Assessment of Different Methods for Shadow Detection in High-Resolution Optical Imagery and Evaluation of Shadow Impact on Calculation of NDVI and Evapotranspiration

Significant efforts have been made recently in the application of high-resolution remote sensing imagery (i.e., sub-meter) captured by unmanned aerial vehicles (UAVs) for precision agricultural applications for high-value crops such as wine grapes. However, at such high resolution, shadows will appear in the optical imagery effectively reducing the reflectance and emission signal received by imaging sensors. To date, research that evaluates procedures to identify the occurrence of shadows in imagery produced by UAVs is limited. In this study, the performance of four different shadow detection methods used in satellite imagery was evaluated for high-resolution UAV imagery collected over a California vineyard during the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) field campaigns. The performance of the shadow detection methods was compared and impacts of shadowed areas on the normalized difference vegetation index (NDVI) and estimated evapotranspiration (ET) using the Two-Source Energy Balance (TSEB) model are presented. The results indicated that two of the shadow detection methods, the supervised classification and index-based methods, had better performance than two other methods. Furthermore, assessment of shadowed pixels in the vine canopy led to significant differences in the calculated NDVI and ET in areas affected by shadows in the high-resolution imagery. Shadows are shown to have the greatest impact on modeled soil heat flux, while net radiation and sensible heat flux are less affected. Shadows also have an impact on the modeled Bowen ratio (ratio of sensible to latent heat) which can be used as an indicator of vine stress level

Mapping evapotranspiration variability over a complex oasis-desert ecosystem based on automated calibration of Landsat 7 ETM+ data in SEBAL

Fragmented ecosystems of the desiccated Aral Sea seek answers to the profound local hydrologically- and water-related problems. Particularly, in the Small Aral Sea Basin (SASB), these problems are associated with low precipitation, increased temperature, land use and evapotranspiration (ET) changes. Here, the utility of high-resolution satellite dataset is employed to model the growing season dynamic of near-surface fluxes controlled by the advective effects of desert and oasis ecosystems in the SASB. This study adapted and applied the sensible heat flux calibration mechanism of Surface Energy Balance Algorithm for Land (SEBAL) to 16 clear-sky Landsat 7 ETM+ dataset, following a guided automatic pixels search from surface temperature T-s and Normalized Difference Vegetation Index NDVI (). Results were comprehensively validated with flux components and actual ET (ETa) outputs of Eddy Covariance (EC) and Meteorological Station (KZL) observations located in the desert and oasis, respectively. Compared with the original SEBAL, a noteworthy enhancement of flux estimations was achieved as follows: - desert ecosystem ETa R-2 = 0.94; oasis ecosystem ETa R-2 = 0.98 (P < 0.05). The improvement uncovered the exact land use contributions to ETa variability, with average estimates ranging from 1.24 mm to 6.98 mm . Additionally, instantaneous ET to NDVI (ETins-NDVI) ratio indicated that desert and oasis consumptive water use vary significantly with time of the season. This study indicates the possibility of continuous daily ET monitoring with considerable implications for improving water resources decision support over complex data-scarce drylands

Incorporating an iterative energy restraint for the Surface Energy Balance System (SEBS)

© 2017 Elsevier Inc. The Surface Energy Balance System (SEBS) has proven itself as an effective remotely sensed estimator of actual evapotranspiration (ETa). However, it has several vulnerabilities associated with the partitioning of the available energy (AE) at the land surface. We introduce a two stage energy restraint process into the SEBS algorithm (SEBS-ER) to overcome these vulnerabilities. The first offsets the remotely sensed surface temperature to ensure the surface to air temperature difference reflects AE, while the second stage uses a domain based image search process to identify and adjust the proportions of sensible (H) and latent (λE) heat flux with respect to AE. We effectively implemented SEBS-ER over 61 acquisitions over two Landsat tiles (path 90 row 84 and path 91 row 85) in south-eastern Australia that feature heterogeneous land covers. Across the two areas we showed that the SEBS-ER algorithm has: greater resilience to perturbed errors in surface energy balance algorithm inputs; significantly improved accuracy (p < 0.05) at two eddy covariance flux towers in heavily forested (RMSE 62.3 W m− 2, R2 0.879) and sub-alpine grassland (RMSE 33.2 W m− 2, R2 0.939) land covers; and greater temporal stability across 52 daily actual evapotranspiration (ETa) estimates compared to a temporally stable and independent ETa dataset. The energy restraint within SEBS-ER has reduced exposure to the complex errors and uncertainties within remotely sensed, meteorological, and land type SEBS inputs, providing more reliable and accurate spatially distributed ETa products

Estimation of Surface Moisture Content and Evapotranspiration Using Weightage Approach.

