scholarly journals Comparison of Remotely Sensed Evapotranspiration Models Over Two Typical Sites in an Arid Riparian Ecosystem of Northwestern China

2020 ◽  
Vol 12 (9) ◽  
pp. 1434 ◽  
Author(s):  
Tao Du ◽  
Guofu Yuan ◽  
Li Wang ◽  
Xiaomin Sun ◽  
Rui Sun
Keyword(s):  
Meteorological Data ◽  
Regional Scale ◽  
Vegetation Indices ◽  
Model Performance ◽  
Remotely Sensed ◽  
Northwestern China ◽  
Primary Sources ◽  
Estimation Accuracy ◽  
Riparian Ecosystem ◽  
Arid And Semiarid

Accurate estimates of evapotranspiration (ET) are essential for the conservation of ecosystems and sustainable management of water resources in arid and semiarid regions. Over the last two decades, several empirical remotely sensed ET models (ERSETMs) had been developed and extensively used for regional-scale ET estimation in arid and semiarid ecosystems. These ERSETMs were constructed by combining datasets from different sites and relating measured daily ET to corresponding meteorological data and vegetation indices at the site scale. Then, regional-scale ET on a pixel basis can be estimated, using the established ERSETMs. The estimation accuracy of these ERSETMs at the site scale plays a fundamental and crucial role in regional-scale ET estimation. Recent studies have revealed that ET estimates from some of these models have significant uncertainties at different spatiotemporal scales. However, little information is available on the performance of these ERSETMs at the site scale. In this study, we compared eight ERSETMs, using ET measurements from 2013 to 2018 for two typical eddy covariance sites (Tamarix site and Populus site) in an arid riparian ecosystem of Northwestern China, intending to provide a guide for the selection of these models. Results showed that the Nagler-2013 model and the Yuan-2016 model outperformed the other models. There were substantial differences in the ET estimation of the eight ERSETMs at daily, monthly, and seasonal scales. The mean ET of the growing season from 2013 to 2018 ranged from 465.93 to 519.65 mm for the Tamarix site and from 386.22 to 437.05 mm for the Populus site, respectively. The differences in model structures and characterization of both meteorological conditions and vegetation factors were the primary sources of different model performance. Our findings provide useful information for choosing models and obtaining accurate ET estimation in arid regions.

scholarly journals Multilayer Soil Moisture Mapping at a Regional Scale from Multisource Data via a Machine Learning Method

2019 ◽  
Vol 11 (3) ◽  
pp. 284 ◽  
Author(s):  
Linglin Zeng ◽  
Shun Hu ◽  
Daxiang Xiang ◽  
Xiang Zhang ◽  
Deren Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Regional Scale ◽  
Remotely Sensed ◽  
Temporal Variations ◽  
Training Data ◽  
Estimation Accuracy ◽  
Learning Approaches ◽  
Remotely Sensed Data ◽  
Deep Soil

Soil moisture mapping at a regional scale is commonplace since these data are required in many applications, such as hydrological and agricultural analyses. The use of remotely sensed data for the estimation of deep soil moisture at a regional scale has received far less emphasis. The objective of this study was to map the 500-m, 8-day average and daily soil moisture at different soil depths in Oklahoma from remotely sensed and ground-measured data using the random forest (RF) method, which is one of the machine-learning approaches. In order to investigate the estimation accuracy of the RF method at both a spatial and a temporal scale, two independent soil moisture estimation experiments were conducted using data from 2010 to 2014: a year-to-year experiment (with a root mean square error (RMSE) ranging from 0.038 to 0.050 m3/m3) and a station-to-station experiment (with an RMSE ranging from 0.044 to 0.057 m3/m3). Then, the data requirements, importance factors, and spatial and temporal variations in estimation accuracy were discussed based on the results using the training data selected by iterated random sampling. The highly accurate estimations of both the surface and the deep soil moisture for the study area reveal the potential of RF methods when mapping soil moisture at a regional scale, especially when considering the high heterogeneity of land-cover types and topography in the study area.

scholarly journals Grain Yield Estimation in Rice Breeding Using Phenological Data and Vegetation Indices Derived from UAV Images

Agronomy ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2439
Author(s):  
Haixiao Ge ◽  
Fei Ma ◽  
Zhenwang Li ◽  
Changwen Du
Keyword(s):  
Grain Yield ◽  
Low Cost ◽  
Vegetation Indices ◽  
Model Performance ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Rice Breeding ◽  
Yield Estimation ◽  
Phenological Data ◽  
Significant Difference

