Support Irrigation Water Management of Cereals Using Optical Remote Sensing and Modeling in a Semi-Arid Region

Irrigated agriculture is an important strategic sector for Morocco, contributing to food security and employment. Nowadays, irrigation scheme managers shall ensure that water is optimally used. The main objective was to support the irrigation monitoring and management of wheat in the irrigated perimeter using optical remote sensing and crop modeling. The potential of spectral indices derived from SPOT-5 images was explored for quantifying and mapping surface water content changes at large scale. Indices were computed using the reflectance in red, near infrared, and shortwave infrared bands. A field crop model (AquaCrop) was adjusted and tested to simulate the grain yield and the temporal evolution of soil moisture status. This research aimed at providing a scientific and technical approach to assist policymakers and stakeholders to improve monitoring irrigation and mitigating wheat water stress at field and irrigation perimeter levels in semi-arid areas. The approach could lead to operational management tools for an efficient irrigation at field and regional levels.

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Biophysical Characterization and Monitoring Large‐Scale Water and Vegetation Anomalies by Remote Sensing in the Agricultural Growing Areas of the Brazilian Semi‐Arid Region

Advances in Remote Sensing for Natural Resource Monitoring ◽

10.1002/9781119616016.ch7 ◽

2021 ◽

pp. 94-109

Author(s):

Antônio Heriberto de Castro Teixeira ◽

Janice Freitas Leivas ◽

Edson Patto Pacheco ◽

Edlene Aparecida Monteiro Garçon ◽

Celina Maki Takemura

Keyword(s):

Remote Sensing ◽

Arid Region ◽

Large Scale ◽

Biophysical Characterization ◽

Semi Arid Region ◽

Semi Arid

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Remote Sensing of Soil Organic Carbon in Semi-Arid Region of Iran

Arid Land Research and Management ◽

10.1080/15324982.2010.502917 ◽

2010 ◽

Vol 24 (4) ◽

pp. 271-281 ◽

Cited By ~ 13

Author(s):

Moslem Ladoni ◽

Seyed Kazem Alavipanah ◽

Hosein Ali Bahrami ◽

Ali Akbar Noroozi

Keyword(s):

Remote Sensing ◽

Organic Carbon ◽

Soil Organic Carbon ◽

Arid Region ◽

Semi Arid Region ◽

Semi Arid

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Use of remote sensing to identify areas at risk of degradation in the semi-arid region

Revista Ciência Agronômica ◽

10.5935/1806-6690.20180047 ◽

2018 ◽

Vol 49 (3) ◽

Cited By ~ 1

Author(s):

Deodato do Nascimento Aquino ◽

Odílio Coimbra da Rocha Neto ◽

Maurício Alves Moreira ◽

Adunias dos Santos Teixeira ◽

Eunice Maia de Andrade

Keyword(s):

Remote Sensing ◽

At Risk ◽

Arid Region ◽

Semi Arid Region ◽

Semi Arid

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Geobotanical Prospecting for Underwater Deposits in Desert and Semi Arid Region. Patagonia Argentina by Remote Sensing Studies

10.47204/eesr.1.2.2020.190-200 ◽

2020 ◽

Vol 1 (2) ◽

pp. 190-200

Author(s):

Javier Ulibarrena ◽

Adolfo Rojo

Keyword(s):

Remote Sensing ◽

Arid Region ◽

Semi Arid Region ◽

Semi Arid

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Machine Learning to Estimate Surface Soil Moisture from Remote Sensing Data

Water ◽

10.3390/w12113223 ◽

2020 ◽

Vol 12 (11) ◽

pp. 3223

Author(s):

Hamed Adab ◽

Renato Morbidelli ◽

Carla Saltalippi ◽

Mahmoud Moradian ◽

Gholam Abbas Fallah Ghalhari

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Soil Moisture ◽

Surface Soil ◽

Remote Sensing Data ◽

Surface Soil Moisture ◽

Machine Learning Methods ◽

Land Use Types ◽

Semi Arid Region ◽

Semi Arid

Soil moisture is an integral quantity parameter in hydrology and agriculture practices. Satellite remote sensing has been widely applied to estimate surface soil moisture. However, it is still a challenge to retrieve surface soil moisture content (SMC) data in the heterogeneous catchment at high spatial resolution. Therefore, it is necessary to improve the retrieval of SMC from remote sensing data, which is important in the planning and efficient use of land resources. Many methods based on satellite-derived vegetation indices have already been developed to estimate SMC in various climatic and geographic conditions. Soil moisture retrievals were performed using statistical and machine learning methods as well as physical modeling techniques. In this study, an important experiment of soil moisture retrieval for investigating the capability of the machine learning methods was conducted in the early spring season in a semi-arid region of Iran. We applied random forest (RF), support vector machine (SVM), artificial neural network (ANN), and elastic net regression (EN) algorithms to soil moisture retrieval by optical and thermal sensors of Landsat 8 and knowledge of land-use types on previously untested conditions in a semi-arid region of Iran. The statistical comparisons show that RF method provided the highest Nash–Sutcliffe efficiency value (0.73) for soil moisture retrieval covered by the different land-use types. Combinations of surface reflectance and auxiliary geospatial data can provide more valuable information for SMC estimation, which shows promise for precision agriculture applications.