Soil moisture (MC) and evapotranspiration (ET) are considered as the most significant boundary conditions controlling most of the hydrological cycle’s processes. However, monitoring them continuously over large areas using the high temporal-resolution optical satellites is very demanding. Satellites such as the Advanced Very High Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS), have a coarse spatial resolution in their images. Thus it not only impedes the acquisition of an accurate MC and ET but also represents multispectral reflections from the holistic surface features. This beside their dependence on vegetation and ground coefficient when assessing MC and ET. The study aims to enhance the spatial accuracy by weighting the MC produced from different surface cover classes within the pixel. MC for each pixel is segmented into three (3) different classes namely urban, vegetation and multi surface cover according to their respective MC weightage. Secondly, to generate an improved actual ETa map by overlaying the segmented MC with a rectified ETo. Images from AVHRR and MODIS satellites were selected in order to generate MC and ET maps. Two powerful MC algorithms were used based on land Surface Temperature (Ts), vegetation Indices (VI) and field measurements of MC; which were conducted at variable depths to examine the depth influence on MC and Ts magnitudes

A Review of Current Methodologies for Regional Evapotranspiration Estimation from Remotely Sensed Data

An overview of the commonly applied evapotranspiration (ET) models using remotely sensed data is given to provide insight into the estimation of ET on a regional scale from satellite data. Generally, these models vary greatly in inputs, main assumptions and accuracy of results, etc. Besides the generally used remotely sensed multi-spectral data from visible to thermal infrared bands, most remotely sensed ET models, from simplified equations models to the more complex physically based two-source energy balance models, must rely to a certain degree on ground-based auxiliary measurements in order to derive the turbulent heat fluxes on a regional scale. We discuss the main inputs, assumptions, theories, advantages and drawbacks of each model. Moreover, approaches to the extrapolation of instantaneous ET to the daily values are also briefly presented. In the final part, both associated problems and future trends regarding these remotely sensed ET models were analyzed to objectively show the limitations and promising aspects of the estimation of regional ET based on remotely sensed data and ground-based measurements

Land Surface Monitoring Based on Satellite Imagery

This book focuses attention on significant novel approaches developed to monitor land surface by exploiting satellite data in the infrared and visible ranges. Unlike in situ measurements, satellite data provide global coverage and higher temporal resolution, with very accurate retrievals of land parameters. This is fundamental in the study of climate change and global warming. The authors offer an overview of different methodologies to retrieve land surface parameters— evapotranspiration, emissivity contrast and water deficit indices, land subsidence, leaf area index, vegetation height, and crop coefficient—all of which play a significant role in the study of land cover, land use, monitoring of vegetation and soil water stress, as well as early warning and detection of forest fires and drought

Study of the urban heat island (UHI) using remote sensing data/techniques: a systematic review.

Urban Heat Islands (UHI) consist of the occurrence of higher temperatures in urbanized areas when compared to rural areas. During the warmer seasons, this effect can lead to thermal discomfort, higher energy consumption, and aggravated pollution effects. The application of Remote Sensing (RS) data/techniques using thermal sensors onboard satellites, drones, or aircraft, allow for the estimation of Land Surface Temperature (LST). This article presents a systematic review of publications in Scopus andWeb of Science (WOS) on UHI analysis using RS data/techniques and LST, from 2000 to 2020. The selection of articles considered keywords, title, abstract, and when deemed necessary, the full text. The process was conducted by two independent researchers and 579 articles, published in English, were selected. Qualitative and quantitative analyses were performed. Cfa climate areas are the most represented, as the Northern Hemisphere concentrates the most studied areas, especially in Asia (69.94%); Landsat products were the most applied to estimates LST (68.39%) and LULC (55.96%); ArcGIS (30.74%) was most used software for data treatment, and correlation (38.69%) was the most applied statistic technique. There is an increasing number of publications, especially from 2016, and the transversality of UHI studies corroborates the relevance of this topic.This work was funded by National Funds through the FCT-Foundation for Science and Technology and FEDER, under the projects UIDB/04683/2020 and PT2020 Program for financial support to CIMO UIDB/00690/2020. This work was funded by National Funds through the FCT-Foundation for Science and Technology and FEDER, under the projects UIDB/04683/2020 and PT2020 Program for financial support to CIMO UIDB/00690/2020.info:eu-repo/semantics/publishedVersio

The application of the surface energy balance system model to estimate evapotranspiration in South Africa

