Search

A remote sensing (RS) platform consisting of a remote-controlled aerial vehicle (RAV) can be used to monitor crop, environmental conditions, and productivity in agricultural areas. However, the current methods for the calibration of RAV-acquired images are cumbersome. Thus, a calibration method must be incorporated into RAV RS systems for practical and advanced applications. Here, we aimed to develop a standalone RAV RS-based calibration system without the need for calibration tarpaulins (tarps) by quantifying the sensor responses of a multispectral camera, which varies with light intensities. To develop the standalone RAV-based RS calibration system, we used a quadcopter with four propellers, with a rotor-to-rotor length of 46 cm and height of 25 cm. The quadcopter equipped with a multispectral camera with green, red, and near-infrared filters was used to acquire spectral images for formulating the RAV RS-based standardization system. To perform the calibration study process, libraries of sensor responses were constructed using pseudo-invariant tarps according to the light intensities to determine the relationship equations between the two factors. The calibrated images were then validated using the reflectance measured in crop fields. Finally, we evaluated the outcomes of the formulated RAV RS-based calibration system. The results of this study suggest that the standalone RAV RS system would be helpful in the processing of RAV RS-acquired images. [ABSTRACT FROM AUTHOR]

In this article, for the first time, we propose a transformer network-based reinforcement learning (RL) method for power distribution network (PDN) optimization of high bandwidth memory (HBM). The proposed method can provide an optimal decoupling capacitor (decap) design to maximize the reduction of PDN self- and transfer impedances seen at multiple ports. An attention-based transformer network is implemented to directly parameterize decap optimization policy. The optimality performance is significantly improved since the attention mechanism has powerful expression to explore massive combinatorial space for decap assignments. Moreover, it can capture sequential relationships between the decap assignments. The computing time for optimization is dramatically reduced due to the reusable network on the positions of probing ports and decap assignment candidates. This is because the transformer network has a context embedding process to capture meta-features including probing ports positions. In addition, the network is trained with randomly generated datasets. The computing time for training and data cost are critically decreased due to the scalability of the network. Due to its shared weight property and the context embedding process, the network can adapt to a larger scale of problems without additional training. For verification, the results are compared with conventional genetic algorithm (GA), random search (RS), and all the previous RL-based methods. As a result, the proposed method outperforms in all the following aspects: optimality performance, computing time, and data efficiency. [ABSTRACT FROM AUTHOR]

Deep learning (DL) and machine learning (ML) procedures are prevailing data-driven schemes capable of advancing crop-modelling practices that assimilate these techniques into a mathematical crop model. A DL or ML modelling scheme can effectively represent complicated algorithms. This study reports on an advanced fusion methodology for evaluating the leaf area index (LAI) of barley and wheat that employs remotely sensed information based on deep neural network (DNN) and ML regression approaches. We investigated the most appropriate ML regressors for exploring LAI estimations of barley and wheat through the relationships between the LAI values and four vegetation indices. After analysing ten ML regression models, we concluded that the gradient boost (GB) regressor most effectively estimated the LAI for both barley and wheat. Furthermore, the GB regressor outperformed the DNN regressor, with model efficiencies of 0.89 for barley and 0.45 for wheat. Additionally, we verified that it would be possible to simulate LAI using proximal and remote sensing data based on assimilating the DNN and ML regressors into a process-based mathematical crop model. In summary, we have demonstrated that if DNN and ML schemes are integrated into a crop model, they can facilitate crop growth and boost productivity monitoring. [ABSTRACT FROM AUTHOR]

