Accurate estimates of land surface energy fluxes and irrigation requirements from UAV-based thermal and multispectral sensors

The two-source energy balance model estimates canopy transpiration (Tr) and soil evaporation (E) traditionally from satellite partitions of remotely sensed land surface temperature (LST) and the Priestley-Taylor equation (TSEB-PT) at seasonal time with limited accuracy. The high spatial-temporal resolution spectral data collected by unmanned aerial vehicles (UAVs) provide valuable opportunity to estimate Tr and E precisely, improve the understanding of the seasonal and the diurnal cycle of evapotranspiration (ET), and timely detect agricultural drought. The UAV data vary in spatial resolution and the uncertainty imposed on the TSEB-PT outcome has thus far not being considered. To address these challenges and prospects, a new energy flux modelling framework based on TSEB-PT for high spatial resolution thermal and multispectral UAV data is proposed in this paper. Diurnal variations of LST in agricultural fields were recorded with a thermal infrared camera installed on an UAV during drought in 2018 and 2019. Observing potato as a test crop, LST, plant biophysical parameters derived from corresponding UAV multispectral data, and meteorological forcing variables were employed as input variables to TSEB-PT. All analyses were conducted at different pixelation of the UAV data to quantify the effect of spatial resolution on the performance. The 1 m spatial resolution produced the highest correlation between Tr modelled by TSEB-PT and measured by sap flow sensors (R2 = 0.80), which was comparable to the 0.06, 0.1, 0.5 and 2 m pixel sizes (R2 = 0.76-0.78) and markedly higher than the lower resolutions of 2 to 24 m (R2 = 0.30-0.72). Modelled Tr was highly and significantly correlated with measured leaf water potential (R2 = 0.81) and stomatal conductance (R2 = 0.74). The computed irrigation requirements (IRs) reflected the field irrigation treatments, ET and conventional irrigation practices in the area with high accuracy. It was also found that using a net primary production model with explicit representation of temperature influences made it possible to distinguish effects of drought vis-a-vis heat stress on crop productivity and water use efficiency. The results showed excellent model performance for retrieving Tr and ET dynamics under drought stress and proved that the proposed remote sensing based TSEB-PT framework at UAV scale is a promising tool for the investigation of plant drought stress and water demand; this is particularly relevant for local and regional irrigations scheduling

Accurate estimates of land surface energy fluxes and irrigation requirements from UAV-based thermal and multispectral sensors

The two-source energy balance model estimates canopy transpiration (Tr) and soil evaporation (E) traditionally from satellite partitions of remotely sensed land surface temperature (LST) and the Priestley-Taylor equation (TSEB-PT) at seasonal time with limited accuracy. The high spatial–temporal resolution spectral data collected by unmanned aerial vehicles (UAVs) provide valuable opportunity to estimate Tr and E precisely, improve the understanding of the seasonal and the diurnal cycle of evapotranspiration (ET), and timely detect agricultural drought. The UAV data vary in spatial resolution and the uncertainty imposed on the TSEB-PT outcome has thus far not being considered. To address these challenges and prospects, a new energy flux modelling framework based on TSEB-PT for high spatial resolution thermal and multispectral UAV data is proposed in this paper. Diurnal variations of LST in agricultural fields were recorded with a thermal infrared camera installed on an UAV during drought in 2018 and 2019. Observing potato as a test crop, LST, plant biophysical parameters derived from corresponding UAV multispectral data, and meteorological forcing variables were employed as input variables to TSEB-PT. All analyses were conducted at different pixelation of the UAV data to quantify the effect of spatial resolution on the performance. The 1 m spatial resolution produced the highest correlation between Tr modelled by TSEB-PT and measured by sap flow sensors (R2 = 0.80), which was comparable to the 0.06, 0.1, 0.5 and 2 m pixel sizes (R2 = 0.76–0.78) and markedly higher than the lower resolutions of 2 to 24 m (R2 = 0.30–0.72). Modelled Tr was highly and significantly correlated with measured leaf water potential (R2 = 0.81) and stomatal conductance (R2 = 0.74). The computed irrigation requirements (IRs) reflected the field irrigation treatments, ET and conventional irrigation practices in the area with high accuracy. It was also found that using a net primary production model with explicit representation of temperature influences made it possible to distinguish effects of drought vis-a-vis heat stress on crop productivity and water use efficiency. The results showed excellent model performance for retrieving Tr and ET dynamics under drought stress and proved that the proposed remote sensing based TSEB-PT framework at UAV scale is a promising tool for the investigation of plant drought stress and water demand; this is particularly relevant for local and regional irrigations scheduling.info:eu-repo/semantics/publishedVersio

