Geographical model for precise agriculture monitoring with real-time remote sensing

Beeri, Ofer; Peled, Ammatzia

doi:10.1016/j.isprsjprs.2008.07.007

Cited by 31 publications

(13 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[1] These technologies have been extensively employed in diverse areas of science and engineering studies such as global warming, [2] sustainable management of natural resources, [3] emergency response systems for natural events, [4] and weather and climate modelling, [5] financial and environmental analysis of investments in energy markets, [6] real estate business, [7] urban development planning, [8] agriculture, [9] farming industry and rural area management, [10,11] and oil and mining industry. [12,13] Remotely sensed image data have been used as a primary information source in spatial technologies.…”

Section: Significance Of Atmospheric Correction In Remote Sensingmentioning

confidence: 99%

Inversion of top of atmospheric reflectance values by conic multivariate adaptive regression splines

Kuter

Weber

Akyürek

et al. 2014

Inverse Problems in Science and Engineering

View full text Add to dashboard Cite

Spatial technologies offer high flexibility to handle substantial amount of spatial data and wide range of modelling capabilities. Remotely sensed data are the most significant data source used in spatial technologies. However, it is often associated with uncertainties due to atmospheric effects (i.e. absorption and scattering by atmospheric gases and aerosols). Methods based on rigorous treatment of radiative transfer models still have some drawbacks in the inversion of top of atmospheric reflectance values to surface reflectance values on large numbers of satellite images. In this paper, our aim is to represent a more flexible (adaptive) approach for the regional atmospheric correction by employing nonparametric regression splines within the frame of inverse problems and modern techniques of continuous optimization. To achieve this objective, atmospheric correction models obtained through conic multivariate adaptive regression splines, which is an alternative method to multivariate adaptive regression splines by constructing a penalized residual sum of squares as a Tikhonov regularization problem, are applied on a set of satellite images in order to convert the top of atmospheric reflectance values into surface reflectance values. The results are compared with the ones obtained by both multivariate adaptive regression splines and a radiative transfer-based method.

show abstract

Section: Significance Of Atmospheric Correction In Remote Sensingmentioning

confidence: 99%

Inversion of top of atmospheric reflectance values by conic multivariate adaptive regression splines

Kuter

Weber

Akyürek

et al. 2014

Inverse Problems in Science and Engineering

View full text Add to dashboard Cite

show abstract

“…For many years, remote sensing (RS) systems have been widely applied for agricultural monitoring and crop identification [1][2][3][4], and these systems provide many surface reflectance images that can be utilized to derive hidden patterns of vegetation coverage. In crop identification tasks, the information of growing dynamics or sequential relationships derived from time-series images is used to perform classification.…”

Section: Introductionmentioning

confidence: 99%

Evaluation and Comparison of Random Forest and A-LSTM Networks for Large-scale Winter Wheat Identification

Xie

Liu

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

Machine learning comprises a group of powerful state-of-the-art techniques for land cover classification and cropland identification. In this paper, we proposed and evaluated two models based on random forest (RF) and attention-based long short-term memory (A-LSTM) networks that can learn directly from the raw surface reflectance of remote sensing (RS) images for large-scale winter wheat identification in Huanghuaihai Region (North-Central China). We used a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) images over one growing season and the corresponding winter wheat distribution map for the experiments. Each training sample was derived from the raw surface reflectance of MODIS time-series images. Both models achieved state-of-the-art performance in identifying winter wheat, and the F1 scores of RF and A-LSTM were 0.72 and 0.71, respectively. We also analyzed the impact of the pixel-mixing effect. Training with pure-mixed-pixel samples (the training set consists of pure and mixed cells and thus retains the original distribution of data) was more precise than training with only pure-pixel samples (the entire pixel area belongs to one class). We also analyzed the variable importance along the temporal series, and the data acquired in March or April contributed more than the data acquired at other times. Both models could predict winter wheat coverage in past years or in other regions with similar winter wheat growing seasons. The experiments in this paper showed the effectiveness and significance of our methods.

show abstract

“…Therefore, the LST is especially important for lots of studies, including climate change, evapotranspiration evaluation, hydrologic cycle, urban heat island, vegetation monitoring and environmental studies [1][2][3][4][5][6][7][8][9][10]. Additionally, the land surface emissivity (LSE) is also an important factor of the thermally emitted radiance from the surface, which depends on the material composition, surface roughness, wavelength, viewing angle and moisture [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

A Temperature and Emissivity Separation Algorithm for Landsat-8 Thermal Infrared Sensor Data

Wang

2015

Remote Sensing

View full text Add to dashboard Cite

On-board the Landsat-8 satellite, the Thermal Infrared Sensor (TIRS), which has two adjacent thermal channels centered roughly at 10.9 and 12.0 μm, has a great benefit for the land surface temperature (LST) retrieval. The single-channel algorithm (SC) and split-window algorithm (SW) have been applied to retrieve the LST from TIRS data, which need the land surface emissivity (LSE) as prior knowledge. Due to the big challenge of determining the LSE, this study develops a temperature and emissivity separation algorithm which can simultaneously retrieve the LST and LSE. Based on the laboratory emissivity spectrum data, the minimum-maximum emissivity difference module (MMD module) for TIRS data is developed. Then, an emissivity log difference method (ELD method) is developed to maintain the emissivity spectrum shape in the iterative process, which is based on the modified Wien's approximation. Simulation results show that the root-meansquare-errors (RMSEs) are below 0.7 K for the LST and below 0.015 for the LSE. Based on the SURFRAD ground measurements, further evaluation demonstrates that the average absolute error of the LST is about 1.7 K, which indicated that the algorithm is capable of retrieving the LST and LSE simultaneously from TIRS data with fairly good results. OPEN ACCESSRemote Sens. 2015, 7 9905 Keywords: Landsat-8; TIRS; land surface temperature (LST); land surface emissivity (LSE); minimum-maximum emissivity difference method (MMD method); emissivity log difference method (ELD method); MODTRAN; SURFRAD

show abstract

Geographical model for precise agriculture monitoring with real-time remote sensing

Cited by 31 publications

References 19 publications

Inversion of top of atmospheric reflectance values by conic multivariate adaptive regression splines

Inversion of top of atmospheric reflectance values by conic multivariate adaptive regression splines

Evaluation and Comparison of Random Forest and A-LSTM Networks for Large-scale Winter Wheat Identification

A Temperature and Emissivity Separation Algorithm for Landsat-8 Thermal Infrared Sensor Data

Contact Info

Product

Resources

About