In this article, we propose an embedded cooling structure with thermal transmission line (ECS-TTL) to improve thermal integrity (TI) and signal integrity (SI) of a high-bandwidth memory (HBM) module. The proposed hierarchical cooling scheme, ECS-TTL, consists of ECS for lowering thermal resistance and TTL for smoothing thermal distribution. The ECS cools the module’s integrated circuits by circulating fluid inside the computing module components. The TTL is a novel interconnection that focuses on transferring internal heat in a horizontal direction to achieve a uniform thermal distribution. The low and uniform thermal distribution achieved by the ECS-TTL can improve the temperature-dependent SI of an HBM module. We designed ECS-TTL by considering the structural reliability and SI of the HBM module based on its physical dimensions. We conducted TI verification of the HBM module by using a 3-D computational fluid dynamics solver; we evaluated the maximum temperature and the temperature uniformity of the HBM module with the proposed ECS-TTL and other cooling structures. Based on the TI evaluation, we conducted SI verification of the HBM by using a 3-D electromagnetic solver considering the operating thermal distribution; we evaluated the eye diagram and skew of the silicon interposer, through silicon via channels and a 3-D clock distribution network in the HBM. The evaluation verified that the proposed ECS-TTL can achieve a low and uniform thermal distribution while improving the eye diagram and skew of the HBM module compared to the conventional cooling structure. [ABSTRACT FROM AUTHOR]

This study aimed to simulate the spatiotemporal variation in cotton (Gossypium hirsutum L.) growth and lint yield using a remote sensing-integrated crop model (RSCM) for cotton. The developed modeling scheme incorporated proximal sensing data and satellite imagery. We formulated this model and evaluated its accuracy using field datasets obtained in Lamesa in 1999, Halfway in 2002 and 2004, and Lubbock in 2003–2005 in the Texas High Plains in the USA. We found that RSCM cotton could reproduce the cotton leaf area index and lint yield across different locations and irrigation systems with a statistically significant degree of accuracy. RSCM cotton was also used to simulate cotton lint yield for the field circles in Halfway. The RSCM system could accurately reproduce the spatiotemporal variations in cotton lint yield when integrated with satellite images. From the results of this study, we predict that the proposed crop-modeling approach will be applicable for the practical monitoring of cotton growth and productivity by farmers. Furthermore, a user can operate the modeling system with minimal input data, owing to the integration of proximal and remote sensing information. [ABSTRACT FROM AUTHOR]

In this article, we, for the first time, propose a reinforcement learning (RL) model to design an optimal 3-D cross-point (X-Point) array structure considering signal integrity issues. The interconnection design problem is modeled to the Markov decision process (MDP). The proposed RL model designs the 3-D X-Point array structure based on three reward factors: the number of bits, the crosstalk, and the IR drop. We applied multilayer perceptron (MLP) and long short-term memory (LSTM) to parameterize the policy. Proximal policy optimization (PPO) is used to optimize the parameters to train the policy. The reward of the proposed RL model is well-converged with variations in the array structure size and hyperparameters of the reward factors. We verified the scalability and sensitivity of the proposed RL model. With the optimal 3-D X-Point array structure design, we analyzed the reward factor and signal integrity issues. The optimal design of the 3-D X-Point array structure shows 17%–26.5% better signal integrity performance than the conventional design in finer process technology. In addition, we suggest a range of possible directions for improvement of the proposed model with variations in MDP tuples, reward factors, and learning algorithms, among other factors. Using the proposed model, we can easily design an optimal 3-D X-Point array structure with a certain size, performance capabilities, and specifications based on reward factors and hyperparameters. [ABSTRACT FROM AUTHOR]

