A review of downscaling methods for remote sensing-based irrigation management: part I

High-resolution daily evapotranspiration (ET) maps would greatly improve irrigation management. Numerous ET mapping algorithms have been developed to make use of thermal remote sensing data acquired by satellite sensors. However, adoption of remote sensing-based ET maps for irrigation management has not been feasible due to inadequate spatial and temporal resolution of ET maps. Data from a coarse spatial resolution image in agricultural fields often cause inaccurate ET estimation because of a high level of spatial heterogeneity in land use. Image downscaling methods have been utilized to overcome spatial and temporal scaling issues in numerous remote sensing applications. In the field of hydrology, the image downscaling method has been used to improve spatial resolution of remote sensing-based ET maps for irrigation scheduling purposes and thus improves estimation of crop water requirements. This paper (part I) reviews downscaling methods to improve spatial resolution of land surface characteristics such as land surface temperature or ET. Each downscaling method was assessed and compared with respect to their capability of downscaling spatial resolutions of images. The companion paper (part II) presents review of image fusion methods that are also designed to increase spatial resolutions of images by integrating multi-spectral and panchromatic images.     

ET mapping for agricultural water management: present status and challenges

Evapotranspiration (ET) is an essential component of the water balance. Remote sensing based agrometeorological models are presently most suited for estimating crop water use at both field and regional scales. Numerous ET algorithms have been developed to make use of remote sensing data acquired by sensors on airborne and satellite platforms. In this paper, a literature review was done to evaluate numerous commonly used remote sensing based algorithms for their ability to estimate regional ET accurately. The reported estimation accuracy varied from 67 to 97% for daily ET and above 94% for seasonal ET indicating that they have the potential to estimate regional ET accurately. However, there are opportunities to further improving these models for accurately estimating all energy balance components. The spatial and temporal remote sensing data from the existing set of earth observing satellite platforms are not sufficient enough to be used in the estimation of spatially distributed ET for on-farm irrigation management purposes, especially at a field scale level (∼10 to 200 ha). This will be constrained further if the thermal sensors on future Landsat satellites are abandoned. However, research opportunities exist to improve the spatial and temporal resolution of ET by developing algorithms to increase the spatial resolution of reflectance and surface temperature data derived from Landsat/ASTER/MODIS images using same/other-sensor high resolution multi-spectral images.     

A Landsat-based energy balance and evapotranspiration model in Western US water rights regulation and planning

The quantification of evapotranspiration (ET) from irrigated projects is important for water rights management, water resources planning and water regulation. Traditionally, ET has been estimated by multiplying a weather-based reference ET by crop coefficients (K_c) determined according to the crop type and the crop growth stage. However, there is typically question regarding whether crops grown compare with the conditions represented by the K_c values, especially in water short areas. In addition, it is difficult to estimate the correct crop growth stage dates for large populations of crops and fields. METRIC (Mapping Evapotranspiration at high Resolution and with Internalized Calibration) is an image-processing model for calculating ET as a residual of the surface energy balance. METRIC is a variant of SEBAL, an energy balance process developed in the Netherlands by Bastiaanssen and was extended to provide tighter integration with ground-based reference ET. METRIC was applied with Landsat images in southern Idaho to predict monthly and seasonal ET for water rights accounting and for operation of ground water models. ET “maps” (i.e., images) provide the means to quantify, in terms of both the amount and spatial distribution, the ET on a field by field basis. The ET maps have been used in Idaho to quantify net ground-water pumpage in areas where water extraction from underground is not measured and to estimate recharge from surface-irrigated lands. Application and testing of METRIC indicates substantial promise as an efficient, accurate, and relatively inexpensive procedure to predict the actual evaporation fluxes from irrigated lands throughout a growing season.     

