scholarly journals Evaluation of Normalized Difference Water Index as a Tool for Monitoring Pasture Seasonal and Inter-Annual Variability in a Mediterranean Agro-Silvo-Pastoral System

Water ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 62 ◽  
Author(s):  
João Serrano ◽  
Shakib Shahidian ◽  
José Marques da Silva
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Hydrological Cycle ◽  
Climatic Factors ◽  
Vegetation Development ◽  
Pastoral System ◽  
Water Index ◽  
Annual Variability ◽  
Normalized Difference Water Index

Extensive animal production in Iberian Peninsula is based on pastures, integrated within the important agro-silvo-pastoral system, named “montado” in Portugal and “dehesa” in Spain. Temperature and precipitation are the main driving climatic factors affecting agricultural productivity and, in dryland pastures, the hydrological cycle of soil, identified by soil moisture content (SMC), is the main engine of the vegetation development. The objective of this work was to evaluate the normalized difference water index (NDWI) based on Sentinel-2 imagery as a tool for monitoring pasture seasonal dynamics and inter-annual variability in a Mediterranean agro-silvo-pastoral system. Forty-one valid NDWI records were used between January and June 2016 and between January 2017 and June 2018. The 2.3 ha experimental field is located within the “Mitra” farm, in the South of Portugal. Soil moisture content, pasture moisture content (PMC), pasture surface temperature (Tir), pasture biomass productivity and pasture quality degradation index (PQDI) were evaluated in 12 satellite pixels (10 m × 10 m). The results show significant correlations (p < 0.01) between NDWI and: (i) SMC (R2 = 0.7548); (ii) PMC (R2 = 0.8938); (iii) Tir (R2 = 0.5428); (iv) biomass (R2 = 0.7556); and (v) PQDI (R2 = 0.7333). These findings suggest that satellite-derived NDWI can be used in site-specific management of “montado” ecosystem to support farmers’ decision making.

scholarly journals Role of Climatic Factors In The Toxicity of Fipronil Toward Earthworms In Two Tropical Soils: Effects of Increased Temperature And Reduced Soil Moisture Content

2021 ◽  
Author(s):  
Thuanne Braúlio Hennig ◽  
Paulo Roger Lopes Alves ◽  
Felipe Ogliari Bandeira ◽  
Liziara da Costa Cabrera ◽  
Jonas Simon Dugatto ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Climatic Factors ◽  
Hazard Quotient ◽  
Tropical Soils ◽  
Effect Of Temperature ◽  
Water Holding ◽  
Water Contents

Abstract The aim of this study was to assess the effect of temperature on the toxicity of fipronil toward earthworms (Eisenia andrei) in two Brazilian soils (Entisol and Oxisol) with contrasting textures. In the case of Entisol, the influence of the soil moisture content on the toxicity was also investigated. Earthworms were exposed for 56 days to soils spiked with increasing concentrations of fipronil under scenarios with different combinations of temperature (20, 25 and 27 ºC) and soil moisture content (60 and 30% of water holding capacity (WHC) for Entisol and 60% WHC for Oxisol). The number of juveniles produced was taken as the endpoint and a risk assessment was performed based on the hazard quotient (HQ). In Entisol, at 60% WHC the fipronil toxicity decreased at 27 ºC compared with the other temperatures tested (EC50 = 52.58, 48.48 and 110 mg kg-1 for 20, 25 and 27 ºC, respectively). In the case of Oxisol at 60% WHC, the fipronil toxicity increased at 27 ºC compared with other temperatures (EC50 = 277.57, 312.87 and 39.89 mg kg-1 at 20, 25 and 27 ºC, respectively). An increase in fipronil toxicity was also observed with a decrease in soil moisture content in Entisol at 27 ºC (EC50 = 27.95 and 110 mg kg-1 for 30% and 60% WHC, respectively). The risk of fipronil was only significant at 27 ºC in Entisol and Oxisol with water contents of 30% and 60% WHC, respectively, revealing that higher temperatures can increase the risk of fipronil toxicity toward earthworms. The results reported herein show that soil properties associated with climatic shifts could enhance the ecotoxicological effects and risk of fipronil for earthworms, depending on the type of soil.