In this article, we propose a processing-in-memory of high bandwidth memory (PIM-HBM) scheme including system architecture and hardware structure. The proposed scheme embeds processing units into the logic layer of the high bandwidth memory (HBM) to expose an excess dynamic random access memory (DRAM) bandwidth. With parallelized DRAM architecture and a high-speed through-silicon via (TSV) structure, the proposed scheme successfully extends the DRAM bandwidth of processing-in-memory (PIM). Also, the total energy consumption is decreased by the reduced interconnection and capacitance-reduced channel structure. We designed the overall architecture and structure with physical feasibility for application to the current HBM. The logic layer and DRAM layers in the HBM are configured to embed the processing units and parallelize the DRAM channels. For high-speed data transfer with low interconnect energy, the TSV and silicon interposer channels are designed and analyzed in consideration of signal integrity (SI). Based on the physical design, we obtained the interconnect length in detail. The interconnect energy and delay of the silicon interposer and on-chip interconnect were modeled through a SPICE simulation. We analyzed the accurate effects of interconnect reduction caused by PIM. For overall system performance and efficiency analysis, a cycle-level architectural simulation was conducted. We successfully evaluated and analyzed the system performance for memory-intensive applications. As a result, the proposed PIM-HBM achieves 53% and 10.4% improvement on average in computing performance and energy efficiency compared to the conventional graphic processing unit of high bandwidth memory (GPU-HBM). [ABSTRACT FROM AUTHOR]

A crop model incorporating proximal sensing images from a remote-controlled aerial system (RAS) can serve as an enhanced alternative for monitoring field-based geospatial crop productivity. This study aimed to investigate wheat productivity for different cultivars and various nitrogen application regimes and determine the best management practice scenario. We simulated spatiotemporal wheat growth and yield by integrating RAS-based sensing images with a crop-modeling system to achieve the study objective. We conducted field experiments and proximal sensing campaigns to acquire the ground truth data and RAS images of wheat growth conditions and yields. These experiments were performed at Gyeongsang National University (GNU), Jinju, South Gyeongsang province, Republic of Korea (ROK), in 2018 and 2019 and at Chonnam National University (CNU), Gwangju, ROK, in 2018. During the calibration at GNU in 2018, the wheat yields simulated by the modeling system were in agreement with the corresponding measured yields without significant differences (p = 0.27–0.91), according to two-sample t -tests. Furthermore, the yields simulated via this approach were in agreement with the measured yields at CNU in 2018 and at GNU in 2019 without significant differences (p = 0.28–0.86), as evidenced by two-sample t -tests; this proved the validity of the proposed modeling system. This system, when integrated with remotely sensed images, could also accurately reproduce the geospatial variations in wheat yield and growth variables. Given the results of this study, we believe that the proposed crop-modeling approach is applicable for the practical monitoring of wheat growth and productivity at the field level. [ABSTRACT FROM AUTHOR]

In this article, we first propose a deep reinforcement learning (RL)-based optimal decoupling capacitor (decap) design method for silicon interposer-based 2.5-D/3-D integrated circuits (ICs). The proposed method provides an optimal decap design that satisfies target impedance with a minimum area. Using deep RL algorithms based on reward feedback mechanisms, an optimal decap design guideline can be derived. For verification, the proposed method was applied to test power distribution networks (PDNs) and self-PDN impedance was compared with full search simulation results. We successfully verified by the full search simulation that the proposed method provides one of the solution sets. Conventional approaches are based on complex analytical models from power integrity (PI) domain expertise. However, the proposed method requires only specifications of the PDN structure and decap, along with a simple reward model, achieving fast and accurate data-driven results. Computing time of the proposed method was a few minutes, significantly reduced than that of the full search simulation, which took more than a month. Furthermore, the proposed deep RL method covered up to $10^{17}$ – $10^{18}$ cases, an approximately $10^{12}$ – $10^{13}$ order increase compared to the previous RL-based methods that did not utilize deep-learning techniques. [ABSTRACT FROM AUTHOR]

Current wireless power transfer (WPT) technology is widely used for various applications because of convenience and safety. However, WPT systems have problems with electromagnetic field (EMF) leakage, which can cause electromagnetic interference (EMI) issues for nearby electrical devices at various frequency ranges. In the case of the Qi standard that operates from 105 to 205 kHz frequency range, the third or fifth harmonic can cause EMI problems for the amplitude modulation bands of a radio corresponding from 530 to 1700 kHz. For the first time, in this article, we propose a frequency-selective EMI reduction method with an additional shielding coil for the effective suppression of EMI radiation of a WPT system. The proposed method reduces the EMI in a specific frequency range and has insignificant effect on the power transfer efficiency (PTE). We experimentally verified that the proposed method with the shielding coil reduced the EMF leakage by 8.96 dB of the third harmonics without reduction of the PTE. Furthermore, we mathematically designed the shielding coil using the proposed method. The mathematical calculation based on the equivalent circuit models is strongly correlated with the experimental measurement results. [ABSTRACT FROM AUTHOR]