Estimation of water consumption and crop water productivity of winter wheat in North China Plain using remote sensing technology

The North China Plain (NCP) is one of the most water stressed areas in the world. The water consumption of winter wheat accounts for more than 50% of the total water consumption in this region. An accurate estimate of the evapotranspiration (ET) and crop water productivity (CWP) at regional scale is therefore key to the practice of water-saving agriculture in NCP. In this research, the ET and CWP of winter wheat in 83 counties during October 2003 to June 2004 in NCP were estimated using the remote sensing data. The daily ET was calculated using SEBAL model with NOAA remote sensing data in 17 non-cloud days whereas the reference daily crop ET was estimated using meteorological data based on Hargreaves approach. The daily ET and the total ET over the entire growing season of winter wheat were obtained using crop coefficient interpolation approach. The calculated average and maximum water consumption of winter wheat in these 83 counties were 424 and 475 mm, respectively. The calculated daily ET from SEBAL model showed good match with the observed data collected in a Lysimeter. The error of ET estimation over the entire growing stage of winter wheat was approximately 4.3%. The highest CWP across this region was 1.67 kg m⁻³, and the lowest was less than 0.5 kg m⁻³. We observed a close linear relationship between CWP and yield. We also observed that the continuing increase of ET leads to a peaking and subsequent decline of CWP, which suggests that the higher water consumption does not necessarily lead to a higher yield.     

Remote sensing for irrigation water management in the semi-arid Northeast of Brazil

Northeast of Brazil is a semi-arid region, where water is a key strategic resource in the development of all sectors of the economy. Irrigation agriculture is the main water consumer in this region. Therefore, policy directives are calling for tools to aid operational monitoring in planning, control and charging of irrigation water. Using Landsat imagery, this study evaluates the utility of a process that measures the level of water use in an irrigated area of the state of Ceará. The experiment, which models evapotranspiration (ET), was carried out within the Jaguaribe-Apodi irrigation scheme (DIJA) during two months of the agricultural season. The ET was estimated with the model Mapping Evapotranspiration at High Resolution and with Internalized Calibration (METRIC). The model uses the residual of the energy balance equation to estimate ET for each pixel in the image. The results of the estimates were validated using measurements of ET from a micrometeorological tower installed within a banana plantation located near the irrigation scheme. After evaluating the ET estimates, the average fraction of depleted water for a set of agricultural parcels combined with the monthly ET mapping estimates by METRIC provided a method for predicting the total water use in DIJA for the study period. The results were then compared against the monthly accumulated flow rates for all the pumping stations provided by the district manager. Finally, this work discusses the potential use of the model as an alternative method to calculate water consumption in irrigated agriculture and the implications for water resource management in irrigated perimeters.     

Evapotranspiration data assimilation with genetic algorithms and SWAP model for on-demand irrigation

Evapotranspiration (ET) is one of the indicators of water use efficiency. Periodic information of ET based on remote sensing is useful for an on-demand irrigation (ODI) management. The main objective of this paper was to develop an ET data assimilation scheme to optimize the parameters of an agro-hydrology model for ODI scheduling. The soil, water, atmosphere, and plant (SWAP) simulation model has been utilized for this purpose. We computed remote sensing-based ET for a wheat field in the Sirsa Irrigation Circle, Haryana, in India using 18 cloud-free moderate resolution imaging spectroradiometer images taken between December 2001 and April 2002. The surface energy balance algorithm for land (SEBAL) was used for this purpose. Because ET estimates from SEBAL provide information on the surface soil moisture state, they were treated as observations to estimate unknown parameters of the SWAP model via a stochastic data assimilation (genetic algorithm) approach. The SWAP parameters were optimized by minimizing the residuals between SEBAL and SWAP model-based ET values. The optimized parameters were used as input to SWAP to estimate soil water balance for ODI scheduling. The results showed that the selected parameters (i.e. sowing, harvesting, and irrigation scheduling dates) were successfully estimated with the data assimilation methodology. The SWAP model produced reasonable states of water balance by assimilating ET observations. The root mean square of error was 0.755 and 2.132 cm³/cm³ for 0–15 and 15–30 cm soil depths the same layers, respectively. With optimized parameters for ODI, SWAP predicted higher yield and water use efficiency than traditional farmer’s irrigation criteria. The data assimilation methodology produced can be considered as an operational tool at the field scale to schedule irrigation or predict irrigation requirements from remote sensing-based ET.     

