Biparabolic NDVI-Ts Space and Soil Moisture Remote Sensing in an Arid and Semi arid Area

2015 ◽  
Vol 41 (3) ◽  
pp. 159-169 ◽  
Author(s):  
Ying Liu ◽  
Lixin Wu ◽  
Hui Yue
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Arid Area ◽  
Semi Arid

Spatiotemporal monitoring of surface soil moisture using optical remote sensing data: a case study in a semi-arid area

2018 ◽  
Vol 65 (3) ◽  
pp. 481-499 ◽  
Author(s):  
Rida Khellouk ◽  
Ahmed Barakat ◽  
Abdelghani Boudhar ◽  
Rachid Hadria ◽  
Hayat Lionboui ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Surface Soil ◽  
Remote Sensing Data ◽  
Arid Area ◽  
Optical Remote Sensing ◽  
Surface Soil Moisture ◽  
Sensing Data ◽  
Semi Arid

scholarly journals Suitability Evaluation of Typical Drought Index in Soil Moisture Retrieval and Monitoring Based on Optical Images

2020 ◽  
Vol 12 (16) ◽  
pp. 2587
Author(s):  
Yan Nie ◽  
Ying Tan ◽  
Yuqin Deng ◽  
Jing Yu
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Arid Regions ◽  
Drought Index ◽  
Dynamic Monitoring ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Rapid Acquisition ◽  
Semi Arid ◽  
Soil Moisture Inversion

As a basic agricultural parameter in the formation, transformation, and consumption of surface water resources, soil moisture has a very important influence on the vegetation growth, agricultural production, and healthy operation of regional ecosystems. The Aksu river basin is a typical semi-arid agricultural area which seasonally suffers from water shortage. Due to the lack of knowledge on soil moisture change, the water management and decision-making processes have been a difficult issue for local government. Therefore, soil moisture monitoring by remote sensing became a reasonable way to schedule crop irrigation and evaluate the irrigation efficiency. Compared to in situ measurements, the use of remote sensing for the monitoring of soil water content is convenient and can be repetitively applied over a large area. To verify the applicability of the typical drought index to the rapid acquisition of soil moisture in arid and semi-arid regions, this study simulated, compared, and validated the effectiveness of soil moisture inversion. GF-1 WFV images, Landsat 8 OLI images, and the measured soil moisture data were used to determine the Perpendicular Drought Index (PDI), the Modified Perpendicular Drought Index (MPDI), and the Vegetation Adjusted Perpendicular Drought Index (VAPDI). First, the determination coefficients of the correlation analyses on the PDI, MPDI, VAPDI, and measured soil moisture in the 0–10, 10–20, and 20–30 cm depth layers based on the GF-1 WFV and Landsat 8 OLI images were good. Notably, in the 0–10 cm depth layers, the average determination coefficient was 0.68; all models met the accuracy requirements of soil moisture inversion. Both indicated that the drought indices based on the Near Infrared (NIR)-Red spectral space derived from the optical remote sensing images are more sensitive to soil moisture near the surface layer; however, the accuracy of retrieving the soil moisture in deep layers was slightly lower in the study area. Second, in areas of vegetation coverage, MPDI and VAPDI had a higher inversion accuracy than PDI. To a certain extent, they overcame the influence of mixed pixels on the soil moisture spectral information. VAPDI modified by Perpendicular Vegetation Index (PVI) was not susceptible to vegetation saturation and, thus, had a higher inversion accuracy, which makes it performs better than MPDI’s in vegetated areas. Third, the spatial heterogeneity of the soil moisture retrieved by the GF-1 WFV and Landsat 8 OLI image were similar. However, the GF-1 WFV images were more sensitive to changes in the soil moisture, which reflected the actual soil moisture level covered by different vegetation. These results provide a practical reference for the dynamic monitoring of surface soil moisture, obtaining agricultural information and agricultural condition parameters in arid and semi-arid regions.

Effects of plant density and tillage on growth and grain yield of sorghum (Sorghum bicolor (L.) Moench) under dryland conditions in a semi-arid area in Ethiopia

1987 ◽  
Vol 108 (2) ◽  
pp. 395-401 ◽  
Author(s):  
D. C. Adjei-Twum
Keyword(s):  
Soil Moisture ◽  
Grain Yield ◽  
Plant Density ◽  
Grain Filling ◽  
Arid Area ◽  
Tillage Practices ◽  
Available Soil Moisture ◽  
Cropping Seasons ◽  
Sorghum Bicolor L ◽  
Semi Arid

SummaryEffects of plant density ranging from 44444 to 133333 plants/ha and tillage practices (planting in flat beds (control), in the furrows of open ridges, on the top of open ridges, in the furrows of tie-ridges and on the top of tie-ridges) on growth and grain yield of sorghum were investigated at Kobo, a typical semi-arid area in Ethiopia, during 1980, 1981 and 1982 cropping seasons. Plant growth was limited in the flat beds because they were likely to be deficient in soil moisture and sometimes in the tie-ridging treatments, due to waterlogging. However, planting on the top of tie-ridges produced 1·6, 0·4 and 1·8 t/ha more yield than in the flat beds, the method commonly practised by the Kobo farmers, during 1980, 1981 and 1982 respectively. In all seasons, the effect of plant density did not show marked differences. The plants rather adjusted their reproductive growth and development to the seasonal rainfall and presumably to the available soil moisture at the grain-filling periods. It was concluded that the highest plant density did not reach the optimum for the area. Planting sorghum on the top of tie-ridges is recommended.

