Remote Sensing-based Model Research Articles

Remote estimates of CDOM using Sentinel-2 remote sensing data in reservoirs with different trophic states across China

Chromophoric dissolved organic matter (DOM) is called as CDOM which could affect the optical properties of surface waters, and is a useful parameter for monitoring complex inland aquatic systems. Large-scale monitoring of CDOM using remote-sensing has been a challenge due to the poor transferability of CDOM retrieval models across regions. To overcome these difficulties, a study is conducted using Sentinel-2 images, in situ reflectance spectral data, and water chemical parameters at 93 water reservoirs across China classified by trophic state. Empirical algorithms are established between CDOM absorption coefficient aCDOM(355) and reflectance band ratio (B5/B2,vegetation Red Edge/Blue) acquired in situ and via Sentinel-2 MSI sensors. Relationships are stronger (r2 > 0.7, p < 0.05) when analysis is conducted separately by trophic states. Validation models show that, by accounting for trophic state of reservoirs and using B5/B2 band ratios, it is possible to expand the geographical range of remote sensing-based models to determine CDOM. However, the accuracy of model validation decreased from oligotrophic (r2: 0.86) to eutrophic reservoirs (r2: 0.82), likely due to increased complexity of CDOM sources in nutrient-rich systems. This study provides a strategy for using local and remote-sensing data to monitor the spatial variations of CDOM in reservoirs based on different trophic states, and will contribute to water resources management.

Energy partitioning between plant canopy and soil, performance of the two-source energy balance model in a vineyard

Partitioning of evapotranspiration (ET) into soil water evaporation and transpiration allows separate assessment of soil and plant water, energy, and carbon exchange. Remote sensing-based models are ideally suited to monitor ET over large areas, but ET partitioning estimates vary widely. The objective of this study was to evaluate the two-source energy balance (TSEB) model for seasonal ET partitioning using total, soil, and vine canopy energy balance fluxes measured over a vineyard in the Negev desert in Israel. Energy fluxes were evaluated with the original TSEB and three adapted versions using (1) measured soil heat flux, (2) optimized plant transpiration parameterization, and (3) measured soil and vine temperatures instead of composite surface temperature as model inputs. Optimization of plant transpiration parameters revealed a model tendency to underestimate transpiration due to underestimation of available energy and potential transpiration. Adaptations included, among others, accounting for higher leaf radiation absorption expected in dense clumped canopies, which increases available energy, and increasing the Priestley-Taylor coefficient from 1.26 to 2, which increases potential transpiration. While the original TSEB gave reasonable total energy fluxes, the vine energy fluxes were greatly underestimated. Both soil heat flux and plant transpiration adaptations improved modeled vine energy fluxes throughout the season under both well-watered and water-stressed conditions. However, the performance of the TSEB version using measured soil and vine temperatures was inferior to applying the standard TSEB with composite temperature. While daily energy fluxes could be estimated with reasonable accuracy, sub-daily fluxes proved to be more challenging and merit further research. Finally, changes in ET partitioning with canopy development and in response to water stress could be detected quite well albeit it with an underestimation of the transpiration fraction of ET, which, on average, amounted to 39% using standard TSEB and 6% with optimized plant transpiration parameters.

Using SMAP Level-4 soil moisture to constrain MOD16 evapotranspiration over the contiguous USA