Simulation of soil moisture and other components of the hydrological cycle using a water budget approach

1996 ◽  
Vol 76 (2) ◽  
pp. 133-142 ◽  
Author(s):  
O. O. Akinremi ◽  
S. M. McGinn ◽  
A. G. Barr
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Crop Production ◽  
Soil Moisture Content ◽  
Hydrological Cycle ◽  
Root Zone ◽  
Drought Monitoring ◽  
Published Data ◽  
Snowmelt Runoff ◽  
Modified Model

Accurate simulation of soil moisture content at any time of the year is important to agriculture in dry regions due to the vital role soil moisture plays in crop production. In certain applications such as drought monitoring, other components of the hydrologic cycle such as runoff, snowmelt runoff, deep drainage and evaporative loss must also be accurately estimated. The goal of this study was to develop a model which accurately accounts for the major components of the hydrological cycle in order to simulate soil moisture content for drought monitoring and crop yield prediction. The versatile soil moisture budget (VSMB) was evaluated and modified to improve the prediction of soil moisture content runoff from rainfall and snowmelt, drainage of moisture out of the root zone and soil surface temperature. The modified components of the model were independently tested and validated using field and published data. The soil moisture output from our modified model correlated well with observed changes in soil moisture during the growing season under wheat, fallow and over the winter. The moisture content of the surface layer was simulated with greater accuracy than that of deeper layers. The soil moisture simulated by the modified model compares better with measured values than that simulated using the original version of the VSMB. The simulation of snow dynamics at Lethbridge, a chinook-dominated region, gave credibility to the snowmelt runoff predicted by the model. Key words: Soil moisture, modelling, runoff, evapotranspiration, snowmelt, Canadian prairies

scholarly journals Potential Rainwater Harvesting Improvement Using Advanced Remote Sensing Applications

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Mohamed Elhag ◽  
Jarbou A. Bahrawi
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Rainwater Harvesting ◽  
Hydrological Cycle ◽  
Landsat 8 ◽  
Sensing Applications ◽  
Average Distribution ◽  
Remote Sensing Applications

The amount of water on earth is the same and only the distribution and the reallocation of water forms are altered in both time and space. To improve the rainwater harvesting a better understanding of the hydrological cycle is mandatory. Clouds are major component of the hydrological cycle; therefore, clouds distribution is the keystone of better rainwater harvesting. Remote sensing technology has shown robust capabilities in resolving challenges of water resource management in arid environments. Soil moisture content and cloud average distribution are essential remote sensing applications in extracting information of geophysical, geomorphological, and meteorological interest from satellite images. Current research study aimed to map the soil moisture content using recent Landsat 8 images and to map cloud average distribution of the corresponding area using 59 MERIS satellite imageries collected from January 2006 to October 2011. Cloud average distribution map shows specific location in the study area where it is always cloudy all the year and the site corresponding soil moisture content map came in agreement with cloud distribution. The overlay of the two previously mentioned maps over the geological map of the study area shows potential locations for better rainwater harvesting.

Analysis on Soil Moisture Content in the Middle Reaches of the Yellow River

2011 ◽  
Vol 28 (1) ◽  
pp. 85-91 ◽  
Author(s):  
Run-chun LI ◽  
Xiu-zhi ZHANG ◽  
Li-hua WANG ◽  
Xin-yan LV ◽  
Yuan GAO
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Yellow River ◽  
The Yellow River

Distribution of soil moisture content and its effect on the potential growth of the over-story species in North-East Chalkidiki

