scholarly journals Combining of the H/A/Alpha and Freeman–Durden Polarization Decomposition Methods for Soil Moisture Retrieval from Full-Polarization Radarsat-2 Data

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Qiuxia Xie ◽  
Qingyan Meng ◽  
Linlin Zhang ◽  
Chunmei Wang ◽  
Qiao Wang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Retrieval ◽  
Decomposition Methods ◽  
Bare Soil ◽  
Growth Stages ◽  
Threshold Values ◽  
Sar Images ◽  
Decomposition Approach ◽  
Different Growth Stages

Soil moisture (SM) plays important roles in surface energy conversion, crop growth, environmental protection, and drought monitoring. As crops grow, the associated vegetation seriously affects the ability of satellites to retrieve SM data. Here, we collected such data at different growth stages of maize using Bragg and X-Bragg scattering models based on the Freeman–Durden polarization decomposition method. We used the H/A/Alpha polarization decomposition approach to extract accurate threshold values of decomposed scattering components. The results showed that the H and Alpha values of bare soil areas were lower and those of vegetated areas were higher. The threshold values of the three scattering components were 0.2–0.4 H and 7–24° Alpha for the surface scattering component, 0.6–0.9 H and 22–50° Alpha for the volume scattering component, and other values for the dihedral scattering component. The SM data retrieved (using the X-Bragg model) on June 27, 2014, were better than those retrieved at other maize growth stages and were thus associated with the minimum root-mean-square error value (0.028). The satellite-evaluated SM contents were in broad agreement with data measured in situ. Our algorithm thus improves the accuracy of SM data retrieval from synthetic-aperture radar (SAR) images.

scholarly journals Autofluorescence Is a Common Trait in Different Oceanic Fungi

2021 ◽  
Vol 7 (9) ◽  
pp. 709
Author(s):  
Eva Breyer ◽  
Markus Böhm ◽  
Magdalena Reitbauer ◽  
Chie Amano ◽  
Marilena Heitger ◽  
...  
Keyword(s):  
Marine Fungi ◽  
Physiological State ◽  
Excitation Wavelength ◽  
Growth Stages ◽  
Future Studies ◽  
Different Types ◽  
Widespread Phenomenon ◽  
Ubiquitous Presence ◽  
Different Growth Stages

Natural autofluorescence is a widespread phenomenon observed in different types of tissues and organisms. Depending on the origin of the autofluorescence, its intensity can provide insights on the physiological state of an organism. Fungal autofluorescence has been reported in terrestrial and human-derived fungal samples. Yet, despite the recently reported ubiquitous presence and importance of marine fungi in the ocean, the autofluorescence of pelagic fungi has never been examined. Here, we investigated the existence and intensity of autofluorescence in five different pelagic fungal isolates. Preliminary experiments of fungal autofluorescence at different growth stages and nutrient conditions were conducted, reflecting contrasting physiological states of the fungi. In addition, we analysed the effect of natural autofluorescence on co-staining with DAPI. We found that all the marine pelagic fungi that were studied exhibited autofluorescence. The intensity of fungal autofluorescence changed depending on the species and the excitation wavelength used. Furthermore, fungal autofluorescence varied depending on the growth stage and on the concentration of available nutrients. Collectively, our results indicate that marine fungi can be auto-fluorescent, although its intensity depends on the species and growth condition. Hence, oceanic fungal autofluorescence should be considered in future studies when fungal samples are stained with fluorescent probes (i.e., fluorescence in situ hybridization) since this could lead to misinterpretation of results.

scholarly journals The Modification of Difference Vegetation Index (DVI) in middle and late growing period of winter wheat and its application in soil moisture inversion

2019 ◽  
Vol 131 ◽  
pp. 01098
Author(s):  
Zhang Hong-wei ◽  
Huai-liang Chen ◽  
Fei-na Zha
Keyword(s):  
Soil Moisture ◽  
Winter Wheat ◽  
Vegetation Index ◽  
Vegetation Coverage ◽  
Growth Stages ◽  
Wheat Growth ◽  
Inversion Result ◽  
Growing Period ◽  
The Difference ◽  
Different Growth Stages

In the middle and late growing period of winter wheat, soil moisture is easily affected by saturation when using MODIS data to retrieve soil moisture. In this paper, in order to reduce the effect of the saturation caused by increasing vegetation coverage in middle and late stage of winter wheat, the Difference Vegetation Index (DVI) model was modified with different coefficients in different growth stages of winter wheat based on MODIS spectral data and LAI characteristics of variation. LAI was divided into three stages, LAI ≤ 1 < LAI ≤, 3 < LAI, and the adjusting coefficient of α=1, α=3, α=5, were taken to modifying the Difference Vegetation Index(DVI). The results show that the Modified Difference Vegetation Index (MDVIα) can effectively reduce the interference of saturation, and the inversion result of soil moisture in the middle and late period of winter wheat growth is obviously superior to the uncorrected inversion model of DVI.