Evapotranspiration (ET) is a key hydrologic variable linking the Earth's water, carbon and energy cycles. At large spatial scales, remote sensing-based (RS) models are often used to quantify ET. Despite the large number of RS ET models available, few include soil moisture as a key environmental input, which can degrade model accuracy and utility. Here, we use model assimilation enhanced soil moisture estimates from the NASA SMAP (Soil Moisture Active Passive) mission as a water supply control in the MOD16 ET algorithm framework. SMAP-derived daily surface (0-5 cm depth) and root zone (0-1 m depth) soil moisture are used with MODIS (Moderate Resolution Imaging Spectroradiometer) vegetation observations, and 4 km gridded regional surface meteorology (Gridmet) as primary inputs for estimating daily ET and underlying model soil and stomatal conductance terms. We calibrated the model environmental response parameters using tower eddy covariance ET observations representing major North American biomes. The model ET results were validated using a holdout set of tower observations spanning a large regional climate gradient. The updated ET estimates outperform the baseline MOD16 product across all tower validation sites (RMSE = 0.758 vs 1.108 mm day−1; R2 = 0.68 vs 0.45, respectively). Smaller relative improvements were obtained using a recalibrated model with 4 km Gridmet meteorology, but no soil moisture control (RMSE = 0.813 mm day−1; R2 = 0.66), indicating that these changes are essential for the improved model performance. The soil moisture-constrained model improvements and relative benefits from the SMAP observations are greater in arid climates, consistent with stronger soil moisture control on ET in water-limited regions. The use of SMAP soil moisture as an additional model constraint improves MOD16 regional performance and provides a new framework for investigating both soil and atmosphere controls on ET.

Regional Distribution of Net Radiation over Different Ecohydrological Land Surfaces

Net radiation is an important component of the earth’s surface energy balance, which plays a vital role in the evolution of regional climate or climate change. The estimation of this component at regional or global scales is critical and challenging due to the sparse and limited ground-based observations. This paper made an attempt to analyze the feasibility of a remote sensing-based surface energy balance model using satellite (TERRA/MODIS) data to derive the net radiation (Rn). In the present study, MODIS data at 15 different days of the year (DOY) were utilized to visualize the spatial pattern of net radiation flux over three versatile and heterogeneous ecohydrological land surfaces (upstream, midstream, and downstream) of northwest China (Heihe river basin). The results revealed that the estimated net radiation from the satellite data agrees well with the ground-based measurements over three different surfaces, with a mean relative error of 9.33% over the upstream superstation (grasslands), 13.95% over the middle stream superstation (croplands), and 11.63% over the downstream superstation (mixed forests), where the overall relative error was 11.64% with an overall rmse of 29.36 W/m2 in the study area. The regional distribution of net radiation over the versatile land surfaces was validated well at a large scale during the five-month period and over different land surfaces. It was also observed that the spatial pattern of net radiation varies spatially over three different landscape regions during four different days of the year, which might be associated with different climatic conditions and landscape features in these regions. The overall findings of this study concluded that satellite-derived net radiation can rationally be obtained using a single-source remote sensing model over different land surfaces.

Carbon exchange of a dryland cotton field and its relationship with PlanetScope remote sensing data

Agricultural systems experience numerous management-associated events during the growing season that can significantly influence seasonal and annual carbon balances. Measurements of carbon fluxes from agricultural fields using micrometeorological techniques such as eddy covariance can improve our understanding of management and weather-driven changes in carbon budgets. In-situ carbon flux data are also valuable in developing remote sensing-based models for extrapolation of biome-specific carbon budgets to higher spatial scales. In this study, net ecosystem carbon dioxide exchange (NEE) was continuously measured for two years (2017 and 2018) from a cotton (Gossypium hirsutum) field in College Station, Texas, USA. The measured NEE was partitioned into assimilatory (Gross Primary Production, GPP) and respiratory (Ecosystem respiration, Reco) fluxes. There were substantial differences in carbon fluxes between the two years, which were driven by variations in meteorological conditions and growth of weeds. Due to dry conditions, growing season carbon uptake in 2018 was reduced (883 g C m−2) compared to 2017 (947 g C m−2). While the growing season of 2018 was dry, the post-harvest off-season was remarkably wet with nearly 68% of the annual precipitation occurring after harvest (848 mm). This favored aggressive growth of weeds, resulting in substantial off-season carbon uptake in 2018 (374 g C m−2 in 2018 compared to 100 g C m−2 in 2017). Overall, the site was a net carbon source (175 g C m−2) in 2017, whereas it was a slight carbon sink (-5 g C m−2) in 2018. A significant correlation was found between satellite-derived normalized difference vegetation index (NDVI) and GPP (R2 0.78 in 2017 and 0.72 in 2018). Given that correlation, it would be possible to broaden these results to the wider region by estimating GPP with satellite data.

