scholarly journals Consistent Long-Term Monthly Coastal Wetland Vegetation Monitoring Using a Virtual Satellite Constellation

2021 ◽  
Vol 13 (3) ◽  
pp. 438
Author(s):  
Subrina Tahsin ◽  
Stephen C. Medeiros ◽  
Arvind Singh
Keyword(s):  
Vegetation Index ◽  
Coastal Wetland ◽  
Thermal Emission ◽  
Mean Squared Error ◽  
Wetland Vegetation ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Vegetation Monitoring ◽  
Time Space

Long-term monthly coastal wetland vegetation monitoring is the key to quantifying the effects of natural and anthropogenic events, such as severe storms, as well as assessing restoration efforts. Remote sensing data products such as Normalized Difference Vegetation Index (NDVI), alongside emerging data analysis techniques, have enabled broader investigations into their dynamics at monthly to decadal time scales. However, NDVI data suffer from cloud contamination making periods within the time series sparse and often unusable during meteorologically active seasons. This paper proposes a virtual constellation for NDVI consisting of the red and near-infrared bands of Landsat 8 Operational Land Imager, Sentinel-2A Multi-Spectral Instrument, and Advanced Spaceborne Thermal Emission and Reflection Radiometer. The virtual constellation uses time-space-spectrum relationships from 2014 to 2018 and a random forest to produce synthetic NDVI imagery rectified to Landsat 8 format. Over the sample coverage area near Apalachicola, Florida, USA, the synthetic NDVI showed good visual coherence with observed Landsat 8 NDVI. Comparisons between the synthetic and observed NDVI showed Root Mean Squared Error and Coefficient of Determination (R2) values of 0.0020 sr−1 and 0.88, respectively. The results suggest that the virtual constellation was able to mitigate NDVI data loss due to clouds and may have the potential to do the same for other data. The ability to participate in a virtual constellation for a useful end product such as NDVI adds value to existing satellite missions and provides economic justification for future projects.

scholarly journals Evaluation of the Consistency of Simultaneously Acquired Sentinel-2 and Landsat 8 Imagery on Plastic Covered Greenhouses

2020 ◽  
Vol 12 (12) ◽  
pp. 2015 ◽  
Author(s):  
Manuel Ángel Aguilar ◽  
Rafael Jiménez-Lao ◽  
Abderrahim Nemmaoui ◽  
Fernando José Aguilar ◽  
Dilek Koc-San ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Remote Sensing Techniques ◽  
Image Pairs ◽  
Study Sites ◽  
High Level ◽  
Sentinel 2

Remote sensing techniques based on medium resolution satellite imagery are being widely applied for mapping plastic covered greenhouses (PCG). This article aims at testing the spectral consistency of surface reflectance values of Sentinel-2 MSI (S2 L2A) and Landsat 8 OLI (L8 L2 and the pansharpened and atmospherically corrected product from L1T product; L8 PANSH) data in PCG areas located in Spain, Morocco, Italy and Turkey. The six corresponding bands of S2 and L8, together with the normalized difference vegetation index (NDVI), were generated through an OBIA approach for each PCG study site. The coefficient of determination (r2) and the root mean square error (RMSE) were computed in sixteen cloud-free simultaneously acquired image pairs from the four study sites to evaluate the coherence between the two sensors. It was found that the S2 and L8 correlation (r2 > 0.840, RMSE < 9.917%) was quite good in most bands and NDVI. However, the correlation of the two sensors fluctuated between study sites, showing occasional sun glint effects on PCG roofs related to the sensor orbit and sun position. Moreover, higher surface reflectance discrepancies between L8 L2 and L8 PANSH data, mainly in the visible bands, were always observed in areas with high-level aerosol values derived from the aerosol quality band included in the L8 L2 product (SR aerosol). In this way, the consistency between L8 PANSH and S2 L2A was improved mainly in high-level aerosol areas according to the SR aerosol band.

