scholarly journals Soil Salinity Mapping and Hydrological Drought Indices Assessment in Arid Environments Based on Remote Sensing Techniques

2016 ◽  
Author(s):  
Mohamed Elhag ◽  
Jarbou A. Bahrawi
Keyword(s):  
Remote Sensing ◽  
Soil Salinity ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Hydrological Drought ◽  
Stress Index ◽  
Drought Indices ◽  
Arid Environments ◽  
Crop Water ◽  
Sensing Applications

Abstract. Vegetation indices are mostly described as crop water derivatives. Normalized Difference Vegetation Index (NDVI) is one of the oldest remote sensing applications that widely used to evaluate crop vigor directly and crop water relationships indirectly. Recently, several NDVI derivatives are exclusively used to assess crop water relationships. Four hydrological drought indices are examined in the current research study. Water Supply Vegetation Index (WSVI), Soil Adjusted Vegetation Index (SAVI), Moisture Stress Index (MSI) and Normalized Difference Infrared Index (NDII) are implemented in the current study as an indirect tool to map the effect of different soil salinity level on crop water stress in arid environments. In arid environments; such as Saudi Arabia, water resources are under pressure especially groundwater levels. Groundwater wells are rapidly depleted due to the heavy abstraction of the reserved water. Heavy abstractions of groundwater; which exceed crop water requirements in most of the cases are powered by high evaporation rates in the designated study area because of the long days of extremely hot summer. Landsat OLI-8 data were extensively used in the current research to obtain several vegetation indices in response to soil salinity in Wadi Ad-Waser. Principal Component Analysis and Artificial Neural Network Analysis are complementary tools to understand the regression pattern of the hydrological drought indices in the designated study area.

scholarly journals Soil salinity mapping and hydrological drought indices assessment in arid environments based on remote sensing techniques

2017 ◽  
Vol 6 (1) ◽  
pp. 149-158 ◽  
Author(s):  
Mohamed Elhag ◽  
Jarbou A. Bahrawi
Keyword(s):  
Remote Sensing ◽  
Soil Salinity ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Hydrological Drought ◽  
Drought Indices ◽  
Landsat 8 ◽  
Arid Environments ◽  
Crop Water ◽  
Sensing Applications

Abstract. Vegetation indices are mostly described as crop water derivatives. The normalized difference vegetation index (NDVI) is one of the oldest remote sensing applications that is widely used to evaluate crop vigor directly and crop water relationships indirectly. Recently, several NDVI derivatives were exclusively used to assess crop water relationships. Four hydrological drought indices are examined in the current research study. The water supply vegetation index (WSVI), the soil-adjusted vegetation index (SAVI), the moisture stress index (MSI) and the normalized difference infrared index (NDII) are implemented in the current study as an indirect tool to map the effect of different soil salinity levels on crop water stress in arid environments. In arid environments, such as Saudi Arabia, water resources are under pressure, especially groundwater levels. Groundwater wells are rapidly depleted due to the heavy abstraction of the reserved water. Heavy abstractions of groundwater, which exceed crop water requirements in most of the cases, are powered by high evaporation rates in the designated study area because of the long days of extremely hot summer. Landsat 8 OLI data were extensively used in the current research to obtain several vegetation indices in response to soil salinity in Wadi ad-Dawasir. Principal component analyses (PCA) and artificial neural network (ANN) analyses are complementary tools used to understand the regression pattern of the hydrological drought indices in the designated study area.

scholarly journals Remote Sensing Indices for Spatial Monitoring of Agricultural Drought in South Asian Countries

2021 ◽  
Vol 13 (11) ◽  
pp. 2059
Author(s):  
Muhammad Shahzaman ◽  
Weijun Zhu ◽  
Muhammad Bilal ◽  
Birhanu Asmerom Habtemicheal ◽  
Farhan Mustafa ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Agricultural Drought ◽  
Stress Index ◽  
Drought Indices ◽  
Drought Period ◽  
Drought Assessment ◽  
The North

