Estimating daily evapotranspiration based on a model of evaporative fraction (EF) for mixed pixels

Currently, applications of remote sensing evapotranspiration (ET) products are limited by the coarse resolution of satellite remote sensing data caused by land surface heterogeneities and the temporal-scale extrapolation of the instantaneous latent heat flux (LE) based on satellite overpass time. This study proposes a simple but efficient model (EFAF) for estimating the daily ET of remotely sensed mixed pixels using a model of the evaporative fraction (EF) and area fraction (AF) to increase the accuracy of ET estimate over heterogeneous land surfaces. To accomplish this goal, we derive an equation for calculating the EF of mixed pixels based on two key hypotheses. Hypothesis 1 states that the available energy (AE) of each sub-pixel is approximately equal to that of any other sub-pixels in the same mixed pixel within an acceptable margin of error and is equivalent to the AE of the mixed pixel. This approach simplifies the equation, and uncertainties and errors related to the estimated ET values are minor. Hypothesis 2 states that the EF of each sub-pixel is equal to that of the nearest pure pixel(s) of the same land cover type. This equation is designed to correct spatial-scale errors for the EF of mixed pixels; it can be used to calculate daily ET from daily AE data. The model was applied to an artificial oasis located in the midstream area of the Heihe River using HJ-1B satellite data with a 300&thinsp;m resolution. The results generated before and after making corrections were compared and validated using site data from eddy covariance systems. The results show that the new model can significantly improve the accuracy of daily ET estimates relative to the lumped method; the coefficient of determination (R2) increased to 0.82 from 0.62, the root mean square error (RMSE) decreased to 1.60 from 2.47&thinsp;MJ&thinsp;m−2(decreased approximately to 0.64 from 0.99&thinsp;mm) and the mean bias error (MBE) decreased from 1.92 to 1.18&thinsp;MJ&thinsp;m−2 (decreased from approximately 0.77 to 0.47&thinsp;mm). It is concluded that EFAF can reproduce daily ET with reasonable accuracy; can be used to produce the ET product; and can be applied to hydrology research, precision agricultural management and monitoring natural ecosystems in the future.</p

Google Earth Engine Open-Source Code for Land Surface Temperature Estimation from the Landsat Series

Land Surface Temperature (LST) is increasingly important for various studies assessing land surface conditions, e.g., studies of urban climate, evapotranspiration, and vegetation stress. The Landsat series of satellites have the potential to provide LST estimates at a high spatial resolution, which is particularly appropriate for local or small-scale studies. Numerous studies have proposed LST retrieval algorithms for the Landsat series, and some datasets are available online. However, those datasets generally require the users to be able to handle large volumes of data. Google Earth Engine (GEE) is an online platform created to allow remote sensing users to easily perform big data analyses without increasing the demand for local computing resources. However, high spatial resolution LST datasets are currently not available in GEE. Here we provide a code repository that allows computing LSTs from Landsat 4, 5, 7, and 8 within GEE. The code may be used freely by users for computing Landsat LST as part of any analysis within GEE

Remote Sensing of Ecology, Biodiversity and Conservation: A Review from the Perspective of Remote Sensing Specialists

Remote sensing, the science of obtaining information via noncontact recording, has swept the fields of ecology, biodiversity and conservation (EBC). Several quality review papers have contributed to this field. However, these papers often discuss the issues from the standpoint of an ecologist or a biodiversity specialist. This review focuses on the spaceborne remote sensing of EBC from the perspective of remote sensing specialists, i.e., it is organized in the context of state-of-the-art remote sensing technology, including instruments and techniques. Herein, the instruments to be discussed consist of high spatial resolution, hyperspectral, thermal infrared, small-satellite constellation, and LIDAR sensors; and the techniques refer to image classification, vegetation index (VI), inversion algorithm, data fusion, and the integration of remote sensing (RS) and geographic information system (GIS)

Status of the Korba groundwater resources (Tunisia): observations and three-dimensional modelling of seawater intrusion