scholarly journals Spatio-temporal variability of biophysical parameters of irrigated maize using orbital remote sensing

2021 ◽  
Vol 42 (4) ◽  
pp. 2181-2202
Author(s):  
Taiara Souza Costa ◽  
◽  
Robson Argolo dos Santos ◽  
Rosângela Leal Santos ◽  
Roberto Filgueiras ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Crop Coefficient ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Crop Evapotranspiration ◽  
Biophysical Parameters ◽  
Planting Dates ◽  
Maize Crop ◽  
Spatio Temporal

This study proposes to estimate the actual crop evapotranspiration, using the SAFER model, as well as calculate the crop coefficient (Kc) as a function of the normalized difference vegetation index (NDVI) and determine the biomass of an irrigated maize crop using images from the Operational Land Imager (OLI) and Thermal Infrared (TIRS) sensors of the Landsat-8 satellite. Pivots 21 to 26 of a commercial farm located in the municipalities of Bom Jesus da Lapa and Serra do Ramalho, west of Bahia State, Brazil, were selected. Sowing dates for each pivot were arranged as North and South or East and West, with cultivation starting firstly in one of the orientations and subsequently in the other. The relationship between NDVI and the Kc values obtained in the FAO-56 report (KcFAO) revealed a high coefficient of determination (R2 = 0.7921), showing that the variance of KcFAO can be explained by NDVI in the maize crop. Considering the center pivots with different planting dates, the crop evapotranspiration (ETc) pixel values ranged from 0.0 to 6.0 mm d-1 during the phenological cycle. The highest values were found at 199 days of the year (DOY), corresponding to around 100 days after sowing (DAS). The lowest BIO values occur at 135 DOY, at around 20 DAS. There is a relationship between ETc and BIO, where the DOY with the highest BIO are equivalent to the days with the highest ETc values. In addition to this relationship, BIO is strongly influenced by soil water availability.

scholarly journals Fusion of MODIS and Landsat-Like Images for Daily High Spatial Resolution NDVI

2020 ◽  
Vol 12 (8) ◽  
pp. 1297
Author(s):  
Roberto Filgueiras ◽  
Everardo Chartuni Mantovani ◽  
Elpídio Inácio Fernandes-Filho ◽  
Fernando França da Cunha ◽  
Daniel Althoff ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Regression Models ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
High Spatial Resolution ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Landsat 8 ◽  
Modis Ndvi

One of the obstacles in monitoring agricultural crops is the difficulty in understanding and mapping rapid changes of these crops. With the purpose of addressing this issue, this study aimed to model and fuse the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) using Landsat-like images to achieve daily high spatial resolution NDVI. The study was performed for the period of 2017 on a commercial farm of irrigated maize-soybean rotation in the western region of the state of Bahia, Brazil. To achieve the objective, the following procedures were performed: (i) Landsat-like images were upscaled to match the Landsat-8 spatial resolution (30 m); (ii) the reflectance of Landsat-like images was intercalibrated using the Landsat-8 as a reference; (iii) Landsat-like reflectance images were upscaled to match the MODIS sensor spatial resolution (250 m); (iv) regression models were trained daily to model MODIS NDVI using the upscaled Landsat-like reflectance images (250 m) of the closest day as the input; and (v) the intercalibrated version of the Landsat-like images (30 m) used in the previous step was used as the input for the trained model, resulting in a downscaled MODIS NDVI (30 m). To determine the best fitting model, we used the following statistical metrics: coefficient of determination (r2), root mean square error (RMSE), Nash–Sutcliffe efficiency index (NSE), mean bias error (MBE), and mean absolute error (MAE). Among the assessed regression models, the Cubist algorithm was sensitive to changes in agriculture and performed best in modeling of the Landsat-like MODIS NDVI. The results obtained in the present research are promising and can enable the monitoring of dynamic phenomena with images available free of charge, changing the way in which decisions are made using satellite images.