In this paper, we first proposed a novel hybrid loop array (HLA) for low leakage electromagnetic field (EMF) level and high efficiency in a wireless high power transfer system. The proposed HLA effectively enhances the system efficiency and shields leakage EMF in a wireless power transfer (WPT) system using kHz range resonant frequency. The key originality of the proposed HLA is the combination of two types of loop coil; shielding loop coil (SLC) and amplifying loop coil (ALC). SLCs reduce leakage EMF, and ALCs significantly enhance the magnetic field from a Tx coil. The simulation and experiment results show that the proposed solution successfully overcomes the limitations of the existing solutions. Analytical modeling and design procedure are introduced and discussed. In addition, the experimental verification of the simulation result is included. We first designed and modeled an HLA considering the coupling effect of neighboring loop coils to evaluate its efficiency and leakage EMF. With the proposed HLA, we demonstrated a 9.36% improvement in the efficiency and 3 dBm reduction in the leakage EMF near the WPT system. [ABSTRACT FROM AUTHOR]

In this paper, we designed and demonstrated a smartwatch strap wireless charging system for the first time. First, we designed a flexible printed circuit board (PCB) coil, shielding material, and receiver (Rx) circuit in a watchstrap. In the design process, we proposed a model for the flexible PCB coil with a bending radius of 40 mm and shielding materials. We used a flexible PCB coil that has 215  μm thickness with dimensions of 54.5 × 16 mm. In addition, ferrite core and sheet are applied on the transmitter (Tx) and Rx coils. We verified the proposed model through a three-dimensional (3-D) electromagnetic (EM) simulation and measurement in the frequency and time domains. The proposed flexible PCB coil inductance modeling results showed 7.5% and 3.4% errors when compared to the 3-D EM simulation and measurement results, respectively. Furthermore, we demonstrated the smartwatch strap wireless charging system using an LG Watch Urbane. A resonance frequency of 100 kHz with the series–series tuning topology is used in accordance with the Qi specifications. Finally, we achieved 30% dc–dc power transfer efficiency and exposed magnetic field of 270 mG, 1 cm away from the system through measurements. [ABSTRACT FROM AUTHOR]

Recent developments in unmanned aerial system (UAS) require an urgent introduction to monitoring technologies of crop diagnostic information because of their advantage in manoeuvering tasks at a high-spatial resolutions and low costs in a user-friendly manner. In this study, an advanced application method of an UAS remote sensing system was performed using the grid GRAMI-rice model such that it can be driven using weather and remote sensing data to monitor the spatiotemporal productivities of rice (Oryza sativa). Remotely sensed data for the model were supplied, along with normalized difference vegetation index images obtained using the UAS remote sensing system. The model was first evaluated using paddy data from experimental fields (treated with two nitrogen (N) applications) at Chonnam National University, Gwangju, Republic of Korea (ROK). Practical application was then performed using paddy data from farm fields under conventional farm management practices at the Gimje plain in ROK. The grid GRAMI-rice model statistically well reproduces the field conditions of spatiotemporal rice productivities, showing an acceptable statistical accuracy in the comparison of growth between the simulated and observed values, using a Nash–Sutcliffe efficiency range of 0.113–0.955. According tot-tests (α = 0.05), there were no significant differences between the simulated and observed grain yields from both the evaluation and practical applications. The scientific approach adopted here is unique, advanced, and practical, in a way that UAS remote sensing methods were effectively incorporated with crop modelling techniques. Therefore, it was concluded that the UAS-based remote sensing techniques proposed in this study could represent an innovative way of projecting reliable spatiotemporal crop productivities for precision agriculture. [ABSTRACT FROM PUBLISHER]