The Korba aquifer is located in the east of the Cape Bon peninsula in Tunisia. A large groundwater depression has been created in the central part of the aquifer since the 1980s, due to intense groundwater pumping for irrigation. The data collected show that the situation continues to deteriorate. Consequently, seawater is continuing to invade a large part of the aquifer. To better understand the situation and try to forecast its evolution, a three-dimensional (3D) transient density-dependent groundwater model has been developed. The model building process was difficult because of data required on groundwater discharge from thousands of unmonitored private wells. To circumvent that difficulty, indirect exhaustive information including remote sensing data and the physical parameters of the aquifer have been used in a multi-linear regression framework. The resulting 3D model shows that the aquifer is over-exploited. It also shows that after 50 years of exploitation, the time needed to turn back to the natural situation would be about 150 years if the authorities would ban all exploitation now. Such an asymmetry in the time scales required to contaminate or remediate an aquifer is an important characteristic of coastal aquifers that must be taken into account in their managemen

Assessment of methods for land surface temperature retrieval from Landsat-5 TM images applicable to multiscale tree-grass ecosystem modeling

Land Surface Temperature (LST) is one of the key inputs for Soil-Vegetation-Atmosphere transfer modeling in terrestrial ecosystems. In the frame of BIOSPEC (Linking spectral information at different spatial scales with biophysical parameters of Mediterranean vegetation in the context of global change) and FLUXPEC (Monitoring changes in water and carbon fluxes from remote and proximal sensing in Mediterranean “dehesa” ecosystem) projects LST retrieved from Landsat data is required to integrate ground-based observations of energy, water, and carbon fluxes with multi-scale remotely-sensed data and assess water and carbon balance in ecologically fragile heterogeneous ecosystem of Mediterranean wooded grassland (dehesa). Thus, three methods based on the Radiative Transfer Equation were used to extract LST from a series of 2009–2011 Landsat-5 TM images to assess the applicability for temperature input generation to a Landsat-MODIS LST integration. When compared to surface temperatures simulated using MODerate resolution atmospheric TRANsmission 5 (MODTRAN 5) with atmospheric profiles inputs (LSTref), values from Single-Channel (SC) algorithm are the closest (root-mean-square deviation (RMSD) = 0.50 °C); procedure based on the online Radiative Transfer Equation Atmospheric Correction Parameters Calculator (RTE-ACPC) shows RMSD = 0.85 °C; Mono-Window algorithm (MW) presents the highest RMSD (2.34 °C) with systematical LST underestimation (bias = 1.81 °C). Differences between Landsat-retrieved LST and MODIS LST are in the range of 2 to 4 °C and can be explained mainly by differences in observation geometry, emissivity, and time mismatch between Landsat and MODIS overpasses. There is a seasonal bias in Landsat-MODIS LST differences due to greater variations in surface emissivity and thermal contrasts between landcover components

Estimation of surface canopy water in Pacific Northwest forests by fusing radar, lidar, and climatic data

Surface Canopy Water (SCW) is the intercepted rain water that resides within the tree canopy and plays a significant role in the hydrological cycle. Challenges arise in measuring SCW in remote areas using traditional ground based techniques. Remote sensing in the radio spectrum has the potential to overcome the challenges where traditional modelling approaches face difficulties. In this study we investigated the capability of the most recent SAR platform, the Sentinel-1 constellation to estimate SCW. We measured the backscatter of six forest stands in the H J Andrews experimental forest in central Oregon (as well as four clear cut areas and one golf course) over three summers to describe how the backscatter signal changes with moisture. We found significant results when we executed the analysis on radar images on which individual trees crowns were delineated from lidar, as opposing to SCW estimated from individual pixels backscatter. Significant differences occur in the mean backscatter between radar images taken during rain vs. during dry periods (no rain for > 1h). A lack in sufficient data prevented the formulation of a robust predictive model, however our results suggest the posibilty of mapping canopy moisture using SAR in the Pacific Northwest

Evaluating the influence of the land surface and air temperature gradient on terrestrial flux estimates derived using satellite earth observation data.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.Abstract available in pdf

Relationship between aerodynamic roughness length and bulk sedge leaf area index in a mixed-species boreal mire complex