scholarly journals First Vegetation Optical Depth Mapping from Sentinel-1 C-band SAR Data over Crop Fields

2019 ◽  
Vol 11 (23) ◽  
pp. 2769 ◽  
Author(s):  
Mohammad El Hajj ◽  
Nicolas Baghdadi ◽  
Jean-Pierre Wigneron ◽  
Mehrez Zribi ◽  
Clément Albergel ◽  
...  
Keyword(s):  
Water Content ◽  
Optical Depth ◽  
Water Loss ◽  
Vegetation Index ◽  
Temporal Dynamics ◽  
Irrigation Management ◽  
Coefficient Of Determination ◽  
Vegetation Monitoring ◽  
Growth Cycles ◽  
Sar Data

Monitoring crop status at plot scale in agricultural areas is essential for crop and irrigation management and yield optimization. The Vegetation Optical Depth (VOD) of canopy is directly related to the canopy water content, and thus, it represents an effective tool for crop health monitoring. Currently, VOD is provided at low spatial resolution which makes these estimations useless for vegetation monitoring at plot scale. Therefore, the aim of this study is to provide the first approach to estimate VOD at plot scale for non-irrigated plots from C-band Sentinel-1 (S1) Synthetic Aperture Radar (SAR) data. The proposed approach was tested on a study site of 50 km × 50 km located in Catalonia, Spain. VOD estimates were provided for two crop growth cycles of non-irrigated crop types (barley, fallow, oat, wheat, and rapeseed). The relevance of VOD estimates was investigated for both growth cycles using temporal profiles of the Normalized Difference Vegetation Index (NDVI). It is shown that the temporal dynamics of VOD values computed from VV polarization fits that of NDVI with a medium to good coefficient of determination (R2 ranging from 0.39 to 0.61 for barley, fallow, oat, and wheat respectively). However, during the beginning of the senescence period in both cycles (mainly in May for winter crops), VOD decreases with the decrease in Vegetation Water Content (VWC) while NDVI keeps increasing as photosynthetic activity continues developing. This illustrates the importance of VOD in crop water loss (stress and/or transpiration) monitoring. The potential of VOD to spot water loss in vegetation is also demonstrated as the evening (18h00) VOD values are lower than those of morning (06h00) due to high daytime temperature that reduces water content in vegetation. Finally, it is shown that VOD values computed from VH polarization are not correlated with NDVI.

scholarly journals Satellite-Derived Estimation of Grassland Aboveground Biomass in the Three-River Headwaters Region of China during 1982–2018

2021 ◽  
Vol 13 (15) ◽  
pp. 2993
Author(s):  
Ruiyang Yu ◽  
Yunjun Yao ◽  
Qiao Wang ◽  
Huawei Wan ◽  
Zijing Xie ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Aboveground Biomass ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Growth Period ◽  
Spatiotemporal Variation ◽  
Coefficient Of Determination ◽  
Gradient Boosting ◽  
Ancillary Data

The long-term estimation of grassland aboveground biomass (AGB) is important for grassland resource management in the Three-River Headwaters Region (TRHR) of China. Due to the lack of reliable grassland AGB datasets since the 1980s, the long-term spatiotemporal variation in grassland AGB in the TRHR remains unclear. In this study, we estimated AGB in the grassland of 209,897 km2 using advanced very high resolution radiometer (AVHRR), MODerate-resolution Imaging Spectroradiometer (MODIS), meteorological, ancillary data during 1982–2018, and 75 AGB ground observations in the growth period of 2009 in the TRHR. To enhance the spatial representativeness of ground observations, we firstly upscaled the grassland AGB using a gradient boosting regression tree (GBRT) model from ground observations to a 1 km spatial resolution via MODIS normalized difference vegetation index (NDVI), meteorological and ancillary data, and the model produced validation results with a coefficient of determination (R2) equal to 0.76, a relative mean square error (RMSE) equal to 88.8 g C m−2, and a bias equal to −1.6 g C m−2 between the ground-observed and MODIS-derived upscaled AGB. Then, we upscaled grassland AGB using the same model from a 1 km to 5 km spatial resolution via AVHRR NDVI and the same data as previously mentioned with the validation accuracy (R2 = 0.74, RMSE = 57.8 g C m−2, and bias = −0.1 g C m−2) between the MODIS-derived reference and AVHRR-derived upscaled AGB. The annual trend of grassland AGB in the TRHR increased by 0.37 g C m−2 (p < 0.05) on average per year during 1982–2018, which was mainly caused by vegetation greening and increased precipitation. This study provided reliable long-term (1982–2018) grassland AGB datasets to monitor the spatiotemporal variation in grassland AGB in the TRHR.