scholarly journals A Refined Four-Stream Radiative Transfer Model for Row-Planted Crops

2020 ◽  
Vol 12 (8) ◽  
pp. 1290 ◽  
Author(s):  
Xu Ma ◽  
Tiejun Wang ◽  
Lei Lu
Keyword(s):  
Radiative Transfer ◽  
Computer Simulations ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Growth Stages ◽  
Canopy Reflectance ◽  
Transfer Equations ◽  
Radiative Transfer Equations ◽  
Different Growth Stages

In modeling the canopy reflectance of row-planted crops, neglecting horizontal radiative transfer may lead to an inaccurate representation of vegetation energy balance and further cause uncertainty in the simulation of canopy reflectance at larger viewing zenith angles. To reduce this systematic deviation, here we refined the four-stream radiative transfer equations by considering horizontal radiation through the lateral “walls”, considered the radiative transfer between rows, then proposed a modified four-stream (MFS) radiative transfer model using single and multiple scattering. We validated the MFS model using both computer simulations and in situ measurements, and found that the MFS model can be used to simulate crop canopy reflectance at different growth stages with an accuracy comparable to the computer simulations (RMSE < 0.002 in the red band, RMSE < 0.019 in NIR band). Moreover, the MFS model can be successfully used to simulate the reflectance of continuous (RMSE = 0.012) and row crop canopies (RMSE < 0.023), and therefore addressed the large viewing zenith angle problems in the previous row model based on four-stream radiative transfer equations. Our results demonstrate that horizontal radiation is an important factor that needs to be considered in modeling the canopy reflectance of row-planted crops. Hence, the refined four-stream radiative transfer model is applicable to the real world.

Exploring the use of machine-learning techniques to integrate ground- and remote sensing-based observations for efficient near-surface soil moisture mapping

2020 ◽  
Author(s):  
Sarah Schönbrodt-Stitt ◽  
Paolo Nasta ◽  
Nima Ahmadian ◽  
Markus Kurtenbach ◽  
Christopher Conrad ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Spatial Resolution ◽  
Surface Soil ◽  
Bare Soil ◽  
Small Scale ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
Campania Region

&lt;p&gt;Mapping near-surface soil moisture (&lt;em&gt;&amp;#952;&lt;/em&gt;) is of tremendous relevance for a broad range of environment-related disciplines and meteorological, ecological, hydrological and agricultural applications. Globally available products offer the opportunity to address &lt;em&gt;&amp;#952;&lt;/em&gt; in large-scale modelling with coarse spatial resolution such as at the landscape level. However, &lt;em&gt;&amp;#952;&lt;/em&gt; estimation at higher spatial resolution is of vital importance for many small-scale applications. Therefore, we focus our study on a small-scale catchment (MFC2) belonging to the &amp;#8220;Alento&amp;#8221; hydrological observatory, located in southern Italy (Campania Region). The goal of this study is to develop new machine-learning approaches to estimate high grid-resolution (about 17 m cell size) &lt;em&gt;&amp;#952;&lt;/em&gt; maps from mainly backscatter measurements retrieved from C-band Synthetic Aperture Radar (SAR) based on Sentinel-1 (S1) images and from gridded terrain attributes. Thus, a workflow comprising a total of 48 SAR-based &lt;em&gt;&amp;#952;&lt;/em&gt; patterns estimated for 24 satellite overpass dates (revisit time of 6 days) each with ascendant and descendent orbits will be presented. To enable for the mapping, SAR-based &lt;em&gt;&amp;#952;&lt;/em&gt; data was calibrated with in-situ measurements carried out with a portable device during eight measurement campaigns at time of satellite overpasses (four overpass days in total with each ascendant and descendent satellite overpasses per day in November 2018). After the calibration procedure, data validation was executed from November 10, 2018 till March 28, 2019 by using two stationary sensors monitoring &lt;em&gt;&amp;#952;&lt;/em&gt; at high-temporal (1-min recording time). The specific sensor locations reflected two contrasting field conditions, one bare soil plot (frequently kept clear, without disturbance of vegetation cover) and one non-bare soil plot (real-world condition). Point-scale ground observations of &lt;em&gt;&amp;#952;&lt;/em&gt; were compared to pixel-scale (17 m &amp;#215; 17 m), SAR-based &lt;em&gt;&amp;#952;&lt;/em&gt; estimated for those pixels corresponding to the specific positions of the stationary sensors. Mapping performance was estimated through the root mean squared error (RMSE). For a short-term time series of &lt;em&gt;&amp;#952;&lt;/em&gt; (Nov 2018) integrating 136 in situ, sensor-based &lt;em&gt;&amp;#952;&lt;/em&gt; (&lt;em&gt;&amp;#952;&lt;/em&gt;&lt;sub&gt;insitu&lt;/sub&gt;) and 74 gravimetric-based &lt;em&gt;&amp;#952;&lt;/em&gt; (&lt;em&gt;&amp;#952;&lt;/em&gt;&lt;sub&gt;gravimetric&lt;/sub&gt;) measurements during a total of eight S1 overpasses, mapping performance already proved to be satisfactory with RMSE=0.039 m&amp;#179;m&lt;sup&gt;-&lt;/sup&gt;&amp;#179; and R&amp;#178;=0.92, respectively with RMSE=0.041 m&amp;#179;m&lt;sup&gt;-&lt;/sup&gt;&amp;#179; and R&amp;#178;=0.91. First results further reveal that estimated satellite-based &lt;em&gt;&amp;#952;&lt;/em&gt; patterns respond to the evolution of rainfall. With our workflow developed and results, we intend to contribute to improved environmental risk assessment by assimilating the results into hydrological models (e.g., HydroGeoSphere), and to support future studies on combined ground-based and SAR-based &lt;em&gt;&amp;#952;&lt;/em&gt; retrieval for forested land (future missions operating at larger wavelengths e.g. NISARL-band, Biomass P-band sensors).&lt;/p&gt;

