scholarly journals Assimilation of Remotely-Sensed LAI into WOFOST Model with the SUBPLEX Algorithm for Improving the Field-Scale Jujube Yield Forecasts

2019 ◽  
Vol 11 (16) ◽  
pp. 1945
Author(s):  
Tiecheng Bai ◽  
Shanggui Wang ◽  
Wenbo Meng ◽  
Nannan Zhang ◽  
Tao Wang ◽  
...  
Keyword(s):  
Developmental Stages ◽  
Vegetation Indices ◽  
Remotely Sensed ◽  
High Yield ◽  
Bias Error ◽  
Growth Stages ◽  
Landsat 8 ◽  
Statistical Regression ◽  
Field Scale ◽  
Remotely Sensed Data

In order to enhance the simulated accuracy of jujube yields at the field scale, this study attempted to employ SUBPLEX algorithm to assimilate remotely sensed leaf area indices (LAI) of four key growth stages into a calibrated World Food Studies (WOFOST) model, and compare the accuracy of assimilation with the usual ensemble Kalman filter (EnKF) assimilation. Statistical regression models of LAI and Landsat 8 vegetation indices at different developmental stages were established, showing a validated R2 of 0.770, 0.841, 0.779, and 0.812, and a validated RMSE of 0.061, 0.144, 0.180, and 0.170 m2 m−2 for emergence, fruit filling, white maturity, and red maturity periods. The results showed that both SUBPLEX and EnKF assimilations significantly improved yield estimation performance compared with un-assimilated simulation. The SUBPLEX (R2 = 0.78 and RMSE = 0.64 t ha−1) also showed slightly better yield prediction accuracy compared with EnKF assimilation (R2 = 0.73 and RMSE = 0.71 t ha−1), especially for high-yield and low-yield jujube orchards. SUBPLEX assimilation produced a relative bias error (RBE, %) that was more concentrated near zero, being lower than 10% in 80.1%, and lower than 20% in 96.1% for SUBPLEX, 72.4% and 96.7% for EnKF, respectively. The study provided a new assimilation scheme based on SUBPLEX algorithm to employ remotely sensed data and a crop growth model to improve the field-scale fruit crops yield estimates.

Modification of a groundwater prospect zone index using remotely sensed data and the analytic network process in the eastern part of Lesvos island, Greece.

2020 ◽  
Vol 11 (4) ◽  
pp. 126-143
Author(s):  
Terpsichori MITSI ◽  
◽  
Demetre ARGIALAS ◽  
Konstantinos VAMVOUKAKIS ◽  
◽  
...  
Keyword(s):  
Ground Water ◽  
Analytic Network Process ◽  
Remotely Sensed ◽  
Groundwater Potential ◽  
Landsat 8 ◽  
Groundwater Prospect ◽  
Remotely Sensed Data ◽  
Potential Index ◽  
Network Process ◽  
Sentinel 2

Because of climate change and overpopulation, the demand for water is increasing. Groundwater constitutes an alternative renewable source of aquifer, so the spatial distribution of ground water provides important information on its qualitative and quantitative status. This paper develops a methodology for delineating potential ground water zones using remotely sensed data and GIS. The developed methodology was based on the empirical index GPI (MGPI – Modified Groundwater Potential Index) and was applied to the eastern part of Lesvos Island, Greece. To evaluate the criteria used for the result, the Analytic Network Process (ANP) was applied to weight each parameter. The dataset used consists of satellite images derived from Sentinel 2 and Landsat 8, which were combined with vector and raster data, to create the necessary thematic layers. To validate the results, existing ground water zones from the Municipal Water Company of Lesvos were used.

scholarly journals Prediction of hookworm prevalence in southern India using environmental parameters derived from Landsat 8 remotely sensed data

2020 ◽  
Vol 50 (1) ◽  
pp. 47-54
Author(s):  
Alexandra V. Kulinkina ◽  
Rajiv Sarkar ◽  
Venkata R. Mohan ◽  
Yvonne Walz ◽  
Saravanakumar P. Kaliappan ◽  
...  
Keyword(s):  
Environmental Parameters ◽  
Remotely Sensed ◽  
Southern India ◽  
Landsat 8 ◽  
Remotely Sensed Data

scholarly journals Spectral characterization of forest plantations with Landsat 8/OLI images for forest planning and management

