Evapotranspiration Estimation Using Remote Sensing Technology Based on a SEBAL Model in the Upper Reaches of the Huaihe River Basin

Understanding the spatial and temporal variations of evapotranspiration (ET) is vital for water resources planning and management and drought monitoring. The development of a satellite remote sensing technique is described to provide insight into the estimation of ET at a regional scale. In this study, the Surface Energy Balance Algorithm for Land (SEBAL) was used to calculate the actual ET on a daily scale from Landsat-8 data and daily ground-based meteorological data in the upper reaches of Huaihe River on 20 November 2013, 16 April 2015 and 23 March 2018. In order to evaluate the performance of the SEBAL model, the daily SEBAL ET (ETSEBAL) was compared against the daily reference ET (ET0) from four theoretical methods: the Penman-Monteith (P-M), Irmak-Allen (I-A), the Turc, and Jensen-Haise (J-H) method, the ETMOD16 product from the MODerate Resolution Imaging Spectrometer (MOD16) and the ETVIC from Variable Infiltration Capacity Model (VIC). A linear regression equation and statistical indices were used to model performance evaluation. The results showed that the daily ETSEBAL correlated very well with the ET0, ETMOD16, and ETVIC, and bias between the ETSEBAL with them was less than 1.5%. In general, the SEBAL model could provide good estimations in daily ET over the study region. In addition, the spatial-temporal distribution of ETSEBAL was explored. The variation of ETSEBAL was significant in seasons with high values during the growth period of vegetation in March and April and low values in November. Spatially, the daily ETSEBAL values in the mountain area were much higher than those in the plain areas over the study region. The variability of ETSEBAL in this study area was positively correlated with elevation and negatively correlated with surface reflectance, which implies that elevation and surface reflectance are the important factors for predicting ET in this study area.

Download Full-text

REGIONAL GEOLOGICAL MAPPING IN THE GRAHAM LAND OF ANTARCTIC PENINSULA USING LANDSAT-8 REMOTE SENSING DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w5-235-2017 ◽

2017 ◽

Vol XLII-4/W5 ◽

pp. 235-238

Author(s):

A. B. Pour ◽

M. Hashim ◽

Y. Park

Keyword(s):

Remote Sensing ◽

Antarctic Peninsula ◽

Regional Scale ◽

Geological Mapping ◽

Mapping Technique ◽

Landsat 8 ◽

Study Region ◽

Mineral Mapping ◽

Mapping Tool ◽

North Eastern

Geological investigations in Antarctica confront many difficulties due to its remoteness and extreme environmental conditions. In this study, the applications of Landsat-8 data were investigated to extract geological information for lithological and alteration mineral mapping in poorly exposed lithologies in inaccessible domains such in Antarctica. The north-eastern Graham Land, Antarctic Peninsula (AP) was selected in this study to conduct a satellite-based remote sensing mapping technique. Continuum Removal (CR) spectral mapping tool and Independent Components Analysis (ICA) were applied to Landsat-8 spectral bands to map poorly exposed lithologies at regional scale. Pixels composed of distinctive absorption features of alteration mineral assemblages associated with poorly exposed lithological units were detected by applying CR mapping tool to VNIR and SWIR bands of Landsat-8.Pixels related to Si-O bond emission minima features were identified using CR mapping tool to TIR bands in poorly mapped andunmapped zones in north-eastern Graham Land at regional scale. Anomaly pixels in the ICA image maps related to spectral featuresof Al-O-H, Fe, Mg-O-H and CO3 groups and well-constrained lithological attributions from felsic to mafic rocks were detectedusing VNIR, SWIR and TIR datasets of Landsat-8. The approach used in this study performed very well for lithological andalteration mineral mapping with little available geological data or without prior information of the study region.

Download Full-text

Spatial variability of mean daily estimates of actual evaporation from remotely sensed imagery and surface reference data

Hydrology and Earth System Sciences ◽

10.5194/hess-23-4891-2019 ◽

2019 ◽

Vol 23 (12) ◽

pp. 4891-4907 ◽

Cited By ~ 1

Author(s):

Robert N. Armstrong ◽

John W. Pomeroy ◽

Lawrence W. Martz

Keyword(s):

