因是中游和下游日总蒸散发减小,而上游日总蒸散发量却增加了3.02×10⁵ m³。2010 年干流区日总蒸散发比2000 年高6.78×10⁵ m³,其中上游和中游日总蒸散发量增加了1.19×10⁶ m³,而下游却降低了5.16×10⁵ m³,主要受中上游地区绿洲耕地面积扩张,水资源开发量过大,下游来水量减少的影响。     

利用SEBAL和改进的SEBAL模型估算黑河中游戈壁、绿洲的蒸散发

蒸散发是干旱、半干旱地区内陆河流域水分消耗的主要途径, 利用遥感估算流域尺度上的蒸散发对内陆河流域水循环和水资源的合理利用具有重要的指导意义. 基于2012年开展的黑河流域生态-水文过程综合遥感观测联合试验(HiWATER)的观测资料和高分辨率的ASTER影像, 分别利用 SEBAL 模型和改进的SEBAL(M-SEBAL)模型估算黑河中游不同时期戈壁、绿洲等不同下垫面的蒸散发, 通过涡动观测数据对比分析了SEBAL模型和M-SEBAL模型估算戈壁、绿洲蒸散发的精度. 结果表明: SEBAL模型在绿洲低估感热通量, 高估潜热通量; 在戈壁高估感热通量, 低估潜热通量. M-SEBAL 模型充分考虑不同下垫面地表辐射温度与植被覆盖度之间的关系, 能很好地反映不同植被覆盖区域的湍流通量的异质性, 估算黑河中游戈壁、绿洲蒸散发的精度高于SEBAL模型.     

Study on estimating the evapotranspiration cover coefficient for stream flow simulation through remote sensing techniques

This study focuses on using remote sensing techniques to estimate the evapotranspiration cover coefficient (CV) which is an important parameter for stream flow. The objective is to derive more accurate stream flow from the estimated CV. The study area is located in the Dan-Shuei watershed in northern Taiwan. The processes include the land-use classification using hybrid classification and four Landsat-5 TM images; the CV estimations based on remote sensing and traditional approaches; comparison of stream flow simulation according to the above two CV values. The result indicated that the study area was classified into seven land-use types with 88.3% classification accuracy. The simulated stream flow using remote sensing approach could represent more accurate hydrological characteristics than a traditional approach. Obviously integrating remote sensing technique and the SEBAL model is a useful approach to estimate the CV. The CV parameter estimated by remote sensing technique did improve the accuracy of the stream flow simulation. Therefore, the results can be extended to further studies such as forest water management.     

ET的遥感监测与流域尺度水资源管理

在水资源短缺、水环境质量恶化的形势下,为实现流域的真实节水,需要以卫星遥感监测的ET(蒸发蒸腾量)为依据,进行流域水资源的有效管理。这是一个新的理念和方法。文中综述了当前国际上几种较先进的遥感监测ET的方法。其中“陆面能量平衡算法(SEBAL)”,由于物理基础较好,近十多年来在许多国家有成功的业务化的应用,已被“GEF海河流域水资源和水环境综合管理”项目采用,以计算从日到季和年,从每个县每种土地利用到全流域的ET分布。以2002年的预分析为例介绍了有关结果。讨论了SEBAL的不足之处及改善意见。     

Spatio-temporal patterns of energy exchange and evapotranspiration during an intense drought for drylands in Brazil