scholarly journals A Classification of Tidal Flat Wetland Vegetation Combining Phenological Features with Google Earth Engine

2021 ◽  
Vol 13 (3) ◽  
pp. 443
Author(s):  
Nan Wu ◽  
Runhe Shi ◽  
Wei Zhuo ◽  
Chao Zhang ◽  
Bingchan Zhou ◽  
...  
Keyword(s):  
Tidal Flat ◽  
Vegetation Index ◽  
Google Earth ◽  
Wetland Vegetation ◽  
Species Level ◽  
Landsat 8 ◽  
Harmonic Model ◽  
Temporal Features ◽  
Google Earth Engine ◽  
Time Series Images

The composition and distribution of wetland vegetation is critical for ecosystem diversity and sustainable development. However, tidal flat wetland environments are complex, and obtaining effective satellite imagery is challenging due to the high cloud coverage. Moreover, it is difficult to acquire phenological feature data and extract species-level wetland vegetation information by using only spectral data or individual images. To solve these limitations, statistical features, temporal features, and phenological features of multiple Landsat 8 time-series images obtained via the Google Earth Engine (GEE) platform were compared to extract species-level wetland vegetation information from Chongming Island, China. The results indicated that (1) a harmonic model obtained the phenological characteristics of wetland vegetation better than the raw vegetation index (VI) and the Savitzky–Golay (SG) smoothing method; (2) classification based on the combination of the three features provided the highest overall accuracy (85.54%), and the phenological features (represented by the amplitude and phase of the harmonic model) had the greatest impact on the classification; and (3) the classification result from the senescence period was more accurate than that from the green period, but the annual mapping result on all seasons was the most accurate. The method described in this study can be applied to overcome the impacts of the complex environment in tidal flat wetlands and to effectively classify wetland vegetation species using GEE. This study could be used as a reference for the analysis of the phenological features of other areas or vegetation types.

scholarly journals Combined Use of Multi-Temporal Landsat-8 and Sentinel-2 Images for Wheat Yield Estimates at the Intra-Plot Spatial Scale

Agronomy ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 327 ◽  
Author(s):  
Remy Fieuzal ◽  
Vincent Bustillo ◽  
David Collado ◽  
Gerard Dedieu
Keyword(s):  
Relative Error ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Wheat Yield ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Optical Images ◽  
Multi Temporal ◽  
Input Variables ◽  
Sentinel 2

The objective of this study is to address the capabilities of multi-temporal optical images to estimate the fine-scale yield variability of wheat, over a study site located in southwestern France. The methodology is based on the Landsat-8 and Sentinel-2 satellite images acquired after the sowing and before the harvest of the crop throughout four successive agricultural seasons, the reflectance constituting the input variables of a statistical algorithm (random forest). The best performances are obtained when the Normalized Difference Vegetation Index (NDVI) is combined with the yield maps collected during the crop rotation, the agricultural season 2014 showing the lower level of performances with a coefficient of determination (R2) of 0.44 and a root mean square error (RMSE) of 8.13 quintals by hectare (q.h−1) (corresponding to a relative error of 12.9%), the three other years being associated with values of R2 close or upper to 0.60 and RMSE lower than 7 q.h−1 (corresponding to a relative error inferior to 11.3%). Moreover, the proposed approach allows estimating the crop yield throughout the agricultural season, by using the successive images acquired from the sowing to the harvest. In such cases, early and accurate yield estimates are obtained three months before the end of the crop cycle. At this phenological stage, only a slight decrease in performance is observed compared to the statistic obtained just before the harvest.