Remote Sensing ◽

Spatial Variability ◽

Land Surface ◽

Management Practices ◽

Reference Data ◽

Meteorological Data ◽

Net Radiation ◽

Study Region ◽

Evaporation Model ◽

Actual Evaporation

Abstract. Land surface evaporation has considerable spatial variability that is not captured by point-scale estimates calculated from meteorological data alone. Knowing how evaporation varies spatially remains an important issue for improving parameterisations of land surface schemes and hydrological models and various land management practices. Satellite-based and aerial remote sensing has been crucial for capturing moderate- to larger-scale surface variables to indirectly estimate evaporative fluxes. However, more recent advances for field research via unmanned aerial vehicles (UAVs) now allow for the acquisition of more highly detailed surface data. Integrating models that can estimate “actual” evaporation from higher-resolution imagery and surface reference data would be valuable to better examine potential impacts of local variations in evaporation on upscaled estimates. This study introduces a novel approach for computing a normalised ratiometric index from surface variables that can be used to obtain more-realistic distributed estimates of actual evaporation. For demonstration purposes the Granger–Gray evaporation model (Granger and Gray, 1989) was applied at a rolling prairie agricultural site in central Saskatchewan, Canada. Visible and thermal images and meteorological reference data required to parameterise the model were obtained at midday. Ratiometric indexes were computed for the key surface variables albedo and net radiation at midday. This allowed point observations of albedo and mean daily net radiation to be scaled across high-resolution images over a large study region. Albedo and net radiation estimates were within 5 %–10 % of measured values. A daily evaporation estimate for a grassed surface was 0.5 mm (23 %) larger than eddy covariance measurements. Spatial variations in key factors driving evaporation and their impacts on upscaled evaporation estimates are also discussed. The methods applied have two key advantages for estimating evaporation over previous remote-sensing approaches: (1) detailed daily estimates of actual evaporation can be directly obtained using a physically based evaporation model, and (2) analysis of more-detailed and more-reliable evaporation estimates may lead to improved methods for upscaling evaporative fluxes to larger areas.

Download Full-text

Harmonizing Multi-Source Remote Sensing Images for Summer Corn Growth Monitoring

Remote Sensing ◽

10.3390/rs11111266 ◽

2019 ◽

Vol 11 (11) ◽

pp. 1266 ◽

Cited By ~ 3

Author(s):

Mingzheng Zhang ◽

Dehai Zhu ◽

Wei Su ◽

Jianxi Huang ◽

Xiaodong Zhang ◽

...

Keyword(s):

Remote Sensing ◽

Time Series ◽

Kalman Filter ◽

Spatial Resolution ◽

Temporal Resolution ◽

Meteorological Data ◽

High Temporal Resolution ◽

Growth Monitoring ◽

Landsat 8 ◽

Data Set

Continuous monitoring of crop growth status using time-series remote sensing image is essential for crop management and yield prediction. The growing season of summer corn in the North China Plain with the period of rain and hot, which makes the acquisition of cloud-free satellite imagery very difficult. Therefore, we focused on developing image datasets with both a high temporal resolution and medium spatial resolution by harmonizing the time-series of MOD09GA Normalized Difference Vegetation Index (NDVI) images and 30-m-resolution GF-1 WFV images using the improved Kalman filter model. The harmonized images, GF-1 images, and Landsat 8 images were then combined and used to monitor the summer corn growth from 5th June to 6th October, 2014, in three counties of Hebei Province, China, in conjunction with meteorological data and MODIS Evapotranspiration Data Set. The prediction residuals ( Δ P R K ) in NDVI between the GF-1 observations and the harmonized images was in the range of −0.2 to 0.2 with Gauss distribution. Moreover, the obtained phenological curves manifested distinctive growth features for summer corn at field scales. Changes in NDVI over time were more effectively evaluated and represented corn growth trends, when considered in conjunction with meteorological data and MODIS Evapotranspiration Data Set. We observed that the NDVI of summer corn showed a process of first decreasing and then rising in the early growing stage and discuss how the temperature and moisture of the environment changed with the growth stage. The study demonstrated that the synthesized dataset constructed using this methodology was highly accurate, with high temporal resolution and medium spatial resolution and it was possible to harmonize multi-source remote sensing imagery by the improved Kalman filter for long-term field monitoring.

Download Full-text

Prediction of Wheat Grain Protein by Coupling Multisource Remote Sensing Imagery and ECMWF Data

Remote Sensing ◽

10.3390/rs12081349 ◽

2020 ◽

Vol 12 (8) ◽

pp. 1349 ◽

Cited By ~ 3

Author(s):

Xiaobin Xu ◽

Cong Teng ◽

Yu Zhao ◽

Ying Du ◽

Chunqi Zhao ◽

...