Predicting forest fire risk based on mining rules with ant-miner algorithm in cloud-rich areas

Annually, millions of hectares of forest lands around the world are destroyed by fires. To minimize the fire-caused losses, more studies on the risk prediction of forest fires need to be carried out. For predicting the risk of forest fires in cloud-rich areas (e.g., the southwest of China), the synergetic use of operational forecasting systems and remote sensing-based models is expected to have a consistent performance. Therefore, we proposed in this study a new model based on ant-miner algorithm which has a good capability of solving multivariable and non-linear problems in the synergetic modeling of multi-source data. Based on historical fire data during 2000–2018 in Chongqing city, its performance was tested, and then was compared with that of other three models (i.e., meteorological data-, Artificial Neural Network-, and Support Vector Machine-based models). Results showed that, without interference from human factors, the risk predictions of proposed model were more objective. And, its mined-rules were easier to understand and also portable across multiple GIS platforms. Moreover, the proposed model has a better performance at predicting risk levels (i.e., overall accuracy was 79.02% and Kappa coefficient was 0.678) and the spatial distribution of its predictions were more detailed. This research indicated that the ant-miner algorithm-based model was more effective and reliable, and it could be used for constructing the operational system of risk predictions for forest fires in cloud-rich areas.

Synergistic Use of Remote Sensing and Modeling for Estimating Net Primary Productivity in the Red Sea With VGPM, Eppley-VGPM, and CbPM Models Intercomparison

Primary productivity (PP) has been recently investigated using remote sensing-based models over quite limited geographical areas of the Red Sea. This work sheds light on how phytoplankton and primary production would react to the effects of global warming in the extreme environment of the Red Sea and, hence, illuminates how similar regions may behave in the context of climate variability. study focuses on using satellite observations to conduct an intercomparison of three net primary production (NPP) models-the vertically generalized production model (VGPM), the Eppley-VGPM, and the carbon-based production model (CbPM)-produced over the Red Sea domain for the 1998-2018 time period. A detailed investigation is conducted using multilinear regression analysis, multivariate visualization, and moving averages correlative analysis to uncover the models' responses to various climate factors. Here, we use the models' eight-day composite and monthly averages compared with satellite-based variables, including chlorophyll-a (Chla), mixed layer depth (MLD), and sea-surface temperature (SST). Seasonal anomalies of NPP are analyzed against different climate indices, namely, the North Pacific Gyre Oscillation (NPGO), the multivariate ENSO Index (MEI), the Pacific Decadal Oscillation (PDO), the North Atlantic Oscillation (NAO), and the Dipole Mode Index (DMI). In our study, only the CbPM showed significant correlations with NPGO, MEI, and PDO, with disagreements relative to the other two NPP models. This can be attributed to the models' connection to oceanographic and atmospheric parameters, as well as the trends in the southern Red Sea, thus calling for further validation efforts.

Tracking the phenology of photosynthesis using carotenoid-sensitive and near-infrared reflectance vegetation indices in a temperate evergreen and mixed deciduous forest.

