Evapotranspiration Estimation Based on MODIS Products and Surface Energy Balance Algorithms for Land(SEBAL) Model in Sanjiang Plain,Northeast China

In this study,the Surface Energy Balance Algorithms for Land(SEBAL) model and Moderate Resolution Imaging Spectroradiometer(MODIS) products from Terra satellite were combined with meteorological data to estimate evapotranspiration(ET) over the Sanjiang Plain,Northeast China.Land cover/land use was classified by using a recursive partitioning and regression tree with MODIS Normalized Difference Vegetation Index(NDVI) time series data,which were reconstructed based on the Savitzky-Golay filtering approach.The MODIS product Quality Assessment Science Data Sets(QA-SDS) was analyzed and all scenes with valid data covering more than 75% of the Sanjiang Plain were selected for the SEBAL modeling.This provided 12 overpasses during 184-day growing season from May 1st to October 31st,2006.Daily ET estimated by the SEBAL model was misestimaed at the range of-11.29% to 27.57% compared with that measured by Eddy Covariance system(10.52% on average).The validation results show that seasonal ET from the SEBAL model is comparable to that from ground observation within 8.86% of deviation.Our results reveal that the time series daily ET of different land cover/use increases from vegetation on-going until June or July and then decreases as vegetation senesced.Seasonal ET is lower in dry farmland(average(Ave):491 mm) and paddy field(Ave:522 mm) and increases in wetlands to more than 586 mm.As expected,higher seasonal ET values are observed for the Xingkai Lake in the southeastern part of the Sanjiang Plain(Ave:823 mm),broadleaf forest(Ave:666 mm) and mixed wood(Ave:622 mm) in the southern/western Sanjiang Plain.The ET estimation with SEBAL using MODIS products can provide decision support for operational water management issues.

基于SEBAL模型的区域蒸发蒸腾遥感估算

蒸发蒸腾是区域水资源循环中重要组成部分,对于区域水文学研究具有重要的意义。传统的蒸发蒸腾计算模型多以"点"上的观测为基础,对潜在蒸发量进行评价,难以在大面积区域上推广应用,在一定程度上限制了其在实际中的应用。遥感技术的发展,以及其能够方便快捷地获取地面信息的特性,为区域蒸发蒸腾量的遥感反演提供了可能。陆面能量平衡方法(SEBAL)模型是一种基于遥感影像的区域地表通量的估算模型,能够对区域蒸发蒸腾进行精确估算。本文在SEBAL模型的基础上,以河北平原为例,采取中分辨率成像光谱辐射仪(MO-DIS)影像产品,根据研究区下垫面的实际情况进行了参数估算,进行了区域实际蒸发蒸腾量计算及模型精确度评价,在此基础上,结合研究区的地面覆盖,对河北平原区域蒸发蒸腾分布进行了分析。

基于SEBAL模型的扎龙湿地蒸散量反演

以扎龙湿地为研究对象,利用SEBAL模型结合ETM+遥感影像、DEM及研究区附近12个站点的气象数据,反演得到净辐射量、土壤热通量、感热通量等地表能量通量;然后通过能量平衡方程得到潜热通量,并推算得到日蒸散量值。结合2001年的土地利用图分析了不同土地利用类型的日蒸散量特征,发现水体、沼泽地具有较高的日蒸散量,水田、草地、林地次之,旱地、未利用地、居民点较低,符合蒸散规律,说明SEBAL模型在区域蒸散量估算方面具有较大的实用性。

基于遥感数据与SEBAL模型的江浙沪地区地表蒸散反演

地表蒸散的准确估算对于研究区域尺度上因气候或土地利用变化引起的水量-能量变化有着重要的意义。本研究集成MODIS数据,利用陆面能量平衡模型(surface energy balance algorithm for land,SEBAL),对江苏、浙江和上海市地区(以下简称江浙沪)地区2002—2015年间生长季的蒸散进行了估算,并使用蒸渗仪地表观测数据对模型进行了验证。在总结蒸散时间扩展方法的基础上,对区域蒸散进行了月、季节尺度的扩展,计算得到区域月、季节尺度的蒸散量。并选取特征年2004年与2013年来分析日尺度与生长季尺度的空间蒸散特征。研究表明,SEBAL模型较适用于江浙沪地区的蒸散估算,该地区的年生长季蒸散范围跨度较大,在空间上呈现南高北低的特征,2004年区域生长季蒸散平均值为930 mm;2013年为758 mm,低于2004年的蒸散值。对特定地物提取的生长季日平均蒸散进行统计发现,江浙沪地区的自然地物蒸散要高于人造地物,而在自然地物中,水体>林地>滩涂滩地>草地>未利用地。而对于人造地物,城镇的蒸散值很低,而水田、旱地以及农村居民点的蒸散值较高。

Irrigation Scheduling Using Remote Sensing Data Assimilation Approach

Remote sensing and crop growth models have enhanced our ability to understand soil water balance in irrigated agriculture. However, limited efforts have been made to adopt data assimilation methodologies in these linked models that use stochastic parameter estimation with genetic algorithm (GA) to improve irrigation scheduling. In this study, an innovative irrigation scheduling technique, based on soil moisture and crop water productivity, was evaluated with data from Sirsa Irrigation Circle of Haryana State, India. This was done by integrating SEBAL (Surface Energy Balance Algorithm for Land)-based evapotranspiration (ET) rates with the SWAP (Soil-Water-Atmosphere-Plant), a process-based crop growth model, using a GA. Remotely sensed ET and ground measurements from an experiment field were combined to estimate SWAP model parameters such as sowing and harvesting dates, irrigation scheduling, and groundwater levels to estimate soil moisture. Modeling results showed that estimated sowing, harvesting, and irrigation application dates were within ±10 days of observations and produced good estimates of ET and soil moisture fluxes. The SWAP-GA model driven by the remotely sensed ET moderately improved surface soil moisture estimates suggesting that it has the potential to serve as an operational tool for irrigation scheduling purposes.

遥感技术和SEBAL模型在干旱区腾发量估算中的应用

陆地表面的腾发量分布具有很大的空间变异性,而传统方法只能进行点上的计算。为反映表面腾发量的空间特征,引入遥感技术和SEBAL(surface energy balance algorithm for land)模型。遥感技术能够方便、快捷地获取地面信息,而SEBAL模型则利用Landsat TM/ETM影像获取的遥感信息计算区域腾发量。该文在SEBAL模型的基础上,针对具体研究区域的客观情况进行参数改进,在ERDAS软件支持下,计算获得了研究区域地面反照率、地面温度以及各能量分量的空间分布。结果分析表明,参数改进后模型计算过程简捷,结果基本合理,具有广阔的应用前景,尤其对于缺乏观测资料的地区,更是一种行之有效的方法。

SEBAL模型参量敏感性再分析

利用SEBAL(surface energy balance algorithm for land)模型反演了黄河流域河龙区间蒸散发,研究了模型对输入参量的敏感性。结果表明:SEBAL模型显热通量反演值对地表温度、冷热点性质(热点地表温度、热点净辐射等)较为敏感,对地气温差、地表发射率、反照率有一定敏感性,而对气象数据(风速、气温、水汽压)、动量粗糙长度、归一化植被指数不敏感。结合参量误差来源及其变异性分析认为,影响SEBAL模型反演结果最大的是冷热点性质和地表温度。相对于热点温度偏高和冷点温度偏低,热点温度偏低和冷点温度偏高对SEBAL模型反演结果影响更大。因此,正确选取研究区内冷热点并采取合理的控制方法,是保证SEBAL模型反演蒸散发精度的重要前提。