Keyword(s):

Remote Sensing ◽

Linear Model ◽

Root Mean Square ◽

Vegetation Index ◽

Meteorological Data ◽

Hierarchical Linear Model ◽

Grain Protein ◽

Landsat 8 ◽

Mean Square ◽

Mean Square Errors

Industrialization production with high quality and effect on winter is an important measure for accelerating the shift from increasing agricultural production to improving quality in terms of grain protein content (GPC). Remote sensing technology achieved the GPC prediction. However, large deviations in interannual expansion and regional transfer still exist. The present experiment was carried out in wheat producing areas of Beijing (BJ), Renqiu (RQ), Quzhou, and Jinzhou in Hebei Province. First, the spectral consistency of Landsat 8 Operational Land Imager (LS8) and RapidEye (RE) was compared with Sentinel-2 (S2) satellites at the same ground point in the same period. The GPC prediction model was constructed by coupling the vegetation index with the meteorological data obtained by the European Center for Medium-range Weather Forecasts using hierarchical linear model (HLM) method. The prediction and spatial expansion of regional GPC were validated. Results were as follows: (1) Spectral information calculated from S2 imagery were highly consistent with LS8 (R2 = 1.00) and RE (R2 = 0.99) imagery, which could be jointly used for GPC modeling. (2) The predicted GPC by using the HLM method (R2 = 0.524) demonstrated higher accuracy than the empirical linear model (R2 = 0.286) and showed higher improvements across inter-annual and regional scales. (3) The GPC prediction results of the verification samples in RQ, BJ, Xiaotangshan (XTS) in 2018, and XTS in 2019 were ideal with root mean square errors of 0.61%, 1.13%, 0.91%, and 0.38%, and relative root mean square error of 4.11%, 6.83%, 6.41%, and 2.58%, respectively. This study has great application potential for regional and inter-annual quality prediction.

Download Full-text

Uma abordagem metodológica de Sensoriamento Remoto para o monitoramento da contaminação do rio Paraopeba pós-desastre de Brumadinho-MG

Ciência e Natura ◽

10.5902/2179460x43632 ◽

2021 ◽

Vol 43 ◽

pp. e36

Author(s):

Neison Cabral Ferreira Freire ◽

Admilson Da Penha Pacheco ◽

Vinícius D'Lucas Bezerra Queiroz

Keyword(s):

Remote Sensing ◽

Spectral Band ◽

Ratio Method ◽

Landsat 8 ◽

Surface Reflectance ◽

Band Ratio ◽

Hydrographic Basin ◽

Search Area ◽

Brazilian History ◽

Sentinel 2

The following article aims to present and discuss the monitoring, through Remote Sensing, of the dirt displacement caused by the collapse of the Córrego do Feijão’s dam I of mining waste, which occurred on January 25, 2019, in the rural area of Brumadinho, a city located in the state of Minas Gerais, Brazil. This event is considered one of the greatest technoindustrial disasters in Brazilian history, placing in danger one of the largest hydrographic basin in Brazil: the São Francisco river basin. The search area comprises from where the sludge mud got in contact with the Paraopeba’s right bank to its mouth into the Três Marias Dam, adding up to approximately 315 km. For this monitoring the spectral band ratio method was utilized, using images from the sensors MSI/Sentinel-2 and OLI/Landsat-8 captured at different dates, employing standardization of means and variances to harmonize the range of the surface reflectance values in each image.

Download Full-text

Mapping Forest Canopy Height in Mountainous Areas Using ZiYuan-3 Stereo Images and Landsat Data

Forests ◽

10.3390/f10020105 ◽

2019 ◽

Vol 10 (2) ◽

pp. 105 ◽

Cited By ~ 4

Author(s):

Mingbo Liu ◽

Chunxiang Cao ◽

Yongfeng Dang ◽

Xiliang Ni

Keyword(s):