2017 ◽  
Vol 52 (11) ◽  
pp. 1072-1079 ◽  
Author(s):  
Elisiane Alba ◽  
Eliziane Pivotto Mello ◽  
Juliana Marchesan ◽  
Emanuel Araújo Silva ◽  
Juliana Tramontina ◽  
...  
Keyword(s):  
Near Infrared ◽  
Vegetation Indices ◽  
Pinus Elliottii ◽  
Growth Stages ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Infrared Band ◽  
Near Infrared Reflectance ◽  
Planning And Management ◽  
Forest Planning And Management

Abstract: The objective of this work was to evaluate the use of Landsat 8/OLI images to differentiate the age and estimate the total volume of Pinus elliottii, in order to determine the applicability of these data in the planning and management of forest activity. Fifty-three sampling units were installed, and dendrometric variables of 9-and-10-year-old P. elliottii commercial stands were measured. The digital numbers of the image were converted into surface reflectance and, subsequently, vegetation indices were determined. Red and near-infrared reflectance values were used to differentiate the ages of the stands. Regression analysis of the spectral variables was used to estimate the total volume. Increase in age caused an addition in reflectance in the near-infrared band and a decrease in the red band. The general equation for estimating the total volume for P.elliottii had an R2adj of 0.67 with a Syx of 31.46 m3 ha-1. Therefore, the spectral data with medium spatial resolution from the Landsat 8/OLI satellite can be used to distinguish the growth stages of the stands and can, thus, be used in the planning and proper management of forest activity on a spatial and temporal scale.

scholarly journals Time-series Multi-spectral Imaging in Soybean for Improving Biomass and Genomic Prediction Accuracy

2021 ◽  
Author(s):  
Kengo Sakurai ◽  
Yusuke Toda ◽  
Hiromi Kajiya-Kanegae ◽  
Yoshihiro Ohmori ◽  
Yuji Yamasaki ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Field Experiments ◽  
Developmental Stages ◽  
Early Stage ◽  
Vegetation Indices ◽  
Optimal Timing ◽  
Growth Stages ◽  
Ms Imaging ◽  
Irrigation Levels

Multi-spectral (MS) imaging enables the measurement of characteristics important for increasing the prediction accuracy of genotypic and phenotypic values for yield-related traits. In this study, we evaluated the potential application of temporal MS imaging for the prediction of above-ground biomass (AGB) and determined which developmental stages should be used for accurate prediction in soybean. Field experiments with 198 accessions of soybean were conducted with four different irrigation levels. Five vegetation indices (VIs) were calculated using MS images from soybean canopies from early to late growth stages. To predict the genotypic values of AGB, VIs at the different growth stages were used as secondary traits in a multi-trait genomic prediction. The accuracy of the prediction model increased starting at an early stage of growth (31 days after sowing). To predict phenotypic values of AGB, we employed multi-kernel genomic prediction. Consequently, the prediction accuracy of phenotypic values reached a maximum at a relatively early growth stage (38 days after sowing). Hence, the optimal timing for MS imaging may depend on the irrigation levels.

scholarly journals Monitoring Crop Evapotranspiration and Crop Coefficients over an Almond and Pistachio Orchard Throughout Remote Sensing

2018 ◽  
Author(s):  
Joaquim Bellvert ◽  
Karine Adeline ◽  
Shahar Baram ◽  
Lars Pierce ◽  
Blake Sanden ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Remotely Sensed ◽  
Actual Evapotranspiration ◽  
Water Evaporation ◽  
Landsat 8 ◽  
Crop Evapotranspiration ◽  
Crop Water ◽  
Field Scale ◽  
Crop Coefficients