Determining effective measures to alleviate the impact of climate change on crops under various regional environments is one of the most urgent issues facing agriculture. In this study, geographic regions of South Korea for future-adjusted barley cultivation were outlined and the impact of climate change on barley production in the next 100 years was evaluated under two greenhouse gas concentration trajectory scenarios: the representative concentration pathway (RCP) 4.5 and RCP 8.5. To achieve our intended study goals, a geospatial crop simulation modeling (GCSM) scheme was formulated using CERES-barley model of Decision Support System for Agricultural Technology (DSSAT) crop model package version 4.6 to simulate grid-based geospatial crop yields. Two experiments were carried out at an open field to obtain model coefficients for the nation and at temperature gradient field chambers to evaluate the performance of the CERES-barley model under elevated temperature conditions. Suitable cultivation regions for three different types of barley (naked, hooded, and malting) under changing climate were projected to expand to the northern regions under both RCP 8.5 and RCP 4.5. However, they were projected to expand more rapidly under RCP 8.5 than those under RCP 4.5. Projected yields of four barley varieties were increased with a slow phase as year progressed under RCP 4.5 scenario. However, they were rapidly increased under RCP 8.5 scenario. It appears that geospatial variation in barley yield under changing climate can be effectively outlined. Therefore, GCSM system might be useful for determining impacts of climate change on geospatial variations of crops, potentially providing means to impede food insecurity. [ABSTRACT FROM AUTHOR]

Previous studies have observed seasonal cooling effects in paddy rice as compared to temperate forest through enhanced evapotranspiration (ET) in Northeast Asia, while rare studies have revealed biophysical factors responsible for spatial variations of ET and its cooling effects. In this study, we adopted a data fusion method that integrated MODIS 8-day surface reflectance products, gridded daily climate data of ground surface, and a remote sensing pixel-based Penman-Monteith ET model (i.e., the RS–PM model) to quantify ET patterns of paddy rice in South Korea from 2011 to 2014. Results indicated that the regional variations of the rice-growing season ET (RGS-ET, the sum of daily ET from the season onset of rapid canopy expansion (SoS) to the end of the rice-growing season (EGS)) were primarily influenced by phenological factors (i.e., the length of growing period-LGP), followed by growing season mean climatic factors (i.e., vapor pressure deficit-VPD, and air temperature). For regional variations of the paddy field ET (PF-ET, the sum of daily ET from the field flooding and transplanting date detected by satellite observations (FFTDsat) to SoS, and to EGS), the extents were substantially reduced, only accounting for 54% of the RGS-ET variations. The FFTDsat and SoS were considered critical for the reduced PF-ET variations. In comparison to the temperate forest, changes in monthly ground surface air temperature (Ts) in paddy fields showed the V-shaped seasonal pattern with significant cooling effects found in late spring and early summer, primarily due to a large decline in daytime Ts that exceeded the nighttime warming. Bringing FFTDsat towards late spring and early summer was identified as vital field management practices, causing significant declines in daytime Ts due to enhanced ET. Results highlighted climate-warming mitigation by paddy fields due to early flooding practices. [ABSTRACT FROM AUTHOR]