Photosynthetic phenology is an important indicator of annual gross primary productivity (GPP). Assessing photosynthetic phenology remotely is difficult for evergreen conifers as they remain green year-round. Carotenoid-based vegetation indices such as the photochemical reflectance index (PRI) and chlorophyll/carotenoid index (CCI) are promising tools to remotely track the invisible phenology of photosynthesis by assessing carotenoid pigment dynamics. PRI, CCI and the near-infrared reflectance of vegetation (NIRV ) index may act as proxies of photosynthetic efficiency (ɛ), an important parameter in light-use efficiency models, or direct proxies of photosynthesis. To understand the physiological mechanisms reflected by PRI and CCI and the ability of vegetation indices to act as proxies of photosynthetic activity for estimating GPP, we measured leaf pigment composition, PRI, CCI, NIRV and photosynthetic activity at the leaf and canopy scales over 2 years in an evergreen and mixed deciduous forest. PRI and CCI captured the large seasonal carotenoid/chlorophyll ratio changes and good relationships were observed between PRI-ɛ and CCI-photosynthesis and NIRV -photosynthesis. PRI-, CCI- and NIRV -based models effectively tracked observed seasonal GPP. We propose that carotenoid-based and near-infrared reflectance vegetation indices may provide useful proxies of photosynthetic activity and can improve remote sensing-based models of GPP in evergreen and deciduous forests.

The potential of remote sensing-based models on global water-use efficiency estimation: An evaluation and intercomparison of an ecosystem model (BESS) and algorithm (MODIS) using site level and upscaled eddy covariance data

Ecosystem water-use efficiency (WUE) is a critical indicator to investigate the interaction between the terrestrial ecosystem carbon and water cycles. WUE, estimated from gross primary productivity (GPP) and evapotranspiration (ET) based on remote sensing (RS)-based ecosystem models and algorithms (e.g., MODIS (MODerate resolution Imaging Spectroradiometer), BESS (Breathing Earth System Simulator)), have been used to quantify the spatiotemporal dynamics of WUE and its responses to environmental changes. However, few studies have assessed the ability of RS-based ecosystem models and algorithms on global WUE estimation. In this study, we evaluated 8-day and annual WUE from MODIS and BESS among different sites, land cover types and climate zones using the FLUXNET2015 dataset as reference, and conducted spatial intercomparisons of annual WUE between MODIS, BESS and an upscaled FLUXNET dataset (MTE). The site level evaluation results showed that BESS WUE had better performance than MODIS WUE at both 8-day and annual scales. Among different land cover types and climate zones, MODIS and BESS WUE performed unsatisfactorily, especially for MODIS WUE in open shrublands and savannas and for BESS WUE in closed shrublands. Additionally, both MODIS and BESS WUE performed poorly in the hot semi-arid climate zone. The spatial intercomparisons over 2001-2011 revealed that BESS WUE had similar spatial patterns of annual WUE and linear trends with MTE WUE over the globe, except at the high latitudes. However, the spatiotemporal patterns of MODIS WUE were different from those of MTE and BESS WUE, particularly in the (sub) tropical arid and semi-arid regions. Our evaluations results suggested that coupling carbon and water cycles into RS-based models could improve their performance on global WUE estimation. Moreover, the performance of MODIS and BESS on global WUE estimation should be further improved, especially for their performance on temporal variation and their performance at the (semi) arid areas and the high latitudes.

An Assessment of Using Remote Sensing-based Models to Estimate Ground Surface Soil Heat Flux on the Tibetan Plateau during the Freeze-thaw Process