Iberian peninsula ecosystem carbon fluxes: a model-data integration study

Dissertação apresentada para obtenção do Grau de Doutor em Engenharia do Ambiente pela Universidade Nova de Lisboa,Faculdade de Ciências e TecnologiaTerrestrial ecosystems play a key role within the context of the global carbon cycle. Characterizing and understanding ecosystem level responses and feedbacks to climate drivers is essential for diagnostic purposes as well as climate modelling projections. Consequently,numerous modelling and data driven approaches emerge, aiming the appraisal of biosphereatmosphere carbon fluxes. The combination of biogeochemical models with observations of ecosystem carbon fluxes in a model-data integration framework enables the recognition of potential limitations of modelling approaches. In this regard, the steady-state assumption represents a general approach in the initialization routines of biogeochemical models that entails limitations in the ability to simulate net ecosystem fluxes and in model development exercises. The present research addresses the generalized assumption of initial steady-state conditions in ecosystem carbon pools for modelling carbon fluxes of terrestrial ecosystems, from local to regional scales. At local scale, this study aims to evaluate the implications of equilibrium assumptions on modelling performance and on optimized parameters and uncertainty estimates based on a model-data integration approach. These results further aim to support the estimates of regional net ecosystem fluxes, following a bottom-up approach, by focusing on parameters governing net primary production (NPP) and heterotrophic respiration (RH)processes, which determine the simulation of the net ecosystem production fluxes in the CASA model. An underlying goal of the current research is addressed by focusing on Mediterranean ecosystem types, or ecosystems potentially present in Iberia, and evaluate the general ability of terrestrial biogeochemical models in estimating net ecosystem fluxes for the Iberian Peninsula region. At regional scales, and given the limited information available, the main objective is to minimize the implications of the initial conditions in the evaluation of the temporal dynamics of net ecosystem fluxes. Inverse model parameter optimizations at site level are constrained by eddy-covariance measurements of net ecosystem fluxes and driven by local observations of meteorological variables and vegetation biophysical variables from remote sensing products. Optimizations under steady-state conditions show significantly poorer model performance and higher parameter uncertainties when compared to optimizations under relaxed initial conditions. In addition, assuming initial steady-state conditions tend to bias parameter retrievals – reducing NPP sensitivity to water availability and RH responses to temperature – in order to prescribe sink conditions. But nonequilibrium conditions can be experienced in soil and/or vegetation carbon pools under alternative underlying dynamics, which are solely discernible through the integration of additional information sources, circumventing equifinality issues.Portuguese Foundation for Science and Technology (FCT),the European Union under Operational Program “Science and Innovation” (POCI 2010), PhD grant ref. SFRH/BD/6517/2001, co-sponsored by the European Social Fund. Further support,concerning the final months of the PhD, was provided by a Max Planck Society research fellowship