This study proposes an improved approach for monitoring the spatial concentrations of hourly particulate matter less than 2.5 μm in diameter (PM2.5) via a deep neural network (DNN) using geostationary ocean color imager (GOCI) images and unified model (UM) reanalysis data over the Korean Peninsula. The DNN performance was optimized to determine the appropriate training model structures, incorporating hyperparameter tuning, regularization, early stopping, and input and output variable normalization to prevent training dataset overfitting. Near-surface atmospheric information from the UM was also used as an input variable to spatially generalize the DNN model. The retrieved PM2.5 from the DNN was compared with estimates from random forest, multiple linear regression, and the Community Multiscale Air Quality model. The DNN demonstrated the highest accuracy compared to that of the conventional methods for the hold-out validation (root mean square error (RMSE) = 7.042 μg/m3, mean bias error (MBE) = −0.340 μg/m3, and coefficient of determination (R2) = 0.698) and the cross-validation (RMSE = 9.166 μg/m3, MBE = 0.293 μg/m3, and R2 = 0.49). Although the R2 was low due to underestimated high PM2.5 concentration patterns, the RMSE and MBE demonstrated reliable accuracy values (<10 μg/m3 and 1 μg/m3, respectively) for the hold-out validation and cross-validation. [ABSTRACT FROM AUTHOR]

It is important to be able to predict the yield and monitor the growth conditions of crops in the field to increase productivity. One way to assess field-based geospatial crop productivity is by integrating a crop model with a remote-controlled aerial system (RAS). The objective of this study was to simulate spatiotemporal barley growth and yield based on the development of a crop-modeling system integrated with RAS-based remote sensing images. We performed field experiments to obtain ground truth data and RAS images of crop growth conditions and yields at Chonnam National University (CNU), Gwangju, South Korea in 2018, and at Gyeongsang National University (GNU), Jinju, South Gyeongsang, South Korea in 2018 and 2019. In model calibration, there was no significant difference (p = 0.12) between the simulated barley yields and measured yields, based on a two-sample t-test at CNU in 2018. In model validation, there was no significant difference between simulated yields and measured yields at p = 0.98 and 0.76, according to two-sample t-tests at GNU in 2018 and 2019, respectively. The remote sensing-integrated crop model accurately reproduced geospatial variations in barley yield and growth variables. The results demonstrate that the crop modeling approach is useful for monitoring at-field barley conditions. [ABSTRACT FROM AUTHOR]

The quantification of canopy photosynthesis and evapotranspiration of crops (ET c) is essential to appreciate the effects of environmental changes on CO 2 flux and water availability in agricultural ecosystems and crop productivity. This study simulated the canopy photosynthesis and ET processes of paddy rice (Oryza sativa) based on the development of physiological modules (i.e., gross primary production [GPP] and ET c) and their incorporation into the GRAMI-rice model that uses remote sensing data. We also projected spatiotemporal variations in the GPP, ET, yield, and biomass of paddy rice at maturity using the updated GRAMI-rice model combined with geostationary satellite images to identify the relationships of canopy photosynthesis and ET c with crop productivity. GPP and ET data for paddy rice were obtained from three KoFlux sites in South Korea in 2015 and 2016. Vegetation indices were acquired from the Geostationary Ocean Color Imager (GOCI) of the Communication Ocean and Meteorological Satellite (COMS) from 2012 to 2017 and integrated into GRAMI-rice. GPP and ET c estimates using GRAMI-rice were in close agreement with flux tower estimates with Nash-Sutcliffe efficiency ranges of 0.40–0.79 for GPP and 0.49–0.62 for ET c. Also, GRAMI-rice was reasonably well incorporated with the COMS GOCI imagery and reproduced spatiotemporal variations in the GPP and ET of rice in the Korean peninsula. The current study results demonstrate that the updated GRAMI–rice model with the canopy photosynthesis and ET c modules is capable of reproducing spatiotemporal variations in CO 2 assimilation and ET of paddy rice at various geographical scales and for regions of interest that are observable by satellite sensors (e.g., inaccessible North Korea). Unlabelled Image • Geostationary satellite images were integrated into the GRAMI-rice model. • Simulated gross primary production (GPP) agreed with flux tower-measured GPP. • Simulated crop evapotranspiration (ET c) corresponded to the flux tower-measured ET c. • The modeling system well reproduced geographical variations in GPP and ET c. • Strong correlations between GPP and yield were observed in western Korea. [ABSTRACT FROM AUTHOR]