In California, water is a perennial concern. As competition for water resources increases due to growth in population, California’s tree nut farmers are committed to improving the efficiency of water used for food production. There is an imminent need to have reliable methods that provide information about the temporal and spatial variability of crop water requirements, which allow farmers to make irrigation decisions at field scale. This study focuses on estimating the actual evapotranspiration and crop coefficients of an almond and pistachio orchard located in Central Valley (California) during an entire growing season by combining a simple crop evapotranspiration model with remote sensing data. A dataset of the vegetation index NDVI derived from Landsat-8 was used to facilitate the estimation of the basal crop coefficient (Kcb), or potential crop water use. The soil water evaporation coefficient (Ke) was measured from microlysimeters. The water stress coefficient (Ks) was derived from airborne remotely sensed canopy thermal-based methods, using seasonal regressions between the crop water stress index (CWSI) and stem water potential (Ystem). These regressions were statistically-significant for both crops, indicating clear seasonal differences in pistachios, but not in almonds. In almonds, the estimated maximum Kcb values ranged between 1.05 to 0.90, while for pistachios, it ranged between 0.89 to 0.80. The model indicated a difference of 97 mm in transpiration over the season between both crops. Soil evaporation accounted for an average of 16% and 13% of the total actual evapotranspiration for almonds and pistachios, respectively. Verification of the model-based daily crop evapotranspiration estimates was done using eddy-covariance and surface renewal data collected in the same orchards, yielding an r2 >= 0.7 and average root mean square errors (RMSE) of 0.74 and 0.91 mm day-1 for almond and pistachio, respectively. It is concluded that the combination of crop evapotranspiration models with remotely-sensed data is helpful for upscaling irrigation information from plant to field scale and thus may be used by farmers for making day-to-day irrigation management decisions.

scholarly journals Automatic Detection of Impervious Surfaces from Remotely Sensed Data Using Deep Learning

2021 ◽  
Vol 13 (16) ◽  
pp. 3166
Author(s):  
Jash R. Parekh ◽  
Ate Poortinga ◽  
Biplov Bhandari ◽  
Timothy Mayer ◽  
David Saah ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Large Scale ◽  
Mean Squared Error ◽  
Remotely Sensed ◽  
Landsat 8 ◽  
Impervious Surfaces ◽  
Remotely Sensed Data ◽  
Statistical Indices ◽  
Deep Learning Neural Network

The large scale quantification of impervious surfaces provides valuable information for urban planning and socioeconomic development. Remote sensing and GIS techniques provide spatial and temporal information of land surfaces and are widely used for modeling impervious surfaces. Traditionally, these surfaces are predicted by computing statistical indices derived from different bands available in remotely sensed data, such as the Landsat and Sentinel series. More recently, researchers have explored classification and regression techniques to model impervious surfaces. However, these modeling efforts are limited due to lack of labeled data for training and evaluation. This in turn requires significant effort for manual labeling of data and visual interpretation of results. In this paper, we train deep learning neural networks using TensorFlow to predict impervious surfaces from Landsat 8 images. We used OpenStreetMap (OSM), a crowd-sourced map of the world with manually interpreted impervious surfaces such as roads and buildings, to programmatically generate large amounts of training and evaluation data, thus overcoming the need for manual labeling. We conducted extensive experimentation to compare the performance of different deep learning neural network architectures, optimization methods, and the set of features used to train the networks. The four model configurations labeled U-Net_SGD_Bands, U-Net_Adam_Bands, U-Net_Adam_Bands+SI, and VGG-19_Adam_Bands+SI resulted in a root mean squared error (RMSE) of 0.1582, 0.1358, 0.1375, and 0.1582 and an accuracy of 90.87%, 92.28%, 92.46%, and 90.11%, respectively, on the test set. The U-Net_Adam_Bands+SI Model, similar to the others mentioned above, is a deep learning neural network that combines Landsat 8 bands with statistical indices. This model performs the best among all four on statistical accuracy and produces qualitatively sharper and brighter predictions of impervious surfaces as compared to the other models.

scholarly journals Spark Sensing: A Cloud Computing Framework to Unfold Processing Efficiencies for Large and Multiscale Remotely Sensed Data, with Examples on Landsat 8 and MODIS Data

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Hai Lan ◽  
Xinshi Zheng ◽  
Paul M. Torrens
Keyword(s):  
Time Series ◽  
Remotely Sensed ◽  
Landsat 8 ◽  
Remotely Sensed Data ◽  
Environmental Sciences ◽  
Modis Data ◽  
The Earth ◽  
Data Provision ◽  
Using Data ◽  
Local Cloud