scholarly journals In Situ Nutritional Evaluation of Maize Varieties Harvested at Different Growth Stages

2020 ◽  
Vol 9 (3) ◽  
Author(s):  
Asim Faraz ◽  
Nasir Ali Tauqir ◽  
Rana Muhammad Bilal ◽  
Fayyaz Ahmad ◽  
Abdul Waheed
Keyword(s):  
Growth Stages ◽  
Nutritional Evaluation ◽  
Maize Varieties ◽  
Different Growth Stages

scholarly journals Changes of soil moisture during apple growth based on TVDI index

2021 ◽  
pp. 955-961
Author(s):  
Hui Kong ◽  
Dan Wu
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Vegetation Index ◽  
Growth Stages ◽  
Temporal Consistency ◽  
Vegetation Water Content ◽  
Content Index ◽  
Research Findings ◽  
Vegetation Water ◽  
Different Growth Stages

Based on MODIS data, soil moisture data and field survey data from 2014 to 2018, the consistency of temperature vegetation drought index (TVDL), normalized vegetation water content index (NDWL), vegetation water supply index (VSWI) and soil moisture at 15cm depth (SM) in apple growth in Fuxian county was investigated. Results showed that the spatial and temporal consistency between VSWI and SM calculated by the enhanced vegetation index (EVI) was best; the sensitivity of remote sensing indexes to soil moisture was different in different apple growth stages. The sensitivity of VSWI was the most obvious in different growth stages, and the sensitivity of soil moisture was higher than that of germination, flowering, fruit expansion and maturity. The research findings were consistent with the law of water demand in different growth stages of apple in Fuxian county and the characteristics of precipitation and drought in Fuxian county. The present results could provide a reference for soil moisture monitoring of apple growth by remote sensing. Bangladesh J. Bot. 50(3): 955-961, 2021 (September) Special

Mow-kill Regulation of Winter Cereals for Spring No-till Crop Production

1996 ◽  
Vol 10 (2) ◽  
pp. 247-252 ◽  
Author(s):  
Erik D. Wilkins ◽  
Robin R. Bellinder
Keyword(s):  
Soil Temperature ◽  
Crop Production ◽  
Dry Matter ◽  
Field Studies ◽  
Bare Soil ◽  
Growth Stages ◽  
Winter Cereals ◽  
No Till ◽  
Soil Temperatures ◽  
Different Growth Stages

Field studies determined the influence of developmental stage on mow-killing of winter wheat and rye. Both crops were clipped at either three or four different growth stages in 1992 and 1993. When mowed at first node, wheat biomass was 4350 and 1970 kg/ha in 1992 and 1993, respectively. At this stage, primary tiller apices were below 10 cm and regrowth was vigorous. Mowing prior to 75% heading consistently yielded more than 1000 kg/ha regrowth 8 wk later. Wheat cut after flowering produced 15 460 and 9160 kg/ha dry matter in 1992 and 1993, respectively, but less than 30 kg/ha total regrowth. At first and second node, rye produced 4440 and 1800 kg/ha biomass in 1992 and 1993. When mowed belore boot, more than 50% of the total rye biomass was due to regrowth. Rye mowed at boot yielded 6940 and 3740 kg/ha in 1992 and 1993 respectively, and regrowth measured 780 and 910 kg/ha 8 wk later. Mowing after flowering resulted in no measurable regrowth. Soil temperature and PAR were affected by mow-kill date and biomass. Biomass at first mowings (first and second node) in both wheat and rye reduced seasonal soil temperatures 3.5 C compared to bare soil temperatures; while biomass at kernal-filling lowered temperatures 6.0 C. Measured 8 wk after mowing, first node mowings absorbed between 55% and 70% PAR, while plants mowed at kernal-filling absorbed less than 5%.