Drought is an intricate atmospheric phenomenon with the greatest impacts on food security and agriculture in South Asia. Timely and appropriate forecasting of drought is vital in reducing its negative impacts. This study intended to explore the performance of the evaporative stress index (ESI), vegetation health index (VHI), enhanced vegetation index (EVI), and standardized anomaly index (SAI) based on satellite remote sensing data from 2002–2019 for agricultural drought assessment in Afghanistan, Pakistan, India, and Bangladesh. The spatial maps were generated against each index, which indicated a severe agricultural drought during the year 2002, compared to the other years. The results showed that the southeast region of Pakistan, and the north, northwest, and southwest regions of India and Afghanistan were significantly affected by drought. However, Bangladesh faced substantial drought in the northeast and northwest regions during the drought year (2002). The longest drought period of seven months was observed in India followed by Pakistan and Afghanistan with six months, while, only three months were perceived in Bangladesh. The correlation between drought indices and climate variables such as soil moisture has remained a significant drought-initiating variable. Furthermore, this study confirmed that the evaporative stress index (ESI) is a good agricultural drought indicator, being quick and with greater sensitivity, and thus advantageous compared to the VHI, EVI, and SAI vegetation indices.

scholarly journals Evaluation of Different Soil Salinity Mapping Using Remote Sensing Techniques in Arid Ecosystems, Saudi Arabia

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Mohamed Elhag
Keyword(s):  
Remote Sensing ◽  
Saudi Arabia ◽  
Soil Salinity ◽  
Sand Dunes ◽  
Vegetation Indices ◽  
Agricultural Practices ◽  
Remote Sensing Data ◽  
Arid Ecosystems ◽  
Groundwater Levels ◽  
Sensing Applications

Land covers in Saudi Arabia are generally described as salty soils with sand dunes and sand sheets. Waterlogging and higher soil salinity are major challenges to sustaining agricultural practices in Saudi Arabia principally within closed drainage basins. Agricultural practices in Saudi Arabia were flourishing in the last two decades. The newly reclaimed lands were added annually and distributed all over the country. Irrigation techniques are mostly modernized to fulfill water saving strategies. Nevertheless, water resources in Saudi Arabia are under stress and groundwater levels are depleted rapidly due to heavy abstraction that may exceed crop water requirements in most of the cases due to high evaporation rates. The excess use of irrigational water leads to severe soil salinity problems. Applications of remote sensing technique in agricultural practices became widely distinctive and cover multidisciplinary principal interests on both local and regional levels. The most important remote sensing applications in agricultural practices are vegetation indices which are related to vegetation and water especially in an arid environment. Soil salinity mapping in an arid ecosystem using remote sensing data is a demanding task. Several soil salinity indices were implemented and evaluated to detect soil salinity effectively and quantitatively. Thematic maps of soil salinity were satisfactorily produced and assessed.

scholarly journals Estimation of Crop Evapotranspiration Using Satellite Remote Sensing-Based Vegetation Index

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Arturo Reyes-González ◽  
Jeppe Kjaersgaard ◽  
Todd Trooien ◽  
Christopher Hay ◽  
Laurent Ahiablame
Keyword(s):  
Remote Sensing ◽  
Irrigation Water ◽  
Water Conservation ◽  
Satellite Remote Sensing ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Crop Evapotranspiration ◽  
Crop Water ◽  
Landsat Images ◽  
Crop Water Consumption