2001 ◽  
Vol 66 ◽  
Author(s):  
M. Aslanidou ◽  
P. Smiris
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Soil Depth ◽  
Moisture Distribution ◽  
Taxus Baccata ◽  
Mixed Stands ◽  
Potential Growth ◽  
North East ◽  
Soil Moisture Distribution

This  study deals with the soil moisture distribution and its effect on the  potential growth and    adaptation of the over-story species in north-east Chalkidiki. These  species are: Quercus    dalechampii Ten, Quercus  conferta Kit, Quercus  pubescens Willd, Castanea  sativa Mill, Fagus    moesiaca Maly-Domin and also Taxus baccata L. in mixed stands  with Fagus moesiaca.    Samples of soil, 1-2 kg per 20cm depth, were taken and the moisture content  of each sample    was measured in order to determine soil moisture distribution and its  contribution to the growth    of the forest species. The most important results are: i) available water  is influenced by the soil    depth. During the summer, at a soil depth of 10 cm a significant  restriction was observed. ii) the    large duration of the dry period in the deep soil layers has less adverse  effect on stands growth than in the case of the soil surface layers, due to the fact that the root system mainly spreads out    at a soil depth of 40 cm iii) in the beginning of the growing season, the  soil moisture content is    greater than 30 % at a soil depth of 60 cm, in beech and mixed beech-yew  stands, is 10-15 % in    the Q. pubescens  stands and it's more than 30 % at a soil depth of 60 cm in Q. dalechampii    stands.

scholarly journals Mapping barrier island soil moisture using a radiative transfer model of hyperspectral imagery from an unmanned aerial system

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rehman S. Eon ◽  
Charles M. Bachmann
Keyword(s):  
Soil Moisture ◽  
Radiative Transfer ◽  
Moisture Content ◽  
Layer Thickness ◽  
Soil Moisture Content ◽  
Hyperspectral Imagery ◽  
Barrier Island ◽  
Water Layer ◽  
Radiative Transfer Model ◽  
Transfer Model

AbstractThe advent of remote sensing from unmanned aerial systems (UAS) has opened the door to more affordable and effective methods of imaging and mapping of surface geophysical properties with many important applications in areas such as coastal zone management, ecology, agriculture, and defense. We describe a study to validate and improve soil moisture content retrieval and mapping from hyperspectral imagery collected by a UAS system. Our approach uses a recently developed model known as the multilayer radiative transfer model of soil reflectance (MARMIT). MARMIT partitions contributions due to water and the sediment surface into equivalent but separate layers and describes these layers using an equivalent slab model formalism. The model water layer thickness along with the fraction of wet surface become parameters that must be optimized in a calibration step, with extinction due to water absorption being applied in the model based on equivalent water layer thickness, while transmission and reflection coefficients follow the Fresnel formalism. In this work, we evaluate the model in both field settings, using UAS hyperspectral imagery, and laboratory settings, using hyperspectral spectra obtained with a goniometer. Sediment samples obtained from four different field sites representing disparate environmental settings comprised the laboratory analysis while field validation used hyperspectral UAS imagery and coordinated ground truth obtained on a barrier island shore during field campaigns in 2018 and 2019. Analysis of the most significant wavelengths for retrieval indicate a number of different wavelengths in the short-wave infra-red (SWIR) that provide accurate fits to measured soil moisture content in the laboratory with normalized root mean square error (NRMSE)< 0.145, while independent evaluation from sequestered test data from the hyperspectral UAS imagery obtained during the field campaign obtained an average NRMSE = 0.169 and median NRMSE = 0.152 in a bootstrap analysis.

scholarly journals Estimating Agricultural Soil Moisture Content through UAV-Based Hyperspectral Images in the Arid Region