The accurate estimation of grain yield in rice breeding is crucial for breeders to screen and select qualified cultivars. In this study, a low-cost unmanned aerial vehicle (UAV) platform mounted with an RGB camera was carried out to capture high-spatial resolution images of rice canopy in rice breeding. The random forest (RF) regression techniques were used to establish yield models by using (1) only color vegetation indices (VIs), (2) only phenological data, and (3) fusion of VIs and phenological data as inputs, respectively. Then, the performances of RF models were compared with the manual observation and CERES-Rice model. The results indicated that the RF model using VIs only performed poorly for estimating yield; the optimized RF model that combined the use of phenological data and color VIs performed much better, which demonstrated that the phenological data significantly improved the model performance. Furthermore, the yield estimation accuracy of 21 rice cultivars that were continuously planted over three years in the optimal RF model had no significant difference (p > 0.05) with that of the CERES-Rice model. These findings demonstrate that the RF model, by combining phenological data and color Vis, is a potential and cost-effective way to estimate yield in rice breeding.

scholarly journals Effects of Distinguishing Vegetation Types on the Estimates of Remotely Sensed Evapotranspiration in Arid Regions

2019 ◽  
Vol 11 (23) ◽  
pp. 2856 ◽  
Author(s):  
Tao Du ◽  
Li Wang ◽  
Guofu Yuan ◽  
Xiaomin Sun ◽  
Shusen Wang
Keyword(s):  
Water Resource Management ◽  
Regional Scale ◽  
Great Influence ◽  
Populus Euphratica ◽  
Remotely Sensed ◽  
Arid Ecosystems ◽  
Tamarix Ramosissima ◽  
Estimation Accuracy ◽  
Vegetation Types ◽  
The Difference

Accurate estimates of evapotranspiration (ET) in arid ecosystems are important for sustainable water resource management due to competing water demands between human and ecological environments. Several empirical remotely sensed ET models have been constructed and their potential for regional scale ET estimation in arid ecosystems has been demonstrated. Generally, these models were built using combined measured ET and corresponding remotely sensed and meteorological data from diverse sites. However, there are usually different vegetation types or mixed vegetation types in these sites, and little information is available on the estimation uncertainty of these models induced by combining different vegetation types from diverse sites. In this study, we employed the most popular one of these models and recalibrated it using datasets from two typical vegetation types (shrub Tamarix ramosissima and arbor Populus euphratica) in arid ecosystems of northwestern China. The recalibration was performed in the following two ways: using combined datasets from the two vegetation types, and using a single dataset from specific vegetation type. By comparing the performance of the two methods in ET estimation for Tamarix ramosissima and Populus euphratica, we investigated and compared the accuracy of ET estimation at the site scale and the difference in annual ET estimation at the regional scale. The results showed that the estimation accuracy of daily, monthly, and yearly ET was improved by distinguishing the vegetation types. The method based on the combined vegetation types had a great influence on the estimation accuracy of annual ET, which overestimated annual ET about 9.19% for Tamarix ramosissima and underestimated annual ET about 11.50% for Populus euphratica. Furthermore, substantial difference in annual ET estimation at regional scale was found between the two methods. The higher the vegetation coverage, the greater the difference in annual ET. Our results provide valuable information on evaluating the estimation accuracy of regional scale ET using empirical remotely sensed ET models for arid ecosystems.

scholarly journals Mapeamento Temático da Cobertura Vegetal na Microrregião do Sertão do São Francisco Alagoano, Utilizando Imagens TM LANDSAT 5 (Thematic Mapping of Plant Cover in Microregion Sertão of the San Francisco Alagoas, Images Using TM LANDSAT 5)

2013 ◽  
Vol 5 (5) ◽  
pp. 1121
Author(s):  
Heliofábio Barros Gomes ◽  
Rosiberto S. da S. Junior ◽  
Frederico Tejo De Paci ◽  
Danilo K. C. De Lima ◽  
Pedro H. P. De Castro ◽  
...  
Keyword(s):  
San Francisco ◽  
Precision Agriculture ◽  
Meteorological Data ◽  
Spectral Response ◽  
Plant Cover ◽  
Regional Scale ◽  
Vegetation Indices ◽  
Area Index ◽  
Thematic Mapping ◽  
Density And Biomass