scholarly journals Using SAR-Derived Vegetation Descriptors in a Water Cloud Model to Improve Soil Moisture Retrieval

2018 ◽  
Vol 10 (9) ◽  
pp. 1370 ◽  
Author(s):  
Junhua Li ◽  
Shusen Wang
Keyword(s):  
Soil Moisture ◽  
Vegetation Index ◽  
Cloud Model ◽  
Data Availability ◽  
Agricultural Crop ◽  
Sar Images ◽  
Area Index ◽  
Water Cloud ◽  
The Impact

The water cloud model (WCM) is a widely used radar backscatter model applied to SAR images to retrieve soil moisture over vegetated areas. The WCM needs vegetation descriptors to account for the impact of vegetation on SAR backscatter. The commonly used vegetation descriptors in WCM, such as Leaf Area Index (LAI) and Normalized Difference Vegetation Index (NDVI), are sometimes difficult to obtain due to the constraints in data availability in in-situ measurements or weather dependency in optical remote sensing. To improve soil moisture retrieval, this study investigates the feasibility of using all-weather SAR derived vegetation descriptors in WCM. The in-situ data observed at an agricultural crop region south of Winnipeg in Canada, RapidEye optical images and dual-polarized Radarsat-2 SAR images acquired in growing season were used for WCM model calibration and test. Vegetation descriptors studied include HV polarization backscattering coefficient ( σ H V ° ) and Radar Vegetation Index (RVI) derived from SAR imagery, and NDVI derived from optical imagery. The results show that σ H V ° achieved similar results as NDVI but slightly better than RVI, with a root mean square error of 0.069 m3/m3 and a correlation coefficient of 0.59 between the retrieved and observed soil moisture. The use of σ H V ° can overcome the constraints of the commonly used vegetation descriptors and reduce additional data requirements (e.g., NDVI from optical sensors) in WCM, thus improving soil moisture retrieval and making WCM feasible for operational use.

scholarly journals Evaluating the effect of in-situ rainwater harvesting techniques on maize production in moisture stress areas of humbo woreda, wolaita zone, southern Ethiopia

2020 ◽  
Vol 10 (1) ◽  
pp. 71-79
Author(s):  
W Naba ◽  
A Moges ◽  
A Gebremichael
Keyword(s):  
Soil Moisture ◽  
Grain Yield ◽  
Rainwater Harvesting ◽  
Block Design ◽  
Growing Season ◽  
Southern Ethiopia ◽  
Growth Stages ◽  
Dry Spell ◽  
Harvesting Techniques

The study was conducted to investigate the effect of different in-situ water harvesting structures as soil moisture conservation techniques under maize crop production in Abela Sippa kebele Wolaita zone, Ethiopia where rainfall variation is affecting agriculture with prolonged dry spells during critical crop growth stages. The experiment was laid out in a Randomized Complete Block Design, with three replications and four treatments. The four treatments used in the study were; Control, Targa, Tie-ridge and Zai pits. Findings from this study revealed that maize grain yield and yield components, such as, grain yield, dry matter biomass, and cob length were highly significant (p<0.05) on Targa. Soil-moisture content over the crop growing season at dry spell periods was significantly higher in Targa and Tie ridges than the control. Maize yield of (7150 kg ha-1), (6190 kg ha-1), (4500 kg ha-1) and (4900 kg ha-1) was obtained from Targa, Tie ridge, Zai pits and Control, respectively. Targa and Tie ridge treatments recorded higher net returns (29712 and 25164 kg ha-1) than Control (20370 kg ha-1) and Zai (14350 kg ha-1) treatments. The results revealed that the in-situ rainwater harvesting techniques could play great role in improving crop yield in dry periods. However, the utilization of the technology is surrounded by various constraints. The major constraints include labour, cost, lack of knowledge and crops planted on bunds. The findings suggest that Targa structure improved water availability during the growing season, thereby protecting crops from dry periods and it needs minimum cost, less labor power ,and easily constructed by local farmers (not require complicated knowledge). Int. J. Agril. Res. Innov. Tech. 10(1): 71-79, June 2020

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