2021 ◽  
Vol 13 (8) ◽  
pp. 1562
Author(s):  
Xiangyu Ge ◽  
Jianli Ding ◽  
Xiuliang Jin ◽  
Jingzhe Wang ◽  
Xiangyue Chen ◽  
...  
Keyword(s):  
Data Mining ◽  
Soil Moisture ◽  
Moisture Content ◽  
Hyperspectral Imaging ◽  
Fractional Order ◽  
Arid Regions ◽  
Soil Moisture Content ◽  
Order Derivative ◽  
Imaging Data ◽  
Strategy I

Unmanned aerial vehicle (UAV)-based hyperspectral remote sensing is an important monitoring technology for the soil moisture content (SMC) of agroecological systems in arid regions. This technology develops precision farming and agricultural informatization. However, hyperspectral data are generally used in data mining. In this study, UAV-based hyperspectral imaging data with a resolution o 4 cm and totaling 70 soil samples (0–10 cm) were collected from farmland (2.5 × 104 m2) near Fukang City, Xinjiang Uygur Autonomous Region, China. Four estimation strategies were tested: the original image (strategy I), first- and second-order derivative methods (strategy II), the fractional-order derivative (FOD) technique (strategy III), and the optimal fractional order combined with the optimal multiband indices (strategy IV). These strategies were based on the eXtreme Gradient Boost (XGBoost) algorithm, with the aim of building the best estimation model for agricultural SMC in arid regions. The results demonstrated that FOD technology could effectively mine information (with an absolute maximum correlation coefficient of 0.768). By comparison, strategy IV yielded the best estimates out of the methods tested (R2val = 0.921, RMSEP = 1.943, and RPD = 2.736) for the SMC. The model derived from the order of 0.4 within strategy IV worked relatively well among the different derivative methods (strategy I, II, and III). In conclusion, the combination of FOD technology and the optimal multiband indices generated a highly accurate model within the XGBoost algorithm for SMC estimation. This research provided a promising data mining approach for UAV-based hyperspectral imaging data.

scholarly journals A GNSS-IR Method for Retrieving Soil Moisture Content from Integrated Multi-Satellite Data That Accounts for the Impact of Vegetation Moisture Content

2021 ◽  
Vol 13 (13) ◽  
pp. 2442
Author(s):  
Jichao Lv ◽  
Rui Zhang ◽  
Jinsheng Tu ◽  
Mingjie Liao ◽  
Jiatai Pang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Satellite Data ◽  
Vegetation Index ◽  
Soil Moisture Content ◽  
Support Vector ◽  
Observation Data ◽  
Multipath Effect ◽  
Mars Model ◽  
The Impact

There are two problems with using global navigation satellite system-interferometric reflectometry (GNSS-IR) to retrieve the soil moisture content (SMC) from single-satellite data: the difference between the reflection regions, and the difficulty in circumventing the impact of seasonal vegetation growth on reflected microwave signals. This study presents a multivariate adaptive regression spline (MARS) SMC retrieval model based on integrated multi-satellite data on the impact of the vegetation moisture content (VMC). The normalized microwave reflection index (NMRI) calculated with the multipath effect is mapped to the normalized difference vegetation index (NDVI) to estimate and eliminate the impact of VMC. A MARS model for retrieving the SMC from multi-satellite data is established based on the phase shift. To examine its reliability, the MARS model was compared with a multiple linear regression (MLR) model, a backpropagation neural network (BPNN) model, and a support vector regression (SVR) model in terms of the retrieval accuracy with time-series observation data collected at a typical station. The MARS model proposed in this study effectively retrieved the SMC, with a correlation coefficient (R2) of 0.916 and a root-mean-square error (RMSE) of 0.021 cm3/cm3. The elimination of the vegetation impact led to 3.7%, 13.9%, 11.7%, and 16.6% increases in R2 and 31.3%, 79.7%, 49.0%, and 90.5% decreases in the RMSE for the SMC retrieved by the MLR, BPNN, SVR, and MARS model, respectively. The results demonstrated the feasibility of correcting the vegetation changes based on the multipath effect and the reliability of the MARS model in retrieving the SMC.

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