The Caatinga biome, located in the northeastern region of Brazil, is the most populated dryland region on the planet and extremely vulnerable to land degradation due to climatological and anthropogenic factors. Energy partitioning substantially influences the local climate and affects the water cycle, which is of utmost importance for the economy and livelihood of the region. Recently, eddy covariance (EC) towers were installed in the area; thus, the scientific community can thoroughly assess the water and energy fluxes over this unique biome. While EC towers have a high degree of accuracy, they only measure energy fluxes over a small land footprint. Given the biome spatial heterogeneity, the use of EC-based techniques has the limitation of not comprehensively representing water and energy fluxes profiles over the entire region. Incorporating remote sensing (RS) data into the landscape analysis is a feasible solution to overcome this issue, given that satellite data can capture the phenomena represented by the EC measurements across large spatial scales. Our research studied the capability of the Surface Energy Balance Algorithm for Land (SEBAL) and MOD16-ET products to represent the EC measurements regarding energy and mass exchange, with an ultimate objective of applying the best approach to assess these fluxes regionally. We applied the SEBAL model using only remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The MOD16-ET model uses a different approach but is also based on MODIS data. Our analysis was based on three years (2014–2016) of data, which was limited to the availability of the EC tower data. We found that for the EC-based measurements, energy balance closure (EBC) achieved an average of 0.84, which is considerably high for the region. This is possibly due to the EC tower being installed on a preserved Caatinga plot, with reduced heterogeneity and higher plant density. When analyzing RS-based products to represent ET profiles in the region, we found that the SEBAL model accurately represented water fluxes during the wet season but not the dry season, whereas the MOD16-ET showed a better agreement with EC-based water fluxes throughout all the seasons. SEBAL inaccuracy in drylands is partially due to the narrow range between the cold and hot pixels in an image, as the algorithm relies on this range for input parameters, especially in the dry season. Therefore, we concluded that MOD16-ET is capable of better-representing water fluxes in the Caatinga region. We analyzed the fluxes regionally and quantified annual ET for the three years. These results are especially relevant for local policymakers on dealing with water and landscape issues in a region where the livelihood and well-being of the population is inextricably bound to water availability.     

黄土高原典型流域土地利用变化对蒸散发影响研究

选取黄土高原典型流域罗玉沟流域为研究区,以2006年和1986年两个时段的TM影像为原始数据,通过SEBAL模型估算蒸散量,采用地面实测资料对估算结果进行验证,表明SEBAL模型在该流域较为适用.同时,得到该流域蒸散发的空间变化规律,并结合该流域相应时段的土地利用变化进行对比分析.结果表明:(1)遥感反演日蒸散量平均为...     

风机对科尔沁草原地表温度和蒸散发的影响

利用Landsat5和Landsat8数据,通过对开鲁风电场建设前、后地表温度(LST)和蒸散发(ET)的模拟,结合根据30m×30m像元统计的风机与周围8个方向的LST、ET均值以及变异系数,研究了风力发电机对科尔沁草原的LST和ET的影响。结果表明:①风力发电机建设不会对科尔沁草原的LST产生明显影响;②风力发电机建设不会改变周围8个方向上ET的原有大小次序;③风力发电机建设将增大原有大小次序的数值差异,使得各方向上的ET变化更加不均匀。     

Assessment and validation of evapotranspiration using SEBAL algorithm and Lysimeter data of IARI agricultural farm,India

Evapotranspiration (ET) is a vital process in land surface atmosphere research. In this study, Surface Energy Balance Algorithm for Land (SEBAL) for the assessment of ET (for 23 December 2010, 8 January 2011, 24 January 2011, 9 February 2011, 25 February 2011, 29 March 2011 and 14 April 2011) from LANDSAT7-ETM+ and validation with Lysimeter data set is illustrated. It is based on the evaporative fraction concept, and it has been applied to LANDSAT7-ETM + (30 m resolution) data acquired over the Indian Agricultural Research Institute’s agricultural farm land. The ET from SEBAL was compared with Lysimeter ET using four statistical tests (root-mean-square error (RMSE), relative root-mean-square error (R-RMSE), mean absolute error (MAE), and normalized root-mean square error (NRMSE)), and each test showed a good correlation between the predicted and observed ET values. Results from this study revealed that the RMSE of crop-growing period was 0.51 mm d^?1 for ET_SEBAL, i.e. ET_SEBAL having good accuracy with respect to observed ET_Lysimeter. Results were also validated using R-RMSE test, which also proved that ET_SEBAL data are having good accuracy with respect to observed ET_Lysimeter as R-RMSE of crop-growing period is 0.19 mm d^?1. MAE (0.19), NRMSE (0.21) and r² (0.91) tests indicated that model prediction is significant, and model can be effectively used for the estimation of ET from SEBAL as input of remote sensing data sets. Finally, the SEBAL has been useful for remote agricultural land where ground-based data (Lysimeter data) are not available for daily ET (ET_24 h) estimation. The temporal study of the ET_24 h values analysed has revealed that the highest ET_24 h values are owing to the higher development (high greenness) of crops, whereas the lower values are related to the lower development (low greenness) or null crop.