美国农业航空技术现状和发展趋势分析

美国是目前农业航空装备技术最先进、应用最广泛的国家,农业航空服务组织体系完善,航空施药作业规范,施药部件系列齐全.一些精准农业技术手段如GPS自动导航、施药自动控制系统、各种作业模型已步入实用阶段,作业精准、高效,对环境的污染低.随着精准农业的发展,航空遥感技术、空间统计学、变量施药控制等技术也用于美国农田产量监测,植物的水分、营养状况、病虫害监测.提出了为改善目前技术存在的不足,提高数据准确性和生产效率,需解决的主要技术问题和研究热点,包括:图像实时处理技术、多传感器数据融合技术、航空变量喷洒技术.     

Evaluation of satellite evapotranspiration estimates using ground-meteorological data available for the Flumen District into the Ebro Valley of N.E. Spain

Accurate estimation of actual evapotranspiration (ET_a) is essential for effective local or regional water management. At a local scale, ET estimates can be made accurately considering a soil-plant-atmospheric system, if adequate meteorological-ground data or ET measurements are available. However, at a regional scale, ET_a values cannot be measured directly and the estimates are frequently subject to errors. Although it is possible to extrapolate to the regional scale from local (point) data of meteorological stations, the relative sparse coverage of ground estimate can make this problematic without some spatial analysis to demonstrate the similarity of the climate in the area. The introduction of remote sensing data and techniques offers alternatives both to estimate variables (i.e. radiation) and parameters (i.e. ET) with few spatial restrictions, thus, it provides potential advantages to the regional ET_a computation. In particular, the use of remote sensing procedures like the surface energy balance-based algorithms (SEB) have been successfully applied in different climates, enabling the estimation of ET_a at local and regional scales. A proper variation of the Surface Energy Balance Algorithm for Land (SEBAL) was applied to 4 years of data for the Flumen District in the Ebro Basin at the N.E. of Spain. Results obtained show that the remote sensing algorithm can provide accurate daily ET_a estimations as compared with lysimeter measurements of daily ET values for two crop plots: one with a reference grass and other with maize or wheat as function of the season. Also a comparison between ET_a and the reference and crop ET values applying the Penman-Monteith method was carried out. The comparison analysis consider an accepted error difference of 1.0 mm d⁻¹ (20% of error) for local scale, the ET_a values for the grass show a bias of 0.30 mm d⁻¹ against the ET_grass and a bias of 0.36 mm d⁻¹ against ET_o. Differences between ET_maize or ET_wheat and ET_a against their average showed an acceptable agreement for the field with s_diff ± 0.6 mm d⁻¹. For the crop fields at regional scale external causes associated to atmospheric and surface variations (i.e. land preparation) rather to the remote sensing algorithm made difficult to determine a percentage of error. Finally, ET_a values were obtained at regional scale and it was demonstrated that using the remote sensing improve significantly the crop ET estimations computed in the area using traditional methods.     

Estimating seasonal evapotranspiration from temporal satellite images

Estimating seasonal evapotranspiration (ET) has many applications in water resources planning and management, including hydrological and ecological modeling. Availability of satellite remote sensing images is limited due to repeat cycle of satellite or cloud cover. This study was conducted to determine the suitability of different methods namely cubic spline, fixed, and linear for estimating seasonal ET from temporal remotely sensed images. Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC) model in conjunction with the wet METRIC (wMETRIC), a modified version of the METRIC model, was used to estimate ET on the days of satellite overpass using eight Landsat images during the 2001 crop growing season in Midwest USA. The model-estimated daily ET was in good agreement (R ²?=?0.91) with the eddy covariance tower-measured daily ET. The standard error of daily ET was 0.6?mm (20%) at three validation sites in Nebraska, USA. There was no statistically significant difference (P?>?0.05) among the cubic spline, fixed, and linear methods for computing seasonal (July–December) ET from temporal ET estimates. Overall, the cubic spline resulted in the lowest standard error of 6?mm (1.67%) for seasonal ET. However, further testing of this method for multiple years is necessary to determine its suitability.     