Remote Sensing of Heat Fluxes Validation and Inter-Sensor Comparison

Instantaneous heat fluxes were modeled using data obtained from Landsat 5 TM (Thematic Mapper), Landsat 7 ETM+ (Enhanced Thematic Mapper Plus) and Terra MODIS (Moderate Resolution Imaging Spectroradioineter) using the Surface Energy Balance Algorithm for Land (SEBAL) model for cloud-free days. The modeled results were compared with measurements of net radiation (both incoming and outgoing, shortwave and longwave), soil sensible and latent heat fluxes from two flux towers located in Brookings, SD, and Fort Peck, MT. Flux tower data consisted of 30 minute averages at every half an hour, and footprints of contributing movement of air within the period were estimated for each satellite overpass by taking into account the factors of observation height, atmospheric stability, and surface roughness, as well as wind speed and directions (Hsieh et al. 2000). It was found that footprints (considering 90% contributing areas) were normally larger than the size of one Landsat pixel (30 m) but smaller than that of one MODIS pixel (1 km). Therefore, for Landsat the data were averaged for pixels within the concurrent footprint, and for MODIS the data for the particular pixel covering the flux tower was used. The R values between the modeled and the observed net radiation (Rn) for Landsat and MODIS were found to be 0.70 and 0.66, respectively. Relatively, comparisons between modeled and observed values were better at Brookings than at Fort Peck for both sensors. This may be because the former site has a relatively flat topography and larger fetch than the latter, minimizing the possible effects of terrain heterogeneity on incoming and outgoing radiation modeling. Both satellites performed poorly in modeling soil heat flux (G0) . Our results show that SEBAL provides a better modeling of sensible heat flux (H) with Landsat (R2= 0.62) than with MODIS (R2 = 0.11), even though the MODIS performance for estimating latent heat flux (lambdaE) improved (R2 = 0.37). The improvement found in estimating latent heat flux is probably due to the fact that in SEBAL cold pixels are used to estimate air temperature and then also used in computation for both Rn and H. The uncertainties associated with this assumption cancelled out in deriving lambdaE. Overall, SEBAL performed better in modeling the heat fluxes when Landsat data were used. This may be due to the scaling issue, as the footprint areas were always significantly less than a single MODIS pixel. By simulating MODIS observations using Landsat, it was found that the R2 value for the aggregated Landsat pixels decreased from 0.62 to 0.25 with an increase of root mean square difference (RMSD) from 50.5 to 68.3 Wm\u272. This suggested that the poor performance of MODIS in estimating heat fluxes was due to heterogeneity of the surface within a field of view. In addition, sensitivity analyses of the model to input parameters suggested that the model is more sensitive to surface-to- air temperature difference than to surface roughness conditions. Appendix A lists symbols mentioned in this thesis

Impact of climate and anthropogenic effects on the energy, water, and carbon budgets of monitored agrosystems: multi-site analysis combining modelling and experimentation

Les terres cultivées représentent une unité importante dans le climat mondial, et en réponse à la population, elles sont en expansion. Il est crucial de comprendre et de quantifier les interactions terre-atmosphère via les échanges d'eau, d'énergie et de carbone. Dans ce contexte, cette thèse a consisté à étudier la variabilité du bilan énergétique en fonction de différentes cultures, phénologies et pratiques agricoles via système Eddy-Covariance. En réponse au manque d'eau dans le sud-ouest de la France, deux modèles de surface (ISBA et ISBA-MEB) ont été évalués sur deux cultures (blé et maïs) pour évaluer leur capacité à estimer les flux d'énergie et d'eau. Enfin, en réponse à la contribution des terres cultivées à l'augmentation du dioxyde de carbone atmosphérique, la capacité du modèle ISBA-MEB à simuler correctement les principaux composants du carbone a été testée sur 11 saisons de maïs et de blé.Croplands represent an important unit within the global climate, and in response to population, they are expanding. Hence, understanding and quantifying the land-atmosphere interactions via water, energy and carbon exchanges is crucial. In this context, the first objective of this thesis studied the variability of the energy balance over different crops, phenologies, and farm practices at Lamasquère and Auradé. Secondly, in response to water scarcity and increasing drought in southwestern France, two land surface models (ISBA and ISBA-MEB) of different configurations were evaluated over some wheat and maize years to test their ability to estimate energy and water fluxes using measurements from an eddy covariance system as reference. Finally, in response to the contribution of croplands to increasing atmospheric carbon dioxide, the capability of the ISBA-MEB model to correctly simulate the major carbon components was tested over 11 seasons of maize and wheat

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

SATELLITE-BASED CHARACTERIZATION OF CROP TYPE AND PRODUCTIVITY OF AGROECOSYSTEMS: CASE STUDIES IN NORTHEAST CHINA, SOUTHERN AFRICA, AND CONTERMINOUS USA