Remote Sensing ◽

Forest Canopy ◽

Remote Sensing Data ◽

Canopy Height ◽

Landsat 8 ◽

Surface Reflectance ◽

Landsat Data ◽

Sensing Data ◽

Forest Height ◽

Reflectance Data

Forest canopy height is an important parameter for studying biodiversity and the carbon cycle. A variety of techniques for mapping forest height using remote sensing data have been successfully developed in recent years. However, the demands for forest height mapping in practical applications are often not met, due to the lack of corresponding remote sensing data. In such cases, it would be useful to exploit the latest, cheaper datasets and combine them with free datasets for the mapping of forest canopy height. In this study, we proposed a method that combined ZiYuan-3 (ZY-3) stereo images, Shuttle Radar Topography Mission global 1 arc second data (SRTMGL1), and Landsat 8 Operational Land Imager (OLI) surface reflectance data. The method consisted of three procedures: First, we extracted a digital surface model (DSM) from the ZY-3, using photogrammetry methods and subtracted the SRTMGL1 to obtain a crude canopy height model (CHM). Second, we refined the crude CHM and correlated it with the topographically corrected Landsat 8 surface reflectance data, the vegetation indices, and the forest types through a Random Forest model. Third, we extrapolated the model to the entire study area covered by the Landsat data, and obtained a wall-to-wall forest canopy height product with 30 m × 30 m spatial resolution. The performance of the model was evaluated by the Random Forest’s out-of-bag estimation, which yielded a coefficient of determination (R2) of 0.53 and a root mean square error (RMSE) of 3.28 m. We validated the predicted forest canopy height using the mean forest height measured in the field survey plots. The validation result showed an R2 of 0.62 and a RMSE of 2.64 m.

Download Full-text

Simulation of Evapotranspiration at a 3-Minute Time Interval Based on Remote Sensing Data and SEBAL Model

Applied Sciences ◽

10.3390/app10144919 ◽

2020 ◽

Vol 10 (14) ◽

pp. 4919

Author(s):

Guoqing Li ◽

Alona Armstrong ◽

Xueli Chang

Keyword(s):

Remote Sensing ◽

Spatial Distribution ◽

Land Surface ◽

Regional Scale ◽

Remote Sensing Data ◽

Time Interval ◽

Sensing Data ◽

Significant Linear Correlation ◽

Sebal Model ◽

Moderate Resolution Imaging Spectroradiometer

Using remote sensing to estimate evapotranspiration minute frequency is the basis for accurately calculating hourly and daily evapotranspiration from the regional scale. However, from the existing research, it is difficult to use remote sensing data to estimate evapotranspiration minute frequency. This paper uses GF-4 and moderate-resolution imaging spectroradiometer (MODIS) data in conjunction with the Surface Energy Balance Algorithm for Land (SEBAL) model to estimate ET at a 3-min time interval in part of China and South Korea, and compares those simulation results with that from field measured data. According to the spatial distribution of ET derived from GF-4 and MODIS, the texture of ET derived from GF-4 is more obvious than that of MODIS, and GF-4 is able to express the variability of the spatial distribution of ET. Meanwhile, according to the value of ET derived from both GF-4 and MODIS, results from these two satellites have significant linear correlation, and ET derived from GF-4 is higher than that from MODIS. Since the temporal resolution of GF-4 is 3 min, the land surface ET at a 3-min time interval could be obtained by utilizing all available meteorological and remote sensing data, which avoids error associated with extrapolating instantaneously from a single image.

Download Full-text

Comparison of In Situ and Remote-Sensing Methods to Determine Turbidity and Concentration of Suspended Matter in the Estuary Zone of the Mzymta River, Black Sea

Remote Sensing ◽

10.3390/rs13010143 ◽

2021 ◽

Vol 13 (1) ◽

pp. 143

Author(s):

Ksenia Nazirova ◽

Yana Alferyeva ◽

Olga Lavrova ◽

Yuri Shur ◽

Dmitry Soloviev ◽

...

Keyword(s):

Remote Sensing ◽

Black Sea ◽

Atmospheric Correction ◽

Landsat 8 ◽

Suspended Particulate Matter Concentration ◽

Water Sampling ◽

Study Region ◽

Sentinel 2A ◽

Remote Sensing Methods

The paper presents the results of a comparison of water turbidity and suspended particulate matter concentration (SPM) obtained from quasi-synchronous in situ and satellite remote-sensing data. Field measurements from a small boat were performed in April and May 2019, in the northeastern part of the Black Sea, in the mouth area of the Mzymta River. The measuring instruments and methods included a turbidity sensor mounted on a CTD (Conductivity, Temperature, Depth), probe, a portable turbidimeter, water sampling for further laboratory analysis and collecting meteorological information from boat and ground-based weather stations. Remote-sensing methods included turbidity and SPM estimation using the C2RCC (Case 2 Regional Coast Color) and Atmospheric correction for OLI ‘lite’ (ACOLITE) ACOLITE processors that were run on Landsat-8 Operational Land Imager (OLI) and Sentinel-2A/2B Multispectral Instrument (MSI) satellite data. The highest correlation between the satellite SPM and the water sampling SPM for the study area in conditions of spring flooding was achieved using C2RCC, but only for measurements undertaken almost synchronously with satellite imaging because of the high mobility of the Mzymta plume. Within the few hours when all the stations were completed, its boundary could shift considerably. The ACOLITE algorithms overestimated by 1.5 times the water sampling SPM in the low value range up to 15 g/m3. For SPM over 20–25 g/m3, a high correlation was observed both with the in situ measurements and the C2RCC results. It was demonstrated that quantitative turbidity and SPM values retrieved from Landsat-8 OLI and Sentinel-2A/2B MSI data can adequately reflect the real situation even using standard retrieval algorithms, not regional ones, provided the best suited algorithm is selected for the study region.