 Atualmente o uso de técnicas de gerenciamento de fazendas utilizando ferramentas de agricultura de precisão vem se tornando cada vez mais comum. Uma dessas ferramentas é a obtenção de informações da resposta espectral dos alvos, cujas aplicações exigem a consideração de vários fatores como a textura do solo e o tipo de vegetação em estudo, pois os mesmos podem influenciar na interpretação dos dados. Os índices de vegetação têm sido muito utilizados no monitoramento de áreas vegetadas, na determinação e estimativa do índice de área foliar, biomassa e da radiação fotossinteticamente ativa. Os índices foram calculados através de etapas do Algoritmo SEBAL (Balanço de Energia da Superfície do Solo), mediante dados de imagens do TM – LANDSAT 5. Os resultados mostraram que ocorreu uma variação na cobertura vegetal da região em estudo, no sentido de alteração negativa da densidade e biomassa. A variação da densidade foi mais acentuada em 2008 do que em 2006 conforme resultados apresentados nos índices estudados. Os resultados obtidos demonstraram que o algoritmo SEBAL teve bom desempenho em escala regional na estimativa dos Índices de Vegetação, com potenciais para serem aplicados em áreas onde a disponibilidade de dados meteorológicos são limitantes.Palavras-chave: NDVI, SAVI, Sensoriamento Remoto. Thematic Mapping of Plant Cover in Microregion Sertão of the San Francisco Alagoas, Images Using TM LANDSAT 5 ABSTRACTCurrently the use of farm management techniques using tools of precision agriculture is becoming increasingly common. One such tool is to obtain information from the spectral response of the targets whose applications require consideration of several factors such as soil texture and type of vegetation in the study, as they may influence the interpretation of data. The vegetation indices have been used in the monitoring of vegetated areas, the determination and estimation of leaf area index, biomass and PAR. Rates were calculated using the algorithm steps of the SEBAL (Surface Energy Balance of Soil) upon image data from TM – LANDSAT 5. The results showed that there was a change in the vegetation of the study area in order to change negative density and biomass. The variation of density was larger in 2008 than in 2006 according to results presented in the indices studied. The results showed that the algorithm SEBAL performed well on a regional scale in the estimation of vegetation indices, with potential for application in areas where the availability of meteorological data are limited.Keywords: NDVI, SAVI, REMOTE SENSING. 

scholarly journals Integrating Spectral Information and Meteorological Data to Monitor Wheat Yellow Rust at a Regional Scale: A Case Study

2021 ◽  
Vol 13 (2) ◽  
pp. 278
Author(s):  
Qiong Zheng ◽  
Huichun Ye ◽  
Wenjiang Huang ◽  
Yingying Dong ◽  
Hao Jiang ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
Vegetation Index ◽  
Yellow Rust ◽  
Meteorological Data ◽  
Regional Scale ◽  
Vegetation Indices ◽  
Support Vector ◽  
Two Stage ◽  
Monitoring Method ◽  
Meteorological Features

Wheat yellow rust has a severe impact on wheat production and threatens food security in China; as such, an effective monitoring method is necessary at the regional scale. We propose a model for yellow rust monitoring based on Sentinel-2 multispectral images and a series of two-stage vegetation indices and meteorological data. Sensitive spectral vegetation indices (single- and two-stage indices) and meteorological features for wheat yellow rust discrimination were selected using the random forest method. Wheat yellow rust monitoring models were established using three different classification methods: linear discriminant analysis (LDA), support vector machine (SVM), and artificial neural network (ANN). The results show that models based on two-stage indices (i.e., those calculated using images from two different days) significantly outperform single-stage index models (i.e., those calculated using an image from a single day), the overall accuracy improved from 63.2% to 78.9%. The classification accuracies of models combining a vegetation index with meteorological feature are higher than those of pure vegetation index models. Among them, the model based on two-stage vegetation indices and meteorological features performs best, with a classification accuracy exceeding 73.7%. The SVM algorithm performed best for wheat yellow rust monitoring among the three algorithms; its classification accuracy (84.2%) was ~10.5% and 5.3% greater than those of LDA and ANN, respectively. Combined with crop growth and environmental information, our model has great potential for monitoring wheat yellow rust at a regional scale. Future work will focus on regional-scale monitoring and forecasting of crop disease.

A Machine Learning Approach to Upscale Net Ecosystem Exchange to a Regional Scale: Integration of Eddy Covariance, Remote Sensing and Reanalysis Data

2020 ◽  
Author(s):  
Oliver Reitz ◽  
Alexander Graf ◽  
Marius Schmidt ◽  
Gunnar Ketzler ◽  
Michael Leuchner
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
High Resolution ◽  
Eddy Covariance ◽  
Catchment Area ◽  
Cross Validation ◽  
Regional Scale ◽  
Vegetation Indices ◽  
Model Performance ◽  
Net Ecosystem Exchange