Includes abstract.Includes bibliographical references.In a water scarce country like South Africa with a number of large consumers of water, it is important to estimate evapotranspiration (ET) with a high degree of accuracy. This is especially important in the semi-arid regions where there is an increasing demand for water and a scarce supply thereof. ET varies regionally and seasonally, so knowledge about ET is fundamental to save and secure water for different uses, and to guarantee that water is distributed to water consumers in a sustainable manner. Models to estimate ET have been developed using a combination of meteorological and remote sensing data inputs. In this study, the pre-packaged Surface Energy Balance System (SEBS) model was used for the first time in the South African environment alongside MODerate Resolution Imaging Spectroradiometer (MODIS) satellite data and validated with eddy covariance data measured in a large apple orchard (11 ha), in the Piketberg area of the Western Cape. Due to the relative infancy of research in this field in South Africa, SEBS is an attractive model choice as it is available as open-source freeware. The model was found to underestimate the sensible heat flux through setting it at the wet limit. Daily ET measured by the eddy covariance system represented 55 to 96% of the SEBS estimate, an overestimation of daily ET. The consistent underestimation of the sensible heat flux was ascribed to sensitivities to the land surface air temperature gradient, the choice of fractional vegetation cover formula as well as the height of the vegetation canopy (3.2 m) relative to weather station reference height (2 m). The methodology was adapted based on the above findings and was applied to a second study area (quaternary catchment P10A, near Grahamstown, Eastern Cape) where two different approaches for deriving surface roughness are applied. It was again demonstrated that the sensible heat flux is sensitive to surface roughness in combination with land surface air temperature gradient and again, the overestimation of daily ET persisted (actual ET being greater than reference ET). It was concluded that in complex environments, at coarse resolution, it is not possible to adequately describe the remote sensing derived input parameters at the correct level of accuracy and at the spatial resolution required for the accurate estimation of the sensible heat flux

Assessment of different methods for shadow detection in high-resolution optical imagery and evaluation of shadow impact on calculation of NDVI, and evapotranspiration

Significant efforts have been made recently in the application of high-resolution remote sensing imagery (i.e., sub-meter) captured by unmanned aerial vehicles (UAVs) for precision agricultural applications for high-value crops such as wine grapes. However, at such high resolution, shadows will appear in the optical imagery effectively reducing the reflectance and emission signal received by imaging sensors. To date, research that evaluates procedures to identify the occurrence of shadows in imagery produced by UAVs is limited. In this study, the performance of four different shadow detection methods used in satellite imagery was evaluated for high-resolution UAV imagery collected over a California vineyard during the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) field campaigns. The performance of the shadow detection methods was compared and impacts of shadowed areas on the normalized difference vegetation index (NDVI) and estimated evapotranspiration (ET) using the Two-Source Energy Balance (TSEB) model are presented. The results indicated that two of the shadow detection methods, the supervised classification and index-based methods, had better performance than two other methods. Furthermore, assessment of shadowed pixels in the vine canopy led to significant differences in the calculated NDVI and ET in areas affected by shadows in the high-resolution imagery. Shadows are shown to have the greatest impact on modeled soil heat flux, while net radiation and sensible heat flux are less affected. Shadows also have an impact on the modeled Bowen ratio (ratio of sensible to latent heat) which can be used as an indicator of vine stress level.info:eu-repo/semantics/acceptedVersio

Land Use /Land Cover Driven Surface Energy Balance and Convective Rainfall Change in South Florida

Modification of land use/land cover in South Florida has posed a major challenge in the region’s eco-hydrology by shifting the surface-atmosphere water and energy balance. Although drainage and development in South Florida took place extensively between the mid- and late- 20th century, converting half of the original Everglades into agricultural and urban areas, urban expansion still accounts for a dominant mode of surface cover change in South Florida. Changes in surface cover directly affect the radiative, thermophysical and aerodynamic parameters which determine the absorption and partitioning of radiation into different components at the Earth surface. The alteration is responsible for changing the thermal structure of the surface and surface layer atmosphere, eventually modifying surface-induced convection. This dissertation is aimed at analyzing the extent and pattern of land cover change in South Florida and delineating the associated development of urban heat island (UHI), energy flux alteration, and convective rainfall modification using observed data, remotely sensed estimates, and modeled results. Urban land covers in South Florida are found to have increased by 10% from 1974 to 2011. Higher Landsat-derived land surface temperatures (LST) are observed in urban areas (LSTu-r =2.8°C) with satisfactory validation statistics for eastern stations (Nash-Sutcliffe coefficient =0.70 and R2 =0.79). Time series trends, significantly negative for diurnal temperature range (DTR= -1°C, p=0.005) and positive for lifting condensation level (LCL \u3e 20m) reveal temporal and conspicuous urban-rural differences in nocturnal temperature (ΔTu-r = 4°C) shows spatial signatures of UHI. Spatially higher (urban: 3, forest: 0.14) and temporally increasing (urban: 1.67 to 3) Bowen’s ratios, and sensible heat fluxes exceeding net radiation in medium and high-intensity developed areas in 2010 reflect the effect of urbanization on surface energy balance. Radar reflectivity-derived surface-induced convective rainfall reveals significantly positive mean differences (thunderstorm cell density: 6/1000 km2and rain rate: 0.24 mm/hr/summer, p \u3c 0.005) between urban and entire South Florida indicating convective enhancement by urban covers. The research fulfils its two-fold purposes: advancing the understanding of post-development hydrometeorology in South Florida and investigating the spatial and temporal impacts of land cover change on the microclimate of a subtropical city