The ground surface soil heat flux (G0) is very important to simulate the changes of frozen ground and the active layer thickness; in addition, the freeze-thaw cycle will also affect G0 on the Tibetan Plateau (TP). As G0 could not be measured directly and soil heat flux is difficult to be observed on the TP in situ due to its high altitude and cold environment, most of previous studies have directly applied existing remote sensing-based models to estimate G0 without assessing whether the selected model is the best one of those models for those study regions. We use in-situ observation data collected at 12 sites combined with Moderate Resolution Imaging Spectroradiometer (MODIS) data (MOD13Q1, MODLT1D, MOD09CMG, and MCD15A2H) and the China meteorological forcing dataset (CMFD-SRad and CMFD-LRad) to validate the main models during the freeze-thaw process. The results show that during the three stages (complete freezing (CF), daily freeze-thaw cycle (DFT), and complete thawing (CT)) of the freeze-thaw cycle, the root mean square error (RMSE) between the models' G0 simulated value and the corresponding G0 "measured value" is the largest in the CT phase and smallest in the CF phase. The simulated results of the second group schemes (SEBAL, Ma, SEBALadj, and Maadj) were slightly underestimated, more stable, and closer to the measured values than the first group schemes (Choudhury, Clawson, SEBS, Choudhuryadj, Clawsonadj, and SEBSadj). The Maadj scheme is the one with the smallest RMSE among all the schemes and could be directly applied across the entire TP. Then, four possible reasons leading to the errors of the main schemes were analyzed. The soil moisture affecting the ratio G0/Rn and the phase shift between G0 and net radiation Rn are not considered in the schemes directly; the scheme cannot completely and correctly capture the direction of G0; and the input data of the schemes to estimate the regional G0 maybe bring some errors into the simulated results. The results are expected to provide a basis for selecting remote sensing-based models to simulate G0 in frozen ground dynamics and to calculate evapotranspiration on the TP during the freeze-thaw process. The scheme Maadj suitable for the TP was also offered in the study. We proposed several improvement directions of remote sensing-based models in order to enhance understanding of the energy exchange between the ground surface and the atmosphere.

Estimating aboveground forest carbon density using Landsat 8 and field-based data: a comparison of modelling approaches

ABSTRACTAccurately estimating and mapping forest aboveground carbon density (AGCD) is important for evaluating forest carbon sequestration efficiency and dynamics. This study compares different modelling approaches for estimating AGCD using Landsat 8 Operational Land Imager (OLI) imagery and field forest inventory data. We tested it in Yongshun County, Hunan Province, China. Linear regression (LR) and Random Forest (RF) were used to map crown density (CD). We fitted models with and without CD dummy variables for AGCD estimations. The fitting results and performances of these two types of AGCD models were analysed and compared. There was a difference in the estimation of remote-sensing-based CD models and AGCD models; the coefficients of determination (R2) were 0.66 for LR and 0.84 for RF, respectively, in CD estimations; R2 values were 0.46 for LR and 0.65 for RF, respectively, in AGCD estimations as indicated by basic AGCD models. RF was the best algorithm for CD and AGCD estimations. All of the dummy variable AGCD models provided more accurate AGCD estimates than their basic AGCD counterparts. The dummy variable models (DLR and DRF), which took the observed CD as a dummy variable, were the most accurate for the R2 were 0.61 and 0.85, respectively. The AGCD estimations were significantly overestimated and underestimated by basic AGCD models in thin and dense CD classes, respectively. Nevertheless, none of the AGCD estimates produced by any of the dummy variable models showed this problem. The models that used predicted CD as a dummy variable were more consistent with those that considered observed CD as a dummy variable. The accuracies of AGCD estimations were significantly improved by the dummy variable AGCD models, especially in thin and dense plots; in addition, the CD estimations produced by CD models could be used as dummy variables in AGCD remote-sensing-based models. This work provides that a method considering CD dummy variable in remote sensing-based models was reliable for mapping AGCD at a regional scale.

Introducing a New Remote Sensing-Based Model for Forecasting Forest Fire Danger Conditions at a Four-Day Scale