scholarly journals Application of geo-informatics engineering to estimate above-ground carbon sequestration

2018 ◽  
Vol 38 (3) ◽  
pp. 303-308
Author(s):  
Teerawong Laosuwan ◽  
Yannawut Uttaruk ◽  
Tanutdech Rotjanakusol ◽  
Kusuma Arsasana
Keyword(s):  
Carbon Sequestration ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Regression Equations ◽  
Landsat 8 Oli ◽  
Green Vegetation ◽  
Simple Ratio ◽  
The Relationship

This research aims to estimate above-ground carbon sequestration of orchards by using the data collected from Landsat 8 OLI. Regression equations are applied to study the relationship between the amount of above-ground carbon sequestration and vegetation indices from Landsat 8 OLI, in which the data was collected in 2015 in 3 methods: 1) Difference Vegetation Index (DVI), 2) Green Vegetation Index (GVI), and 3) Simple Ratio (SR). The results are as follows: 1) By DVI method, it results in the equation y = 0.3184e0.0482x and the coefficient of determination R² = 0.8457. The amount of the above-ground sequestration calcula-tion's result is 213.176 tons per rai. 2) Using the GVI method, it results in the equation y = 0.2619e0.0489x and the coefficient of determination R²=0.8763. The amount of the above-ground sequestration calculation's result is 220.510 tons per rai. 3) Using the SR method, it results in the equation y = 0.8900e0.0469x and the coefficient of determination R² = 0.7748. The amount of the above-ground sequestration calculation's result is 234.229 tons per rai.

scholarly journals SENSORIAMENTO REMOTO APLICADO A MODELAGEM DE PRODUTIVIDADE DA CULTURA DA CANA-DE-AÇÚCAR

2019 ◽  
Vol 34 (2) ◽  
pp. 263-270
Author(s):  
Victor Costa Leda ◽  
Aline Kuramoto Golçalves ◽  
Natalia da Silva Lima
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Sao Paulo ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
São Paulo ◽  
Vegetation Indexes ◽  
Production Area ◽  
Remote Sensing Techniques ◽  
Sugarcane Productivity