A remote sensing-integrated crop model (RSCM) able to simulate crop growth processes using proximal or remote sensing data was formulated for simulation of soybean through estimating parameters required for modelling. The RSCM-soybean was then evaluated for its capability of simulating leaf area index (LAI), above-ground dry mass (AGDM), and yield, utilising the proximally sensed data integration into the modelling procedure. Field experiments were performed at two sites, one in 2017 and 2018 at Chonnam National University, Gwangju, and the other in 2017 at Jonnam Agricultural Research and Extension Services in Naju, Chonnam province, South Korea. The estimated parameters of radiation use efficiency, light extinction coefficient, and specific leaf area were 1.65 g MJ−1, 0.71, and 0.017 m2 g−1, respectively. Simulated LAI and AGDM values agreed with the measured values with significant model efficiencies in both calibration and validation, meaning that the proximal sensing data were effectively integrated into the crop model. The RSCM reproduced soybean yields in significant agreement with the measured yields in the model assessment. The study results demonstrate that the well-calibrated RSCM-soybean scheme can reproduce soybean growth and yield using simple input requirement and proximal sensing data. RSCM-soybean is easy to use and applicable to various soybean monitoring projects. [ABSTRACT FROM AUTHOR]

Remote sensing is a useful technique to determine spatial variations in crop growth while crop modelling can reproduce temporal changes in crop growth. In this study, we formulated a hybrid system of remote sensing and crop modelling based on a random-effect model and the empirical Bayesian approach for parameter estimation. Moreover, the relationship between the reflectance and the leaf area index was incorporated into the statistical model. Plant growth and ground-based canopy reflectance data of paddy rice were measured at three study sites in South Korea. Spatiotemporal vegetation indices were processed using remotely-sensed data from the RapidEye satellite and the Communication Ocean and Meteorological Satellite (COMS). Solar insulation data were obtained from the Meteorological Imager (MI) sensor of the COMS. Reanalysis of air temperature data was collected from the Korea Local Analysis and Prediction System (KLAPS). We report on a statistical hybrid approach of crop modelling and remote sensing and a method to project spatiotemporal crop growth information. Our study results show that the crop growth values predicted using the hybrid scheme were in statistically acceptable agreement with the corresponding measurements. Simulated yields were not significantly different from the measured yields at p = 0.883 in calibration and p = 0.839 in validation, according to two-sample t tests. In a geospatial simulation of yield, no significant difference was found between the simulated and observed mean value at p = 0.392 based on a two-sample t test as well. The fabricated approach allows us to monitor crop growth information and estimate crop-modelling processes using remote sensing data from various platforms and optical sensors with different ground resolutions. [ABSTRACT FROM AUTHOR]

Radiometric calibration of satellite imaging sensors should be performed periodically to account for the effect of sensor degradation in the space environment on image accuracy. In this study, we performed vicarious radiometric calibrations (relying on in situ data) of multispectral imaging sensors on the Korea multi-purpose satellite-3 and -3A (KOMPSAT-3 and -3A) to adjust the existing radiometric conversion coefficients according to time delay integration (TDI) adjustments and sensor degradation over time. The Second Simulation of a Satellite Signal in the Solar Spectrum (6S) radiative transfer model was used to obtain theoretical top of atmosphere radiances for both satellites. As input parameters for the 6S model, surface reflectance values of well-characterized pseudo-invariant tarps were measured using dual ASD FieldSpec® 3 hyperspectral radiometers, and atmospheric conditions were measured using Microtops II® Sunphotometer and Ozonometer. We updated the digital number (DN) of the radiance coefficients of the satellites; these had been used to calibrate the sensors during in-orbit test periods in 2013 and 2015. The coefficients of determination, R2, values between observed DNs of the sensors, and simulated radiances for the tarps were more than 0.999. The calibration errors were approximately 5.7% based on manifested error sources. We expect that the updated coefficients will be an important reference for KOMPSAT-3 and -3A users. [ABSTRACT FROM AUTHOR]