Leaf area index (LAI) is an important parameter in natural ecosystems, representing the seasonal development of vegetation and photosynthetic potential. However, direct measurement techniques require labor-intensive field campaigns that are usually limited in time, while remote sensing approaches often do not yield reliable estimates. Here we propose that the bulk LAI of sedges (LAI(s)) can be estimated alternatively from a micrometeorological parameter, the aerodynamic roughness length for momentum (z(0)). z(0) can be readily calculated from high-response turbulence and other meteorological data, typically measured continuously and routinely available at ecosystem research sites. The regressions of LAI versus z(0) were obtained using the data from two Finnish natural sites representative of boreal fen and bog ecosystems. LAI(s) was found to be well correlated with z(0) and sedge canopy height. Superior method performance was demonstrated in the fen ecosystem where the sedges make a bigger contribution to overall surface roughness than in bogs.Peer reviewe

Doctor of Philosophy

dissertationThe atmospheric boundary layer (ABL) has been widely investigated due to the complexity of its physical processes and its impact on human life. One of the most challenging yet critical topics in this layer is scalar transport. Many efforts have been dedicated to investigating heat and moisture transport in the ABL using experimental and numerical approaches over the last several decades. However, there are still many knowledge gaps that limit the performance of numerical weather prediction models, in particular over complex terrain. For example, insufficient understanding of near-surface processes has resulted difficulties in parameterizing meteorological variables in numerical models. Hence, the main objective of this work is to gain a better fundamental understanding of flow processes and scalar transport in the surface boundary layer over different types of terrain with the ultimate goal of improving numerical weather forecasting models by developing more accurate surface parameterizations. Three different topics are discussed in this dissertation. The first topic is a study of land-atmosphere interactions over a desert playa to better understand the impacts of spatial and temporal heterogeneity in water availability as part of the short-term hydrologic cycle. High evaporation rates and the exponential decay of these rates are observed following occasional rainfall events. Three main factors explained the fast evaporation observed following rain- fall. The first factor is the existence of a powerful positive feedback mechanisms initialized by rainfall events that leads to increasing volumetric water content, decreasing surface albedo and Bowen ratio, followed by increases in net radiation, and eventually the enhancement of evaporation rates. The second factor is the clay soil texture, which has low permeability and high capacity. The soil property makes more water available near the surface for evaporation. The third factor is the non-negligible nocturnal evaporation rates that are correlated with increasing soil moisture content. Moreover, a higher spatial variability of surface soil moisture and evaporation is observed when the surface is dry. The second topic is articulated around a case study of the mechanisms that modulates the evolution of valley fog. A typical shallow, early-morning, short- lived valley fog is observed in a sheltered alpine valley. This work shows that mountain circulations play a critical role in the formation and development of shallow valley fog by modulating temperature and moisture fields through katabatic flow interactions and gravity waves. In particular, internal gravity waves are shown to modulate fog processes by varying the near-surface temperature within a time period of ≈ 20 min. The purpose of the last topic is to better understand the potential temperature variance budget over three different surfaces, a desert playa (dry lakebed), characterized by a flat surface devoid of vegetation; a vegetated site, characterized by a flat valley floor covered with greasewood vegetation, and a mountain terrain site with a slope angle of 2 -4° and covered by high-elevation vegetation. The analysis reveals the presence of a 5-m layer where the production and dissipation terms of potential temperature variance drop rapidly below this level. Within the 5-m layer, turbulent transport of potential temperature variance acts as a sink term at all sites of interest. The ratio of turbulent transport to production of potential temperature variance remains constant as stability decreases. The imbalance ratio between production and dissipation shows no correlation with the stability conditions

Monitoring the Sustainable Intensification of Arable Agriculture:the Potential Role of Earth Observation

Sustainable intensification (SI) has been proposed as a possible solution to the conflicting problems of meeting projected increases in food demand and preserving environmental quality. SI would provide necessary production increases while simultaneously reducing or eliminating environmental degradation, without taking land from competing demands. An important component of achieving these aims is the development of suitable methods for assessing the temporal variability of both the intensification and sustainability of agriculture. Current assessments rely on traditional data collection methods that produce data of limited spatial and temporal resolution. Earth Observation (EO) provides a readily accessible, long-term dataset with global coverage at various spatial and temporal resolutions. In this paper we demonstrate how EO could significantly contribute to SI assessments, providing opportunities to quantify agricultural intensity and environmental sustainability. We review an extensive body of research on EO-based methods to assess multiple indicators of both agricultural intensity and environmental sustainability. To date these techniques have not been combined to assess SI; here we identify the opportunities and initial steps required to achieve this. In this context, we propose the development of a set of essential sustainable intensification variables (ESIVs) that could be derived from EO data