基于CiteSpace的农作物遥感估产知识图谱分析

为了更直观地了解国内外农作物遥感估产研究现状,本文运用CiteSpace软件对CNKI中文数据库与WOS英文数据库中关于农作物估产研究的825篇文献进行可视化分析得到知识图谱。结果表明,作物遥感估产领域的核心关键词主要是(冬)小麦、植被指数和模型,以冬小麦为估产对象研究讨论的文章居多;国内在作物遥感估产领域的作者发文量差异较大,合作呈现“大部分集中,小部分分散”的分布方式;发文机构主要集中在北京、浙江和江苏等地的中科院二级机构、地方研究所和大学;WOS数据库中,美国、中国和印度在该领域刊文数量排名前3;WOS数据库文献的共引分析中,共引次数排名前10的文章,共引次数均达到15以上,这些文章代表了作物遥感估产领域的基础知识和研究前沿的演变。     

青海省农业生态区参考作物蒸散量的遥感反演

根据青海省农业生态区15个气象站2003年气象资料,应用FAO推荐的Penman-Monteith方程计算参考作物蒸散量(ET0),利用MODIS高程(DEM)、地表温度(LST)及法国SPOT卫星的归一化植被指数(NDVI)遥感影像资料,提取遥感数据并耦合到时间分辨率为旬,空间分辨率为1km,将其与计算所得ET0进行相关分析,运用MATLAB软件进行模型拟合,获得该地区的ET0遥感反演模型及其适用条件,使用Arcgis9.3对利用该模型反演的结果进行了空间分布规律分析。研究结果表明,2003年7—11月各旬遥感因子DEM、NDVI和LST与ET0的线性关系显著,其中DEM与ET0呈显著负相关关系,NDVI和LST与ET0呈极显著正相关关系;获得了该地区基于遥感数据的旬ET0三元线性遥感反演模型,该模型通过α=0.01的F和t显著性检验,模型效果极显著,适用期为7—10月;对2004年计算及反演结果表明,ET0空间分布从西北往东南方向递增,低海拔处往高海拔处递减;模型反演平均相对误差为-1.9%,有较好的反演结果。     

Performance assessment of an irrigation scheme using indicators determined with remote sensing techniques

In this work, remote sensing-based assessments of actual evapotranspiration using METRIC integrated with a water balance model provided accurate estimates of irrigation performance. This new methodology was applied and tested in the Genil–Cabra Irrigation Scheme located in southern Spain during the 2004–2005 irrigation season. The performance indicators used, the annual relative irrigation supply (ARIS) and the irrigation water productivity (IWP), required ET input data which were calculated using either METRIC or standard FAO methodology. The new procedure that used METRIC detected overirrigation (ARIS of 1.27) in situations where the ARIS calculated with the standard FAO methodology indicated near-optimal irrigation (ARIS of 0.98). Additionally, the proposed methodology allows the estimation of the volume of applied water at the field scale. Comparisons between the ARIS and IWP values obtained from actual applied water records against those calculated with the new methodology resulted in good agreement. It is concluded that the integration of the METRIC method to calculate actual ET with a water balance model allowed the determination of performance indicators in an irrigation scheme in a reliable and accurate fashion, requiring only very limited information at the field level.     

基于地面观测和Sentinel-2数据的玉米实际蒸散发估算

基于遥感技术估算作物蒸散发(Evapotranspiration,ET)对农业用水效率评价和精量灌溉决策具有重要意义。结合Sentinel-2数据和农田连续地面观测资料,利用混合双源蒸散发模型(Hybrid dual-source scheme and trapezoid framework-based evapotranspiration model,HTEM)对宁夏回族自治区中卫市2019年两个试验田玉米主要生育期(5—8月)的蒸散发量进行估算,并用水量平衡法对遥感估算结果进行验证和评价。结果表明:Sentinel-2数据具有高时空分辨率,能够与研究区复杂的种植地块相匹配,减少了混合像元的数量;遥感反演参数与地面观测数据拟合度较高,研究区2019年遥感反演的玉米田净辐射量均方根误差为36.256 W/m²。利用HTEM模型估算可得,主要生育期内研究区两个玉米试验田的日均实际蒸散发量分别为4.269 mm/d和4.339 mm/d,实际蒸散发总量分别为525.114 mm和533.690 mm,其中植被蒸腾量分别为363.483 mm和358.196 mm,生育初期主要以土壤蒸发形式消耗水分,随着作物的生长,在生育中后期主要以植被蒸腾的形式消耗水分。ET遥感反演结果与水量平衡结果之间差别不显著,两个观测点绝对误差分别为13.533 mm和7.774 mm。因此,结合地面连续观测系统和Sentinel-2数据估算研究区玉米生育阶段蒸散发量具有较高的精度,可为作物耗水规律研究及区域农业水管理提供技术支撑。     