Forest fires are natural disasters that create a significant risk to the communities living in the vicinity of forested landscape. To minimize the risk of forest fires for the resilience of such urban communities and forested ecosystems, we proposed a new remote sensing-based medium-term (i.e., four-day) forest fire danger forecasting system (FFDFS) based on an existing framework, and applied the system over the forested regions in the northern Alberta, Canada. Hence, we first employed moderate resolution imaging spectroradiometer (MODIS)-derived daily land surface temperature (Ts) and surface reflectance products along with the annual land cover to generate three four-day composite for Ts, normalized difference vegetation index (NDVI), and normalized difference water index (NDWI) at 500 m spatial resolution for the next four days over the forest-dominant regions. Upon generating these four-day composites, we calculated the variable-specific mean values to determine variable-specific fire danger maps with two danger classes (i.e., high and low). Then, by assuming the cloud-contaminated pixels as the low fire danger areas, we combined these three danger maps to generate a four-day fire danger map with four danger classes (i.e., low, moderate, high, and very high) over our study area of interest, which was further enhanced by incorporation of a human-caused static fire danger map. Finally, the four-day scale fire danger maps were evaluated using observed/ground-based forest fire occurrences during the 2015–2017 fire seasons. The results revealed that our proposed system was able to detect about 75% of the fire events in the top two danger classes (i.e., high and very high). The system was also able to predict the 2016 Horse River wildfire, the worst fire event in Albertian and Canadian history, with about 67% agreement. The higher accuracy outputs from our proposed model indicated that it could be implemented in the operational management, which would be very useful for lessening the adverse impact of such fire events.

RICE (KHARIF) PRODUCTION ESTIMATION USING SAR DATA OF DIFFERENT SATELLITES AND YIELD MODELS: A COMPARATIVE ANALYSIS OF THE ESTIMATES GENERATED UNDER FASAL PROJECT

Abstract. Rice is the most important food crop of India. Majority of Rice is sown in kharif season in the country. This is monsoon season for the country where cloud cover poses a major problem for optical remote sensing. Therefore, for these states rice acreage estimation is being done using Synthetic Aperture Radar (SAR) data operationally in India since 1998. A case study is presented in this paper for analysis of past 6 years’ (2012–13 to 2017–18) estimations. Multi temporal Radarsat-2 (HH), RISAT-1 ScanSAR (HH) and Sentinel-1 (VV) data was used in years 2012, 2013–2016, and 2017, respectively for paddy identification. Hierarchal Decision Rule based classification (HDRC) approach was used to identify rice areas under sample segments. Extensive ground truth collected by state remote sensing departments and agriculture departments was utilized in setting the limits of HDRC models and accuracy assessment. Yield was estimated using weather based and remote sensing-based models. Area, production and yield estimates were made and compared with those given by DES. RMSE and R2 were used as statistical measures to assess the accuracy of results. The RMSE % ranged from 2.3 to 4.3; 0.84 to 1.35; 0.24 to 0.27 for area, production and yield respectively. The coefficient of determination (R2) ranged from 0.62 to 0.92; 0.75 to 0.91; 0.5 to 0.83 for area, production and yield respectively. The study showed that use of multi temporal SAR data (both HH and VV) is quite useful for paddy acreage estimation, especially during monsoon.

Local climate zone classification of surface energy flux distribution within an urban area of a hot-humid tropical city

In this study, the surface energy flux distribution within Akure city, Nigeria, was assessed using a remote sensing-based model. The Surface Energy Balance over Land (SEBAL) model was used to estimate the net radiation, soil heat flux, sensible and latent heat flux over the city. The estimated fluxes were extracted based on Local Climate Zones (LCZ) and the intensity of each component was assessed. Results showed that LCZ_3 and LCZ_6 with the highest impervious surface cover (above 50%) had higher sensible heat flux intensities, while other zones with lower percentage of impervious surface cover such as LCZ_6c and LCZ_9 had lower sensible heat flux intensities. The derived mean sensible heat flux at the built-up zones ranged between 196 W/m2 and 349 W/m2 while the latent heat flux ranged between 78 W/m2 and 295 W/m2. The latent heat flux was lower over the built-up zones compared to the zones with intense vegetation which recorded as high as 415 W/m2. The urban surface characteristics were shown to have impact on the surface energy flux distribution within the city as the components were altered differently and at dissimilar intensities at each zone.