<p>Net Ecosystem Exchange (NEE) is an important factor regarding the impact of land use changes to the global carbon cycle and thus climate change. The Eddy Covariance technique is the most direct way of measuring CO<sub>2</sub> fluxes, however, it provides spatially discontinuous data from a sparse network of stations. Thus, generating high-resolution spatiotemporal products of carbon fluxes remains a major challenge. Machine Learning (ML) techniques are a promising approach to upscale this information to regional and global scales and can thereby help to produce better NEE datasets for earth-system modelling.</p><p>Our approach uses statistical relationships between NEE, vegetation indices and meteorological variables to train a Random Forest model with spatial feature selection to predict daily NEE values at 1 km spatial resolution for the Rur-catchment area (ca. 2400 km²) in western Germany. Data from twelve Eddy stations of different land use types of the TERENO Network Eifel/Lower Rhine Valley between 2010 and 2018 were used to train and test the ML model. Factors potentially affecting NEE such as vegetation indices (NDVI, EVI, LAI) extracted from MODIS products, incoming solar radiation from Heliosat (SARAH-2) and additional meteorological variables from COSMO REA6 reanalysis products served as independent variables, which were further evaluated in regard to their relative importance for NEE prediction.</p><p>A novel spatial cross-validation scheme has been applied and compared to a conventional random k-fold cross-validation. This is important for the assessment of the model performance regarding spatial predictions beyond the scope of training locations in contrast to mere data reproduction. Results indicate a lower model performance evaluated with spatial cross-validation and that conventional random cross-validation hence leads to an overoptimistic view of the prediction skills. Nonetheless, the ML approach displayed a feasible way to upscale carbon fluxes to a regional scale utilizing different datasets and produced high-resolution NEE-raster for an entire catchment area.</p>

scholarly journals Regression Models for Soil Water Storage Estimation Using the ESA CCI Satellite Soil Moisture Product: A Case Study in Northeast Portugal

Water ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 37
Author(s):  
Tomás de Figueiredo ◽  
Ana Caroline Royer ◽  
Felícia Fonseca ◽  
Fabiana Costa de Araújo Schütz ◽  
Zulimar Hernández
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Regression Models ◽  
Meteorological Data ◽  
Water Storage ◽  
Model Performance ◽  
Soil Water Balance ◽  
Soil Water Storage ◽  
The Relationship

The European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM) product provides soil moisture estimates from radar satellite data with a daily temporal resolution. Despite validation exercises with ground data that have been performed since the product’s launch, SM has not yet been consistently related to soil water storage, which is a key step for its application for prediction purposes. This study aimed to analyse the relationship between soil water storage (S), which was obtained from soil water balance computations with ground meteorological data, and soil moisture, which was obtained from radar data, as affected by soil water storage capacity (Smax). As a case study, a 14-year monthly series of soil water storage, produced via soil water balance computations using ground meteorological data from northeast Portugal and Smax from 25 mm to 150 mm, were matched with the corresponding monthly averaged SM product. Linear (I) and logistic (II) regression models relating S with SM were compared. Model performance (r2 in the 0.8–0.9 range) varied non-monotonically with Smax, with it being the highest at an Smax of 50 mm. The logistic model (II) performed better than the linear model (I) in the lower range of Smax. Improvements in model performance obtained with segregation of the data series in two subsets, representing soil water recharge and depletion phases throughout the year, outlined the hysteresis in the relationship between S and SM.

scholarly journals Influence of the aerosol solar extinction on photochemistry during the 2010 Russian wildfires episode

2015 ◽  
Vol 15 (19) ◽  
pp. 10983-10998 ◽  
Author(s):  
J. C. Péré ◽  
B. Bessagnet ◽  
V. Pont ◽  
M. Mallet ◽  
F. Minvielle
Keyword(s):  
Transport Model ◽  
Regional Scale ◽  
Model Performance ◽  
Surface Concentration ◽  
Near Surface ◽  
Aerosol Mass ◽  
Aerosol Mass Concentration ◽  
Air Quality Measurements ◽  
Photolysis Rates ◽  
Optical Module

Abstract. In this work, impact of aerosol solar extinction on the photochemistry over eastern Europe during the 2010 wildfires episode is discussed for the period from 5 to 12 August 2010, which coincides to the peak of fire activity. The methodology is based on an online coupling between the chemistry-transport model CHIMERE (extended by an aerosol optical module) and the radiative transfer code TUV. Results of simulations indicate an important influence of the aerosol solar extinction, in terms of intensity and spatial extent, with a reduction of the photolysis rates of NO2 and O3 up to 50 % (in daytime average) along the aerosol plume transport. At a regional scale, these changes in photolysis rates lead to a 3–15 % increase in the NO2 daytime concentration and to an ozone reduction near the surface of 1–12 %. The ozone reduction is shown to occur over the entire boundary layer, where aerosols are located. Also, the total aerosol mass concentration (PM10) is shown to be decreased by 1–2 %, on average during the studied period, caused by a reduced formation of secondary aerosols such as sulfates and secondary organics (4–10 %) when aerosol impact on photolysis rates is included. In terms of model performance, comparisons of simulations with air quality measurements at Moscow indicate that an explicit representation of aerosols interaction with photolysis rates tend to improve the estimation of the near-surface concentration of ozone and nitrogen dioxide as well as the formation of inorganic aerosol species such as ammonium, nitrates and sulfates.

Sign in / Sign up

Export Citation Format

Share Document