Enhancing the Simplified Surface Energy Balance (SSEB) approach for estimating landscape ET: Validation with the METRIC model

Evapotranspiration (ET) can be derived from satellite data using surface energy balance principles. METRIC (Mapping EvapoTranspiration at high Resolution with Internalized Calibration) is one of the most widely used models available in the literature to estimate ET from satellite imagery. The Simplified Surface Energy Balance (SSEB) model is much easier and less expensive to implement. The main purpose of this research was to present an enhanced version of the Simplified Surface Energy Balance (SSEB) model and to evaluate its performance using the established METRIC model. In this study, SSEB and METRIC ET fractions were compared using 7 Landsat images acquired for south central Idaho during the 2003 growing season. The enhanced SSEB model compared well with the METRIC model output exhibiting an r² improvement from 0.83 to 0.90 in less complex topography (elevation less than 2000 m) and with an improvement of r² from 0.27 to 0.38 in more complex (mountain) areas with elevation greater than 2000 m. Independent evaluation showed that both models exhibited higher variation in complex topographic regions, although more with SSEB than with METRIC. The higher ET fraction variation in the complex mountainous regions highlighted the difficulty of capturing the radiation and heat transfer physics on steep slopes having variable aspect with the simple index model, and the need to conduct more research. However, the temporal consistency of the results suggests that the SSEB model can be used on a wide range of elevation (more successfully up 2000 m) to detect anomalies in space and time for water resources management and monitoring such as for drought early warning systems in data scarce regions. SSEB has a potential for operational agro-hydrologic applications to estimate ET with inputs of surface temperature, NDVI, DEM and reference ET.     

应用RS技术建立估算蒸散发模型的研究

蒸散发是水资源分配利用的依据,能够估算出不同气候环境、不同区域的蒸散发是目前研究的重点。这些年运用遥感手段建立模型,结合地面气象站的气象数据、卫星影像等信息,提取参数建立模型来估算蒸散发的方法已经有了较为广泛的运用。其中,利用能量平衡来建立模型尤为广泛,不同的模型,选取参数、处理方法上有所不同,着重针对SEBAL、SEBS估算模型进行详细的对比介绍,SEBAL模型是具有较为坚实的物理基础,已经在平原、盆地、流域等区域进行了成功的应用,SEBS模型与SEBAL模型在模型建立原理上一致,但在计算感热通量H时,采用总体大气的相似理论,引入了热传输粗糙度长度。对这两模型进行了系统学习分析,并针对在新疆未来可开展的工作进行展望。     

基于SEBAL模型的土默特右旗腾发量研究

【目的】评价SEBAL模型在估算内蒙古包头市土默特右旗腾发量时的适用性。【方法】基于2015年作物生育期内的Landsat8遥感影像,建立SEBAL模型,估算腾发量,利用FAO Penman-Monteith公式与水量平衡法估算得到腾发量进行了验证及评价,并采用多元逐步回归方法对其影响因素进行了分析。【结果】日腾发量的分布具有明显的空间差异性,呈现出"山峰型"变化趋势。SEBAL模型腾发量估算值与水量平衡法估算值相比,相对误差的平均值为6.053%;Penman-Monteith公式计算得到的日腾发量与水量平衡法估算值相差7.682%,都在10%以内,达到了精度要求,且SEBAL模型估算腾发量的精度高于Penman-Monteith公式。日腾发量与NDVI和地表温度相关性显著,由二者建立了最优的多元逐步回归方程。【结论】在缺乏数据的情况下,利用SEBAL模型可以较为准确地估算出土右旗的腾发量,且NDVI和地表温度对其的影响较大。     