Agroecosystem, or agricultural ecosystems, is the important anthropogenic ecosystem to meet the human demand for food, fiber, and feed, and it covers approximately 40-50% of the earth’s land surface. Accurate estimates of agricultural land use and land cover and Gross Primary Production (GPP) are indispensable for global food security and understanding variations in the terrestrial carbon budgets. This dissertation aimed to strengthen the capacities of remote sensing to produce the high-quality products of crop type maps and primary productivity on large regional scales. In chapter 2, we designed simple algorithms to identify paddy rice of two different phenological phases (flooding/transplanting and ripening) at regional scales using 30-m multi-temporal Landsat images. Sixteen Landsat images from 2010 - 2012 were used to generate the paddy rice map in the Sanjiang Plain, northeast China - one of the intensive paddy rice cultivation regions in Northern Asia. The user and producer accuracies of paddy rice on the resultant Landsat-based paddy rice map were 90% and 94%, respectively, and was an improvement over the paddy rice dataset derived through visual interpretation and digitalization on the fine-resolution satellite images and traditional agricultural census data. Chapter 3 evaluated the capacities of the temporal MODIS vegetation indices and the satellite-based Vegetation Photosynthesis Model (VPM) to describe phenology and model the seasonal dynamics of GPP for savanna woodlands in Southern Africa on the site level. The results showed that the VPM-based GPP estimates tracked the seasonal dynamics and interannual variation of GPP estimated from eddy covariance measurements at flux tower sites. This study suggests that the VPM is a valuable tool for estimating GPP of semi-arid and semi-humid savanna woodland ecosystems in Southern Africa. Chapter 4 assessed the accuracies of air temperature and downward shortwave radiation of the North America Regional Reanalysis (NARR) by the National Centers for Environmental Prediction (NCEP), and evaluated impacts of the accuracies of regional climate inputs on the VPM-based GPP estimates for U.S. Midwest cropland. The results implied that the bias of NARR downward shortwave radiation introduced significant uncertainties into the VPM-based GPP estimates, suggesting that more accurate surface radiation datasets are needed to estimate primary production of terrestrial ecosystems at regional and global scales. Chapter 5 presented independent and complementary analyses of the impact of 2012 flash drought on productivity in the U.S. Midwest using multiple sources of evidences, i.e., in-situ AmeriFlux CO2 data, satellite observations of vegetation indices and solar-induced chlorophyll fluorescence (SIF), and scaled ecosystem modeling. The results showed that phenological activities of all biomes advanced 1-2 weeks earlier in 2012 compared to other years of 2010-2014, and the drought threatened the U.S. Midwest agroecosystems. The growth of grassland/prairie and cropland was suppressed from June and it didn’t recover until the end of the growing season. As the frequency and severity of droughts have been predicted to increase in future, this study provides better insights into the impacts of flash droughts on vegetation productivity and carbon cycling of major biomes in the U.S. Midwest

Improving data-oriented light use efficiency models of gross primary productivity with remotely sensed spectral indices

Several studies sustained the possibility that a photochemical reflectance index (PRI) directly obtained from satellite data can be used as a proxy for ecosystem light use efficiency (LUE) in diagnostic models of gross primary productivity. This modelling approach would avoid the complications that are involved in using me- teorological data as constraints for a fixed maximum LUE. However, no unifying model predicting LUE across climate zones and time based on MODIS PRI has been published to date. This study evaluates the effectiveness with which MODIS-based PRI can be used to estimate ecosystem light use efficiency at study sites of different plant functional types and vegetation densities. The objective is to examine if known limitations such as dependence on viewing and illumination geometry can be overcome and a single PRI-based model of LUE (i.e. based on the same reference band) can be applied under a wide range of conditions. Fur- thermore, this thesis examines the effect of using different fraction of absorbed photosynthetically active radiation (faPAR) products on the in-situ LUE used as ground truth and thus on the whole evaluation exercise. The conclusion of this study is that estimating LUE at site-level based on PRI reduces uncertainty compared to the approaches relying on a maximum LUE reduced by minimum temperature and vapour pressure deficit. Despite the advantages of using PRI to estimate LUE at site-level, a universally applicable light use efficiency model based on MODIS PRI could not be established. Models that were optimised for a pool of data from several sites did not perform well

Standardisation of eddy-covariance flux measurements of methane and nitrous oxide

Commercially available fast-response analysers for methane (CH4) and nitrous oxide (N2O) have recently become more sensitive, more robust and easier to operate. This has made their application for long-term flux measurements with the eddycovariance method more feasible. Unlike for carbon dioxide (CO2) and water vapour (H2O), there have so far been no guidelines on how to optimise and standardise the measurements. This paper reviews the state-of-the-art of the various steps of the measurements and discusses aspects such as instrument selection, setup and maintenance, data processing as well as the additional measurements needed to aid interpretation and gap-filling. It presents the methodological protocol for eddy covariance measurements of CH4 and N2O fluxes as agreed for the ecosystem station network of the pan-European Research Infrastructure Integrated Carbon Observation System and provides a first international standard that is suggested to be adopted more widely. Fluxes can be episodic and the processes controlling the fluxes are complex, preventing simple mechanistic gap-filling strategies. Fluxes are often near or below the detection limit, requiring additional care during data processing. The protocol sets out the best practice for these conditions to avoid biasing the results and long-term budgets. It summarises the current approach to gap-filling.Peer reviewe