Download Full-text

Geostatistical and Remote Sensing Studies to Identify High Metallogenic Potential Regions in the Kivi Area of Iran

Minerals ◽

10.3390/min10100869 ◽

2020 ◽

Vol 10 (10) ◽

pp. 869

Author(s):

Adel Shirazy ◽

Mansour Ziaii ◽

Ardeshir Hezarkhani ◽

Timofey Timkin

Keyword(s):

Remote Sensing ◽

Statistical Processing ◽

Principal Component ◽

Stream Sediment ◽

Geochemical Data ◽

Landsat 8 ◽

Study Region ◽

Linear Discriminant ◽

Survey Results ◽

East Azerbaijan

The Kivi area in the East Azerbaijan Province of Iran is one of the country’s highest-potential regions for metal element exploration. The primary goal herein was to process the data obtained from geochemical, geostatistical, and remote sensing tools (in the form of stream sediment samples and satellite images) to identify metallic mineralization anomalies in the region. After correcting the raw stream sediment geochemical data, single-variable statistical processing was performed, and Ti and Zn were identified as the elements with the highest degree of contrast. The relationship among these elements was further investigated using correlation and hierarchical clustering analyses. Principal component analysis was then applied to determine the principal components related to these elements, which were subsequently plotted on a regional geological map. Elements related to Ti and Zn were identified using threshold limits of anomalous samples determined via linear discriminant analysis. Lithological units and alteration patterns were detected through remote sensing investigations on Landsat-8 images. Stream sediment geochemical and remote sensing survey results identified anomalous areas of Ti and Zn in the eastern part of the study region. Our results indicate that Ti and Zn are good pathfinder elements for further exploratory investigation in this area.

Download Full-text

Spatial variability of mean daily estimates of actual evaporation from remotely sensed imagery and surface reference data

10.5194/hess-2018-600 ◽

2018 ◽

Author(s):

Robert N. Armstrong ◽

John W. Pomeroy ◽

Lawrence W. Martz

Keyword(s):

Remote Sensing ◽

Spatial Variability ◽

Land Surface ◽

Management Practices ◽

Reference Data ◽

Meteorological Data ◽

Net Radiation ◽

Study Region ◽

Evaporation Model ◽

Actual Evaporation

Abstract. Land surface evaporation has considerable spatial variability that is not captured by point scale estimates calculated from meteorological data alone. Knowing how evaporation varies spatially remains an important issue for improving parameterisations of land surface schemes and hydrological models, and various land management practices. Satellite-based and aerial remote sensing has been crucial for capturing moderate to larger scale surface variables to indirectly estimate evaporative fluxes. However, more recent advances for field research via unmanned aerial vehicles (UAVs) now allows for the acquisition of more highly detailed surface data. Integrating models that can estimate actual evaporation from higher resolution imagery and surface reference data would be valuable to better examine potential impacts of local variations in evaporation on upscaled estimates. This study introduces a novel approach for computing a normalised index from surface variables that can be used to obtain more realistic distributed estimates of actual evaporation. For demonstration purposes the Granger and Gray evaporation model (G–D) was applied at a complex parkland site in central Saskatchewan, Canada. Visible and thermal images and meteorological reference data required to parameterise the model was obtained at midday. Normalised indexes (simple ratios) were computed at midday for albedo and net radiation. This allowed for single measured values albedo and mean daily net radiation to be scaled across high resolution images over a large study region. Albedo and net radiation estimates were within 5–10 % of measured values. An evaporation estimate for a grassed surface was 0.5 mm larger than eddy covariance measurements. The methods applied have two key advantages for estimating evaporation over previous remote sensing approaches, 1. Detailed daily estimates of actual evaporation were directly obtained using a physically-based evaporation model, and 2. Analysis of more detailed and reliable evaporation estimates may lead to improved methods for upscaling evaporative fluxes to larger scales.

Download Full-text