Irrigation water is limited and scarce in many areas of the world, including Comarca Lagunera, Mexico. Thus better estimations of irrigation water requirements are essential to conserve water. The general objective was to estimate crop water demands or crop evapotranspiration (ETc) at different scales using satellite remote sensing-based vegetation index. The study was carried out in northern Mexico (Comarca Lagunera) during four growing seasons. Six, eleven, three, and seven clear Landsat images were acquired for 2013, 2014, 2015, and 2016, respectively, for the analysis. The results showed thatETcwas low at initial and early development stages, whileETcwas high during mid-season and harvest stages. These results are not new but give us confidence in the rest of ourETcresults. DailyETcmaps helped to explain the variability of crop water use during the growing season. Based on the results we can conclude thatETcmaps developed from remotely sensed multispectral vegetation indices are a useful tool for quantifying crop water consumption at regional and field scales. UsingETcmaps at the field scale, farmers can supply appropriate amounts of irrigation water corresponding to each growth stage, leading to water conservation.

scholarly journals Regional-scale drought monitor using synthesized index based on remote sensing in northeast China

2020 ◽  
Vol 12 (1) ◽  
pp. 163-173 ◽  
Author(s):  
Xiaofang Sun ◽  
Meng Wang ◽  
Guicai Li ◽  
Yuanyuan Wang
Keyword(s):  
Remote Sensing ◽  
Northeast China ◽  
Vegetation Index ◽  
Potential Evapotranspiration ◽  
Regional Scale ◽  
Growth Period ◽  
Stress Index ◽  
Drought Indices ◽  
Drought Severity ◽  
Yield Data

AbstractDrought has a significant impact on agricultural, ecological, and socioeconomic spheres. Although many drought indices have been proposed until now, the detection of droughts at regional scales still needs to be further studied. The Standardized Vegetation Index (SVI) that represents vegetation growing condition, the Standardized Water Index (SWI) that represents canopy water content, and the Evaporative Stress Index (ESI) that quantifies anomalies in the ratio of actual to potential evapotranspiration were calculated based on the Moderate-resolution Imaging Spectroradiometer (MODIS) data. A new remote sensing-based Vegetation Drought Monitor Synthesized Index (VDSI) was proposed by integrating the SVI, SWI, and ESI in the northeast China. When tested against the in situ Standardized Precipitation Evapotranspiration Index (SPEI), VDSI with proper weights of three variables outperformed individual remote sensing drought indices. The county-level yields of the main crops in the study area from 2001 to 2010 were also used to validate the VDSI. The correlation analysis between the yield data and the VDSI data during the crop growing season was performed, and its results showed that VDSI during the crop reproductive growth period was strongly correlated with the variation of crop yield. It was proved that this index is a potential indicator for assessment of the spatial pattern of drought severity in northeast China.

scholarly journals Evaluation of Drought Indices Based on Thermal Remote Sensing of Evapotranspiration over the Continental United States

2011 ◽  
Vol 24 (8) ◽  
pp. 2025-2044 ◽  
Author(s):  
Martha C. Anderson ◽  
Christopher Hain ◽  
Brian Wardlow ◽  
Agustin Pimstein ◽  
John R. Mecikalski ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Drought Index ◽  
Temporal Correlation ◽  
Meteorological Drought ◽  
Rainfall Data ◽  
Added Value ◽  
Stress Index ◽  
Drought Indices ◽  
Shallow Water Table

Abstract The reliability of standard meteorological drought indices based on measurements of precipitation is limited by the spatial distribution and quality of currently available rainfall data. Furthermore, they reflect only one component of the surface hydrologic cycle, and they cannot readily capture nonprecipitation-based moisture inputs to the land surface system (e.g., irrigation) that may temper drought impacts or variable rates of water consumption across a landscape. This study assesses the value of a new drought index based on remote sensing of evapotranspiration (ET). The evaporative stress index (ESI) quantifies anomalies in the ratio of actual to potential ET (PET), mapped using thermal band imagery from geostationary satellites. The study investigates the behavior and response time scales of the ESI through a retrospective comparison with the standardized precipitation indices and Palmer drought index suite, and with drought classifications recorded in the U.S. Drought Monitor for the 2000–09 growing seasons. Spatial and temporal correlation analyses suggest that the ESI performs similarly to short-term (up to 6 months) precipitation-based indices but can be produced at higher spatial resolution and without requiring any precipitation data. Unique behavior is observed in the ESI in regions where the evaporative flux is enhanced by moisture sources decoupled from local rainfall: for example, in areas of intense irrigation or shallow water table. Normalization by PET serves to isolate the ET signal component responding to soil moisture variability from variations due to the radiation load. This study suggests that the ESI is a useful complement to the current suite of drought indicators, with particular added value in parts of the world where rainfall data are sparse or unreliable.