RegisTree: a registration algorithm to enhance forest inventory plot georeferencing

The accuracy of remote sensing-based models of forest attributes could be improved by controlling the spatial registration of field and remote sensing data. We have demonstrated the potential of an algorithm matching plot-level field tree positions with lidar canopy height models and derived local maxima to achieve a precise registration automatically. The accuracy of remote sensing-based estimates of forest parameters depends on the quality of the spatial registration of the training data. This study introduces an algorithm called RegisTree to correct field plot positions by matching a spatialized field tree height map with lidar canopy height models (CHMs). RegisTree is based on a point (field positions) to surface (CHM) adjustment approach modified to ensure that at least one field tree position corresponds to CHM local maxima. RegisTree has been validated with respect to positioning errors and the performance of lidar-derived estimation of plot volume. Overall, RegisTree enabled to register field plots surveyed in a range of forest conditions with a precision of 1.5 m (± 1.23 m), but a higher performance for conifer plots, and a limited efficiency in homogeneous stands, having similar heights. Improved plot positions were found to have a limited impact on volume predictions under the range of tested conditions, with a gain up to 1.3%. RegisTree could be used to improve the forest plot position from field surveys collected with low-grade GPS and to contribute to the development of processing chains of 3D remote sensing-based models of forest parameters.

Estimating Surface Soil Heat Flux in Permafrost Regions Using Remote Sensing-Based Models on the Northern Qinghai-Tibetan Plateau under Clear-Sky Conditions

The ground surface soil heat flux (G0) quantifies the energy transfer between the atmosphere and the ground through the land surface. However; it is difficult to obtain the spatial distribution of G0 in permafrost regions because of the limitation of in situ observation and complication of ground surface conditions. This study aims at developing an improved G0 parameterization scheme applicable to permafrost regions of the Qinghai-Tibet Plateau under clear-sky conditions. We validated several existing remote sensing-based models to estimate G0 by analyzing in situ measurement data. Based on the validation of previous models on G0; we added the solar time angle to the G0 parameterization scheme; which considered the phase difference problem. The maximum values of RMSE and MAE between “measured G0” and simulated G0 using the improved parameterization scheme and in situ data were calculated to be 6.102 W/m2 and 5.382 W/m2; respectively. When the error of the remotely sensed land surface temperature is less than 1 K and the surface albedo measured is less than 0.02; the accuracy of estimates based on remote sensing data for G0 will be less than 5%. MODIS data (surface reflectance; land surface temperature; and emissivity) were used to calculate G0 in a 10 x 10 km region around Tanggula site; which is located in the continuous permafrost region with long-term records of meteorological and permafrost parameters. The results obtained by the improved scheme and MODIS data were consistent with the observation. This study enhances our understanding of the impacts of climate change on the ground thermal regime of permafrost and the land surface processes between atmosphere and ground surface in cold regions.

Coastal accumulation of microplastic particles emitted from the Po River, Northern Italy: Comparing remote sensing and hydrodynamic modelling with in situ sample collections

Microplastic research has mainly concentrated on open seas, while riverine plumes remain largely unexplored despite their hypothesized importance as a microplastic source to coastal waters. This work aimed to model coastal accumulation of microplastic particles (1–5 mm) emitted by the Po River over 1.5 years. We posit that river-induced microplastic accumulation on adjacent coasts can be predicted using (1) hydrodynamic-based and (2) remote sensing-based modelling. Model accumulation maps were validated against sampling at nine beaches, with sediment microplastic concentrations up to 78 particles/kg (dry weight). Hydrodynamic modelling revealed that discharged particle amount is only semi-coupled to beaching rates, which are strongly mouth dependent and occur within the first ten days. Remote sensing modelling was found to better capture river mouth relative strength, and accumulation patterns were found consistent with hydrodynamic modelling. This methodology lays groundwork for developing an operational monitoring system to assess microplastic pollution emitted by a major river.