SENSORIAMENTO REMOTO APLICADO A MODELAGEM DE PRODUTIVIDADE DA CULTURA DA CANA-DE-AÇÚCAR   VICTOR COSTA LEDA1, ALINE KURAMOTO GOLÇALVES2, NATALIA DA SILVA LIMA3   1 Departamento de Solos e Recursos Ambientais, Universidade Paulista “Júlio de Mesquita Filho” – Unesp, Fazenda Experimental Lageado, Avenida Universitária, nº 3780, Altos do Paraíso, CEP 18610-034, Botucatu, São Paulo, Brasil, [email protected]. 2 Departamento de Solos e Recursos Ambientais, Universidade Paulista “Júlio de Mesquita Filho” – Unesp, Fazenda Experimental Lageado, Avenida Universitária, nº 3780, Altos do Paraíso, CEP 18610-034, Botucatu, São Paulo, Brasil, [email protected]. 3 Departamento de Solos e Recursos Ambientais, Universidade Paulista “Júlio de Mesquita Filho” – Unesp, Fazenda Experimental Lageado, Avenida Universitária, nº 3780, Altos do Paraíso, CEP 18610-034, Botucatu, São Paulo, Brasil, [email protected].   RESUMO: O trabalho objetivou modelar as correlações de produtividade da cana-de-açúcar com índices de vegetação obtidos por meio de análise de imagens orbitais. Para análise, foram elaborados modelos matemáticos que expliquem a produtividade da cana-de-açúcar por meio das técnicas de geoprocessamento e sensoriamento remoto. O experimento foi realizado na área de produção comercial da Agrícola Rio Claro, parceira do grupo Zilor, que está localizada nos municípios de Lençóis Paulista e Pratânia, SP. A área ocupa aproximadamente 6000 ha, com altimetrias variando entre 600 e 700 m. Foi constatado que as modelagens foram satisfatórias, variando o coeficiente de determinação entre 0,15 a 0,97, sendo que, em períodos de colheita com elevados coeficientes de determinação, podem geralmente ser encontradas áreas de forma aglomerada, o que sugere uma menor incidência de variáveis. Enquanto áreas que apresentaram coeficientes de determinação baixos, podem ser explicadas devido a fatores como, dispersão dos talhões na área, classes de solo, precipitação e variedades da cultura, provavelmente distintos.   Palavras-chaves: índices de vegetação, Landsat 8, regressão linear múltipla.   REMOTE SENSING FOR THE SUGARCANE PRODUCTIVITY MODELING   ABSTRACT: The aim of this study was to model the sugarcane productivity correlations with vegetation indexes obtained through orbital image analysis. From the analysis was elaborated      mathematical models to explain sugarcane productivity through geoprocessing and remote sensing techniques. The experiment was carried out in the commercial production area of Agrícola Rio Claro, a partner of the Zilor group, located in the municipalities of Lençóis Paulista and Pratânia, SP, with approximately 6,000 hectares, with altimetry varying between 600 and 700 meters. It was verified that the modeling was satisfactory, varying the coefficient of determination between 0,15 and 0,97. Once      in periods with high determination coefficients, areas of agglomerated form can usually be found, which suggests a lower incidence of variables. While, in periods with low determination coefficients, can be explain due to listed factors that occurred as dispersion of the stands in the area, classes of soil, precipitation and probably different varieties of the crop.   Keywords: vegetation index, landsat8, multiple linear regression.

scholarly journals Modelling Reservoir Turbidity Using Landsat 8 Satellite Imagery by Gene Expression Programming

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1479 ◽  
Author(s):  
Liu ◽  
Wang
Keyword(s):  
Gene Expression ◽  
Performance Evaluation ◽  
Satellite Imagery ◽  
Mean Squared Error ◽  
Accuracy Assessment ◽  
Gene Expression Programming ◽  
Model Performance ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Model Performance Evaluation

This study aimed to develop a reliable turbidity model to assess reservoir turbidity based on Landsat-8 satellite imagery. Models were established by multiple linear regression (MLR) and gene-expression programming (GEP) algorithms. Totally 55 and 18 measured turbidity data from Tseng-Wen and Nan-Hwa reservoir paired and screened with satellite imagery. Finally, MLR and GEP were applied to simulated 13 turbid water data for critical turbidity assessment. The coefficient of determination (R2), root mean squared error (RMSE), and relative RMSE (R-RMSE) calculated for model performance evaluation. The result show that, in model development, MLR and GEP shows a similar consequent. However, in model testing, the R2, RMSE, and R-RMSE of MLR and GEP are 0.7277 and 0.8278, 0.7248 NTU and 0.5815 NTU, 22.26% and 17.86%, respectively. Accuracy assessment result shows that GEP is more reasonable than MLR, even in critical turbidity situation, GEP is more convincible. In the model performance evaluation, MLR and GEP are normal and good level, in critical turbidity condition, GEP even belongs to outstanding level. These results exhibit GEP denotes rationality and with relatively good applicability for turbidity simulation. From this study, one can conclude that GEP is suitable for turbidity modeling and is accurate enough for reservoir turbidity estimation.

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