Mapping and assessing water use in a Central Asian irrigation system by utilizing MODIS remote sensing products

Spatial and temporal patterns of water depletion in the irrigated land of Khorezm, a region located in Central Asia in the lower floodplains of the Amu Darya River, were mapped and monitored by means of MODIS land products. Land cover and land use were classified by using a recursive partitioning and regression tree with 250 m MODIS Normalized Difference Vegetation Index (NDVI) time series. Seasonal actual evapotranspiration (ET_act) was obtained by applying the Surface Energy Balance Algorithm for Land (SEBAL) to 1 km daily MODIS data. Elements of the SEBAL based METRIC model (Mapping Evapotranspiration at high Resolution and with Internalized Calibration) were adopted and modified. The upstream–downstream difference in irrigation was reflected by analyzing agricultural land use and amounts of depleted water (ET_act) using Geographical Information Systems (GIS). The validity of the MODIS albedo and emissivity used for modeling ET_act was assessed with data extracted from literature. The r ² value of 0.6 indicated a moderate but significant association between ET_act and class-A-pan evaporation. Deviations of ET_act from the 10-day reference evapotranspiration of wheat and cotton were found to be explainable. In Khorezm, seasonal maximum values superior to 1,200 and 1,000 mm ET_act were estimated for rice and cotton fields, respectively. Spatio-temporal comparisons of agricultural land use with seasonal ET_act disclosed unequal water consumption in Khorezm. Seasonal ET_act on agricultural land decreased with increasing distance to the water intake points of the irrigation system (972–712 mm). Free MODIS data provided reliable, exhaustive, and consistent information on water use relevant for decision support in Central Asian water management.     

基于无人机遥感植被指数优选的覆膜冬小麦估产研究

为进一步提高无人机遥感估产的精度,本研究以2021—2022年的覆膜冬小麦为研究对象,对返青期、拔节期、抽穗期和灌浆期的多光谱影像进行覆膜背景剔除,并优选最佳遥感窗口期,基于最优植被指数构建覆膜冬小麦估产模型。结果表明,利用支持向量机监督分类法剔除覆膜背景后冠层反射率更接近真实值,抽穗期和灌浆期的估产精度更高。将不同生育期的植被指数与产量进行相关性分析发现,最佳遥感窗口期为抽穗期。基于逐步回归和全子集回归法优选最优植被指数时发现,基于逐步回归法筛选变量为MCARI、MSR、EVI2、NDRE、VARI、NDGI、NGBDI、ExG时产量反演模型精度最高。此外,利用偏最小二乘法、人工神经网络和随机森林3种机器学习法构建的产量反演模型中,基于逐步回归法的随机森林模型的反演精度最高,R2为0.82,RMSE为0.84t/hm2。该研究可为提高遥感估产精度、实现农业生产精细化管理提供技术支持。     

Review on estimation of evapotranspiration from remote sensing data: From empirical to numerical modeling approaches

Different methods have been developed to estimate evapotranspiration from remote sensing data, from empirical approaches such as the simplified relationship to complex methods based on remote sensing data assimilation along with SVAT models. The simplified relationship has been applied from small spatial scale using airborne TIR images to continental scale with NOAA data. Assimilation procedures often require remote sensing data over different spectral domains to retrieve input parameters which characterize surface properties such as albedo, emissivity or Leaf Area Index. A brief review of these different approaches is presented, with a discussion about the main physical bases and assumptions of various models. The paper reports also some examples and results obtained over the experimental area of the Alpilles Reseda project, where various types of models have been applied to estimate surface fluxes from remote sensing data.     

遥感技术在农业信息化中的应用

在介绍遥感技术相关概念的基础上,从农业资源调查及动态监测、农作物估产和农业灾害预报监测3个方面,阐述了遥感技术在农业信息化中的应用。遥感技术的发展必将促进我国农业信息化的快速发展。