scholarly journals Machine Learning to Estimate Surface Soil Moisture from Remote Sensing Data

Water ◽  
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.

scholarly journals Investigating temporal field sampling strategies for site-specific calibration of three soil moisture–neutron intensity parameterisation methods

2015 ◽  
Vol 19 (7) ◽  
pp. 3203-3216 ◽  
Author(s):  
J. Iwema ◽  
R. Rosolem ◽  
R. Baatz ◽  
T. Wagener ◽  
H. R. Bogena
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Satellite Remote Sensing ◽  
Cosmic Ray ◽  
Sampling Strategies ◽  
Site Specific ◽  
Neutron Intensity ◽  
Soil Wetness ◽  
Semi Arid

Abstract. The Cosmic-Ray Neutron Sensor (CRNS) can provide soil moisture information at scales relevant to hydrometeorological modelling applications. Site-specific calibration is needed to translate CRNS neutron intensities into sensor footprint average soil moisture contents. We investigated temporal sampling strategies for calibration of three CRNS parameterisations (modified N0, HMF, and COSMIC) by assessing the effects of the number of sampling days and soil wetness conditions on the performance of the calibration results while investigating actual neutron intensity measurements, for three sites with distinct climate and land use: a semi-arid site, a temperate grassland, and a temperate forest. When calibrated with 1 year of data, both COSMIC and the modified N0 method performed better than HMF. The performance of COSMIC was remarkably good at the semi-arid site in the USA, while the N0mod performed best at the two temperate sites in Germany. The successful performance of COSMIC at all three sites can be attributed to the benefits of explicitly resolving individual soil layers (which is not accounted for in the other two parameterisations). To better calibrate these parameterisations, we recommend in situ soil sampled to be collected on more than a single day. However, little improvement is observed for sampling on more than 6 days. At the semi-arid site, the N0mod method was calibrated better under site-specific average wetness conditions, whereas HMF and COSMIC were calibrated better under drier conditions. Average soil wetness condition gave better calibration results at the two humid sites. The calibration results for the HMF method were better when calibrated with combinations of days with similar soil wetness conditions, opposed to N0mod and COSMIC, which profited from using days with distinct wetness conditions. Errors in actual neutron intensities were translated to average errors specifically to each site. At the semi-arid site, these errors were below the typical measurement uncertainties from in situ point-scale sensors and satellite remote sensing products. Nevertheless, at the two humid sites, reduction in uncertainty with increasing sampling days only reached typical errors associated with satellite remote sensing products. The outcomes of this study can be used by researchers as a CRNS calibration strategy guideline.

Quantification of irrigation water using remote sensing of soil moisture in a semi-arid region

2019 ◽  
Vol 231 ◽  
pp. 111226 ◽  
Author(s):  
Ehsan Jalilvand ◽  
Masoud Tajrishy ◽  
Sedigheh Alsadat Ghazi Zadeh Hashemi ◽  
Luca Brocca
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Irrigation Water ◽  
Arid Region ◽  
Semi Arid Region ◽  
Semi Arid

Drought monitoring over Semi-Humid rain-fed winter wheat region in northwest China using remote sensing data from 1981 to 2010

2020 ◽  
Author(s):  
Ni Guo ◽  
Wei Wang ◽  
Lijuan Wang
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Winter Wheat ◽  
Natural Disasters ◽  
Drought Index ◽  
Growth Period ◽  
Drought Monitoring ◽  
Study Region ◽  
The World ◽  
Semi Arid

<p>Drought is a widespread climate phenomenon throughout the world, as well as one of the natural disasters that seriously impact agricultural. Losses caused by drought in China reach up to about 15 percent of the all losses caused by natural disasters every year. Therefore, to monitoring the drought real-time and effectively, to improving the level of drought monitoring and early warning capacity have important significance to defense drought effectively. Satellite remote sensing technique of drought developed rapidly and had been one of the significant methods that widely used throughout the world since 1980s. Studies have shown that remote sensing drought index, especially the Vegetation drought Index (VIs) is the most suitable one that can be used in semi-arid and semi-humid climate region. We choose semi-arid region of Longdong rain-fed agriculture area in the northwest of Gansu Province as the study area, which is the most frequency area in China that drought occurs. To estimate the drought characteristics from 1981 to 2010, monthly NDVI data, the VCI and AVI index data got from NDVI data, the Comprehensive meteorological drought Index (CI) data during this period, and soil moisture observation data in 20 cm were used. Results show that:</p><ol><li>The frequency and severity of drought in Longdong region appeared a low-high-low trend from 1981 to 2010. 1980s showed a lowest value, 1990s showed a highest value and 2000s showed a falling trend in the frequency and severity.</li> <li>AVI and VCI showed a good consistency of drought monitoring together with CI and soil moisture, but a higher volatility and lagged behind for 1 month.</li> <li>A Winter Wheat Drought Index (WWDI) was proposed through the analyses of inter-annual NDVI data during the winter wheat growth period and it represents the drought degree in the whole growth period commendably. Thus provide an efficient index to the winter wheat disaster assessment.</li> <li>The winter wheat drought degree in the study region from 1981 to 2010 was obtained using WWDI data. The most drought years got from WWDI data were 1995, 2000, 1992, 1996 and 1997, which displayed a very high consistency with the actual disaster situations.</li> </ol>

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