scholarly journals Practical Applications of a Multisensor UAV Platform Based on Multispectral, Thermal and RGB High Resolution Images in Precision Viticulture

Agriculture ◽  
2018 ◽  
Vol 8 (7) ◽  
pp. 116 ◽  
Author(s):  
Alessandro Matese ◽  
Salvatore Di Gennaro
Keyword(s):  
Water Stress ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Stress Index ◽  
Crop Water Stress Index ◽  
Practical Applications ◽  
Water Stress Index ◽  
Sensing Applications ◽  
Wide Range ◽  
Precision Viticulture

High spatial ground resolution and highly flexible and timely control due to reduced planning time are the strengths of unmanned aerial vehicle (UAV) platforms for remote sensing applications. These characteristics make them ideal especially in the medium–small agricultural systems typical of many Italian viticulture areas of excellence. UAV can be equipped with a wide range of sensors useful for several applications. Numerous assessments have been made using several imaging sensors with different flight times. This paper describes the implementation of a multisensor UAV system capable of flying with three sensors simultaneously to perform different monitoring options. The intra-vineyard variability was assessed in terms of characterization of the state of vines vigor using a multispectral camera, leaf temperature with a thermal camera and an innovative approach of missing plants analysis with a high spatial resolution RGB camera. The normalized difference vegetation index (NDVI) values detected in different vigor blocks were compared with shoot weights, obtaining a good regression (R2 = 0.69). The crop water stress index (CWSI) map, produced after canopy pure pixel filtering, highlighted the homogeneous water stress areas. The performance index developed from RGB images shows that the method identified 80% of total missing plants. The applicability of a UAV platform to use RGB, multispectral and thermal sensors was tested for specific purposes in precision viticulture and was demonstrated to be a valuable tool for fast multipurpose monitoring in a vineyard.

scholarly journals CLASSIFICATION OF LISS IV IMAGERY USING DECISION TREE METHODS

2016 ◽  
Vol XLI-B8 ◽  
pp. 1061-1066 ◽  
Author(s):  
Amit Kumar Verma ◽  
P. K. Garg ◽  
K. S. Hari Prasad ◽  
V. K. Dadhwal
Keyword(s):  
Remote Sensing ◽  
Decision Tree ◽  
Vegetation Indices ◽  
Spectral Information ◽  
Main Concern ◽  
Crop Land ◽  
Sensing Applications ◽  
Crop Classification ◽  
Decision Tree Method ◽  
Tree Method

Image classification is a compulsory step in any remote sensing research. Classification uses the spectral information represented by the digital numbers in one or more spectral bands and attempts to classify each individual pixel based on this spectral information. Crop classification is the main concern of remote sensing applications for developing sustainable agriculture system. Vegetation indices computed from satellite images gives a good indication of the presence of vegetation. It is an indicator that describes the greenness, density and health of vegetation. Texture is also an important characteristics which is used to identifying objects or region of interest is an image. This paper illustrate the use of decision tree method to classify the land in to crop land and non-crop land and to classify different crops. In this paper we evaluate the possibility of crop classification using an integrated approach methods based on texture property with different vegetation indices for single date LISS IV sensor 5.8 meter high spatial resolution data. Eleven vegetation indices (NDVI, DVI, GEMI, GNDVI, MSAVI2, NDWI, NG, NR, NNIR, OSAVI and VI green) has been generated using green, red and NIR band and then image is classified using decision tree method. The other approach is used integration of texture feature (mean, variance, kurtosis and skewness) with these vegetation indices. A comparison has been done between these two methods. The results indicate that inclusion of textural feature with vegetation indices can be effectively implemented to produce classifiedmaps with 8.33% higher accuracy for Indian satellite IRS-P6, LISS IV sensor images.