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Remote Sensing Landslide Recognition Based on Convolutional Neural Network

Mathematical Problems in Engineering ◽

10.1155/2019/8389368 ◽

2019 ◽

Vol 2019 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Yu Wang ◽

Xiaofei Wang ◽

Junfan Jian

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Convolutional Neural Network ◽

Large Scale ◽

Vegetation Index ◽

Near Infrared ◽

Spatial Information ◽

Spectral Characteristics ◽

Remote Sensing Images ◽

Landslide Event

Landslides are a type of frequent and widespread natural disaster. It is of great significance to extract location information from the landslide in time. At present, most articles still select single band or RGB bands as the feature for landslide recognition. To improve the efficiency of landslide recognition, this study proposed a remote sensing recognition method based on the convolutional neural network of the mixed spectral characteristics. Firstly, this paper tried to add NDVI (normalized difference vegetation index) and NIRS (near-infrared spectroscopy) to enhance the features. Then, remote sensing images (predisaster and postdisaster images) with same spatial information but different time series information regarding landslide are taken directly from GF-1 satellite as input images. By combining the 4 bands (red + green + blue + near-infrared) of the prelandslide remote sensing images with the 4 bands of the postlandslide images and NDVI images, images with 9 bands were obtained, and the band values reflecting the changing characteristics of the landslide were determined. Finally, a deep learning convolutional neural network (CNN) was introduced to solve the problem. The proposed method was tested and verified with remote sensing data from the 2015 large-scale landslide event in Shanxi, China, and 2016 large-scale landslide event in Fujian, China. The results showed that the accuracy of the method was high. Compared with the traditional methods, the recognition efficiency was improved, proving the effectiveness and feasibility of the method.

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An Algorithm to Retrieve Total Precipitable Water Vapor in the Atmosphere from FengYun 3D Medium Resolution Spectral Imager 2 (FY-3D MERSI-2) Data

Remote Sensing ◽

10.3390/rs12213469 ◽

2020 ◽

Vol 12 (21) ◽

pp. 3469

Author(s):

Bilawal Abbasi ◽

Zhihao Qin ◽

Wenhui Du ◽

Jinlong Fan ◽

Chunliang Zhao ◽

...

Keyword(s):

Remote Sensing ◽

Water Vapor ◽

Near Infrared ◽

Precipitable Water ◽

Precipitable Water Vapor ◽

Optical Remote Sensing ◽

Statistical Regression ◽

Sensing Applications ◽

Medium Resolution ◽

Spectral Imager

The atmosphere has substantial effects on optical remote sensing imagery of the Earth’s surface from space. These effects come through the functioning of atmospheric particles on the radiometric transfer from the Earth’s surface through the atmosphere to the sensor in space. Precipitable water vapor (PWV), CO2, ozone, and aerosol in the atmosphere are very important among the particles through their functioning. This study presented an algorithm to retrieve total PWV from the Chinese second-generation polar-orbiting meteorological satellite FengYun 3D Medium Resolution Spectral Imager 2 (FY-3D MERSI-2) data, which have three near-infrared (NIR) water vapor absorbing channels, i.e., channel 16, 17, and 18. The algorithm was improved from the radiance ratio technique initially developed for Moderate-Resolution Imaging Spectroradiometer (MODIS) data. MODTRAN 5 was used to simulate the process of radiant transfer from the ground surfaces to the sensor at various atmospheric conditions for estimation of the coefficients of ratio technique, which was achieved through statistical regression analysis between the simulated radiance and transmittance values for FY-3D MERSI-2 NIR channels. The algorithm was then constructed as a linear combination of the three-water vapor absorbing channels of FY-3D MERSI-2. Measurements from two ground-based reference datasets were used to validate the algorithm: the sun photometer measurements of Aerosol Robotic Network (AERONET) and the microwave radiometer measurements of Energy’s Atmospheric Radiation Measurement Program (ARMP). The validation results showed that the algorithm performs very well when compared with the ground-based reference datasets. The estimated PWV values come with root mean square error (RMSE) of 0.28 g/cm2 for the ARMP and 0.26 g/cm2 for the AERONET datasets, with bias of 0.072 g/cm2 and 0.096 g/cm2 for the two reference datasets, respectively. The accuracy of the proposed algorithm revealed a better consistency with ground-based reference datasets. Thus, the proposed algorithm could be used as an alternative to retrieve PWV from FY-3D MERSI-2 data for various remote sensing applications such as agricultural monitoring, climate change, hydrologic cycle, and so on at various regional and global scales.

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Rapid dehazing algorithm based on large-scale median filtering for high-resolution visible near-infrared remote sensing images

International Journal of Wavelets Multiresolution and Information Processing ◽

10.1142/s0219691314610104 ◽

2014 ◽

Vol 12 (05) ◽

pp. 1461010 ◽

Cited By ~ 1

Author(s):

Changmiao Hu ◽

Ping Tang

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Land Surface ◽

Large Scale ◽

Near Infrared ◽

Low Frequency ◽

Median Filtering ◽

Remote Sensing Images ◽

Infrared Band ◽

Remote Sensing Image Processing

In recent years, China's demand for satellite remote sensing images increased. Thus, the country launched a series of satellites equipped with high-resolution sensors. The resolutions of these satellites range from 30 m to a few meters, and the spectral range covers the visible to the near-infrared band. These satellite images are mainly used for environmental monitoring, mapping, land surface classification and other fields. However, haze is an important factor that often affects image quality. Thus, dehazing technology is becoming a critical step in high-resolution remote sensing image processing. This paper presents a rapid algorithm for dehazing based on a semi-physical haze model. Large-scale median filtering technique is used to extract large areas of bright, low-frequency information from images to estimate the distribution and thickness of the haze. Four images from different satellites are used for experiment. Results show that the algorithm is valid, fast, and suitable for the rapid dehazing of numerous large-sized high-resolution remote sensing images in engineering applications.

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