Inquiry using data from remote Earth-observing platforms often confronts a straightforward but particularly thorny problem: huge amounts of data, in ever-replenishing supplies, are available to support inquiry, but scientists’ agility in converting data into actionable information often struggles to keep pace with rapidly incoming streams of data that amass in expanding archival silos. Abstraction of those data is a convenient response, and many studies informed purely by remotely sensed data are by necessity limited to a small study area with a relatively few scenes of imagery, or they rely on larger mosaics of images at low resolution. As a result, it is often challenging to thread explanations across scales from the local to the global, even though doing so is often critical to the science under pursuit. Here, a solution is proposed, by exploiting Apache Spark, to implement parallel, in-memory image processing with ability to rapidly classify large volumes of multiscale remotely sensed images and to perform necessary analysis to detect changes on the time series. It shows that processing on three different scales of Landsat 8 data (up to ~107.4 GB, five-scene, time series image sets) can be accomplished in 1018 seconds on local cloud environment. Applying the same framework with slight parameter adjustments, it processed same coverage MODIS data in 54 seconds on commercial cloud platform. Theoretically, the proposed scheme can handle all forms of remote sensing imagery commonly used in the Earth and environmental sciences, requiring only minor adjustments in parameterization of the computing jobs to adjust to the data. The authors suggest that the “Spark sensing” approach could provide the flexibility, extensibility, and accessibility necessary to keep inquiry in the Earth and environmental sciences at pace with developments in data provision.

STAND AGE MODEL FOR MAPPING SPATIAL DISTRIBUTION OF RUBBER TREE USING REMOTELY SENSED DATA IN KEDAH, MALAYSIA

2016 ◽  
Vol 78 (5) ◽  
Author(s):  
Iqbal Putut Ash Shidiq ◽  
Mohd Hasmadi Ismail
Keyword(s):  
Spatial Distribution ◽  
Vegetation Indices ◽  
Rubber Tree ◽  
Remote Sensing Data ◽  
Stand Age ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Remotely Sensed Data ◽  
Age Model ◽  
The Relationship

This study attempt to develop stand age model of rubber tree by using remote sensing data. Rubber tree is one of the important biomass that has been considered as the essential part in global warming reduction plan due to its beneficial carbon sequestration capability. The spatial distribution of rubber tree based on different age was most highlighted as the focus of this study. Felda Lubuk Merbau in state of Kedah has been selected as a study area and Landsat 8 OLI-TIRS data was utilized to map rubber tree and differentiate them based on age group. The relationship between vegetation indices namely NDVI, SAVI and EVI to different age stages of rubber tree were discussed.

scholarly journals Estimating Rice Agronomic Traits Using Drone-Collected Multispectral Imagery

2019 ◽  
Vol 11 (5) ◽  
pp. 545 ◽  
Author(s):  
Dimitris Stavrakoudis ◽  
Dimitrios Katsantonis ◽  
Kalliopi Kadoglidou ◽  
Argyris Kalaitzidis ◽  
Ioannis Gitas
Keyword(s):  
Near Infrared ◽  
Agronomic Traits ◽  
Oryza Sativa L ◽  
N Uptake ◽  
Vegetation Indices ◽  
Growth Stages ◽  
Linear Regression Models ◽  
Remotely Sensed Data ◽  
Cost Efficient ◽  
N Management

The knowledge of rice nitrogen (N) requirements and uptake capacity are fundamental for the development of improved N management. This paper presents empirical models for predicting agronomic traits that are relevant to yield and N requirements of rice (Oryza sativa L.) through remotely sensed data. Multiple linear regression models were constructed at key growth stages (at tillering and at booting), using as input reflectance values and vegetation indices obtained from a compact multispectral sensor (green, red, red-edge, and near-infrared channels) onboard an unmanned aerial vehicle (UAV). The models were constructed using field data and images from two consecutive years in a number of experimental rice plots in Greece (Thessaloniki Regional Unit), by applying four different N treatments (C0: 0 N kg∙ha−1, C1: 80 N kg∙ha−1, C2: 160 N kg∙ha−1, and C4: 320 N kg∙ha−1). Models for estimating the current crop status (e.g., N uptake at the time of image acquisition) and predicting the future one (e.g., N uptake of grains at maturity) were developed and evaluated. At the tillering stage, high accuracies (R2 ≥ 0.8) were achieved for N uptake and biomass. At the booting stage, similarly high accuracies were achieved for yield, N concentration, N uptake, biomass, and plant height, using inputs from either two or three images. The results of the present study can be useful for providing N recommendations for the two top-dressing fertilizations in rice cultivation, through a cost-efficient workflow.

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