Assessment of the State of the Territories of Closed Waste Disposal Facilities by Vegetation Indices

2021 ◽  
Vol 25 (9) ◽  
pp. 30-37
Author(s):  
N.N. Sliusar ◽  
A.P. Belousova ◽  
G.M. Batrakova ◽  
R.D. Garifzyanov ◽  
M. Huber-Humer ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Waste Disposal ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Cost Effective ◽  
Google Earth ◽  
The State ◽  
Landsat 8 ◽  
The Earth

The possibilities of using remote sensing of the Earth data to assess the formation of phytocenoses at reclaimed dumps and landfills are presented. The objects of study are landfills and dumps in the Perm Territory, which differed from each other in the types and timing of reclamation work. The state of the vegetation cover on the reclaimed and self-overgrowing objects was compared with the reference plots with naturally formed herbage of zonal meadow vegetation. The process of reclamation of the territory of closed landfills was assessed by the presence and homogeneity of the vegetation layer and by the values of the vegetation index NDVI. To identify the dynamics of changes in the vegetation cover, we used multi-temporal satellite images from the open resources of Google Earth and images in the visible and infrared ranges of the Landsat-5/TM and Landsat-8/OLI satellites. It is shown that the data of remote sensing of the Earth, in particular the analysis of vegetation indices, can be used to assess the dynamics of overgrowing of territories of reclaimed waste disposal facilities, as well as an additional and cost-effective method for monitoring the restoration of previously disturbed territories.

scholarly journals Can We Detect the Brownness or Greenness of the Congo Rainforest Using Satellite-Derived Surface Albedo? A Study on the Role of Aerosol Uncertainties

2019 ◽  
Vol 11 (5) ◽  
pp. 1410 ◽  
Author(s):  
Suman Moparthy ◽  
Dominique Carrer ◽  
Xavier Ceamanos
Keyword(s):  
Remote Sensing ◽  
Surface Albedo ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Satellite Observations ◽  
Atmospheric Effects ◽  
Forest Regions ◽  
Aerosol Emissions

The ability of spatial remote sensing in the visible domain to properly detect the slow transitions in the Earth’s vegetation is often a subject of debate. The reason behind this is that the satellite products often used to calculate vegetation indices such as surface albedo or reflectance, are not always correctly decontaminated from atmospheric effects. In view of the observed decline in vegetation over the Congo during the last decade, this study investigates how effectively satellite-derived variables can contribute to the answering of this question. In this study, we use two satellite-derived surface albedo products, three satellite-derived aerosol optical depth (AOD) products, two model-derived AOD products, and synthetic observations from radiative transfer simulations. The study discusses the important discrepancies (of up to 70%) found between these satellite surface albedo products in the visible domain over this region. We conclude therefore that the analysis of trends in vegetation properties based on satellite observations in the visible domain such as NDVI (normalized difference vegetation index), calculated from reflectance or albedo variables, is still quite questionable over tropical forest regions such as the Congo. Moreover, this study demonstrates that there is a significant increase (of up to 14%) in total aerosols within the last decade over the Congo. We note that if these changes in aerosol loads are not correctly taken into account in the retrieval of surface albedo, a greenness change of the surface properties (decrease of visible albedo) of around 8% could be artificially detected. Finally, the study also shows that neglecting strong aerosol emissions due to volcano eruptions could lead to an artificial increase of greenness over the Congo of more than 25% in the year of the eruptions and up to 16% during the 2–3 years that follow.

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