Influence of wind direction on the surface roughness of vineyards

Remote sensing-based models are the most viable means of collecting the high-resolution spatially distributed estimates of evaporative water loss needed to manage irrigation and ensure the effective use of limited water resources. However, due to the unique canopy structure and configuration of vineyards, these models may not be able to adequately describe the physical processes driving evapotranspiration from vineyards. Using data collected from 2014 to 2016 as a part of the Grape Remote sensing Atmospheric Profile and Evapotranspiration Experiment (GRAPEX), the twofold objective of this study was to (1) identify the relationship between the roughness parameters, zero-plane displacement height (do) and roughness length for momentum (zo), and local environmental conditions, specifically wind direction and vegetation density and (2) determine the effect of using these relationships on the ability of the remote sensing-based Two-Source Energy Balance (TSEB) model to estimate the sensible (H) and latent (λE) heat fluxes. Although little variation in do was identified during the growing season, a well-defined sigmoidal relationship was observed between zo and wind direction. When the output from a version of the TSEB model incorporating these relationships (TSEBVIN) was compared to output from the standard model (TSEBSTD), there were large changes to the roughness parameters, particularly zo, but only modest changes in the turbulent fluxes. When the output from TSEBVIN was compared to that of a version using a parameterization scheme representing open canopies (TSEBOPN), the mean absolute difference between the estimates of do and zo were 0.44 m and 0.25 m, respectively. While these values represent differences in excess of 45%, the turbulent fluxes differed by just 13 W m−2 or 10%, on average. The results suggest that the TSEB model is largely insensitive to changes in the roughness parameters for the range in roughness values evaluated in this study. This also suggests that the requirement for highly accurate roughness values has limited utility in the application of the TSEB model in vineyard systems. Since there is no significant advantage to using the more complex TSEBOPN and TSEBVIN models, it is recommended that the standard model be used.

Monitoring and validating spatially and temporally continuous daily evaporation and transpiration at river basin scale

Operational estimation of spatio-temporal continuously daily evapotranspiration (ET), and the components evaporation (E) and transpiration (T), at river basin scale is very useful for developing sustainable water resource strategies, particularly in regions of limited water supplies. In this study, multi-year all-weather daily ET, E and T were estimated using MODIS-based (Dual Temperature Difference) DTD model under different land covers in the Heihe river basin in China, with a total area of approximately 143 × 103 km2. The remotely sensed ET was validated using ground measurements from large aperture scintillometer systems, with a source area of several kilometers, over grassland, cropland and riparian shrub-forest land cover. The results showed that the remotely sensed ET produced mean absolute percent differences (MAPD) of around 20% with the ground measurements during the growing season under clear sky conditions, but the model performance deteriorated for cloudy days. However, the daily ET product gave reasonable estimates for croplands with an MAPD value of about 20% and the estimates of T/ET and E/ET in good agreement with ground measurements. The DTD model also significantly outperformed other remote sensing-based models being applied globally. Based on these results the DTD model is considered reliable for monitoring crop water use and stress and to develop efficient irrigation strategies.

Improving remote estimation of winter crops gross ecosystem production by inclusion of leaf area index in a spectral model.

The hysteresis of the seasonal relationships between vegetation indices (VIs) and gross ecosystem production (GEP) results in differences between these relationships during vegetative and reproductive phases of plant development cycle and may limit their applicability for estimation of croplands productivity over the entire season. To mitigate this problem and to increase the accuracy of remote sensing-based models for GEP estimation we developed a simple empirical model where greenness-related VIs are multiplied by the leaf area index (LAI). The product of this multiplication has the same seasonality as GEP, and specifically for vegetative periods of winter crops, it allowed the accuracy of GEP estimations to increase and resulted in a significant reduction of the hysteresis of VIs vs. GEP. Our objective was to test the multiyear relationships between VIs and daily GEP in order to develop more general models maintaining reliable performance when applied to years characterized by different climatic conditions. The general model parametrized with NDVI and LAI product allowed to estimate daily GEP of winter and spring crops with an error smaller than 14%, and the rate of GEP over- (for spring barley) or underestimation (for winter crops and potato) was smaller than 25%. The proposed approach may increase the accuracy of crop productivity estimation when greenness VIs are saturating early in the growing season.