Spectral reflectivity variations of Brassica napus depending on degree of soil moisture

2021 ◽  
Author(s):  
Yury Davidovich
Keyword(s):  
Optical Properties ◽  
Soil Moisture ◽  
Brassica Napus ◽  
Spectral Reflectance ◽  
Multispectral Image ◽  
Anthropogenic Factors ◽  
Landsat 8 ◽  
Reflectance Function ◽  
Spectrometric Measurements

<p>Studying of the optical properties of agricultural vegetation is one of the methods for plants condition estimation, prediction of their development and changes influenced by natural and anthropogenic factors.</p><p>The work is dedicated to the investigation of spectral reflectance function of agricultural <em>Brassica napus</em> taking into account the degree of soil moisture. When most of the agricultural lands in Belarus are covered with vegetation in summer, employing the optical properties of agricultural vegetation for deciphering the soil depends on the degree of soil moisture. Insufficient numbers of days in year when the soil is not covered by vegetation or is in a plowed state requires in-situ optical measurements, because there are more than 50 % cloudy conditions in the year, especially in spring and autumn time.</p><p>The study has been carried out near the Minsk 11.06.2020 (53.837004º N, 27.487597º E) in clear, cloudless day. The relief for investigated field is hilly-ridge, characterized by a predominance of elevation marks from 250 to 300 m and it is actively sown field. During the spectrometric measurements, the field has been sown with <em>Brassica napus</em> in the phenological phase of pod formation.</p><p>When studying the spectral reflectance of <em>Brassica napus</em>, in-situ spectrometric measurements and analysis of a multispectral image have been carried out. Spectrometric measurements have been carried out by FSR-02 spectrometer (spectral range 400-900 nm, spectral resolution 4.3 nm) aiming to retrieve spectral reflectance function.</p><p>The normalized vegetation index NDVI has been used for analyzing the multispectral image from Landsat 8 OLI system with a spatial resolution of 30 m. The results of a study of the correlation between the reflection coefficient of <em>Brassica napus</em><span> and the area of observed soils will be presented. In addition, the results of the analysis of quasi-synchronous values of the NDVI index and in-situ measurements of the spectral reflectance of <em>Brassica napus</em> will be discussed.</span></p>

scholarly journals Soil Moisture Estimation Using Citizen Observatory Data, Microwave Satellite Imagery, and Environmental Covariates

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2160
Author(s):  
Daniel Kibirige ◽  
Endre Dobos
Keyword(s):  
Soil Moisture ◽  
Expert Knowledge ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Regression Kriging ◽  
Environmental Covariates ◽  
Observatory Data ◽  
Digital Elevation ◽  
Elevation Model

Soil moisture (SM) is a key variable in the climate system and a key parameter in earth surface processes. This study aimed to test the citizen observatory (CO) data to develop a method to estimate surface SM distribution using Sentinel-1B C-band Synthetic Aperture Radar (SAR) and Landsat 8 data; acquired between January 2019 and June 2019. An agricultural region of Tard in western Hungary was chosen as the study area. In situ soil moisture measurements in the uppermost 10 cm were carried out in 36 test fields simultaneously with SAR data acquisition. The effects of environmental covariates and the backscattering coefficient on SM were analyzed to perform SM estimation procedures. Three approaches were developed and compared for a continuous four-month period, using multiple regression analysis, regression-kriging and cokriging with the digital elevation model (DEM), and Sentinel-1B C-band and Landsat 8 images. CO data were evaluated over the landscape by expert knowledge and found to be representative of the major SM distribution processes but also presenting some indifferent short-range variability that was difficult to explain at this scale. The proposed models were evaluated using statistical metrics: The coefficient of determination (R2) and root mean square error (RMSE). Multiple linear regression provides more realistic spatial patterns over the landscape, even in a data-poor environment. Regression kriging was found to be a potential tool to refine the results, while ordinary cokriging was found to be less effective. The obtained results showed that CO data complemented with Sentinel-1B SAR, Landsat 8, and terrain data has the potential to estimate and map soil moisture content.

scholarly journals BIO OPTICAL MODELLING OF LAGUNA LAKE USING BOMBER TOOL AND WASI-DERIVED INVERTED PARAMETERS

2019 ◽  
Vol XLII-4/W16 ◽  
pp. 277-282
Author(s):  
R. L. Jalbuena ◽  
A. C. Blanco ◽  
A. Manuel ◽  
R. R. Sta. Ana ◽  
J. A. Santos
Keyword(s):  
Water Quality ◽  
Optical Properties ◽  
Optical Model ◽  
Water Source ◽  
Biological Properties ◽  
Landsat 8 ◽  
Produce Water ◽  
Optical Modelling ◽  
Laguna Lake

Abstract. Laguna Lake is significant to its surrounding cities and municipalities as it serves multiple purposes: flood basin, aquaculture, water source for irrigation and domestic use, among others. Monitoring the lake’s water quality is an integral part ensuring that the lake would continue to serve its purposes. Bio-optical modelling is a type of empirical model that relates the inherent optical properties of water to different biological properties like chlorophyll-a. The BOMBER (Bio-Optical Model Based tool for Estimating water quality and bottom properties from Remote sensing images) tool makes use of the different IOPs apparent optical properties (AOPs) of satellite images to be able to produce water quality maps. To localize the parameters used by the BOMBER tool, the use of WASI (The Water Color Simulator) tool was introduced. Inverting in situ spectral measurements of the lake, WASI tool was able to produce parameters localized for the lake. This research used 2018 Landsat 8 Images to produce images and used a water profiler to validate results. Results show the bio-optical model provided a R-squared value of 0.6912 and an RMSE of 2.43 μg/l which shows good correlation between the in-situ and the bio-optical model results.

scholarly journals ANALYSIS OF IN-SITU SPECTRAL REFLECTANCE OF SAGO AND OTHER PALMS: IMPLICATIONS FOR THEIR DETECTION IN OPTICAL SATELLITE IMAGES

2018 ◽  
Vol IV-3 ◽  
pp. 185-191
Author(s):  
Jojene Rendon Santillan ◽  
Meriam Makinano-Santillan
Keyword(s):  
Optical Sensors ◽  
Spectral Reflectance ◽  
Satellite Images ◽  
Spectral Response ◽  
Coconut Oil ◽  
Landsat 8 ◽  
Electromagnetic Spectrum ◽  
Palm Species ◽  
Optical Satellite Images

We present a characterization, comparison and analysis of in-situ spectral reflectance of Sago and other palms (coconut, oil palm and nipa) to ascertain on which part of the electromagnetic spectrum these palms are distinguishable from each other. The analysis also aims to reveal information that will assist in selecting which band to use when mapping Sago palms using the images acquired by these sensors. The datasets used in the analysis consisted of averaged spectral reflectance curves of each palm species measured within the 345–1045 nm wavelength range using an Ocean Optics USB4000-VIS-NIR Miniature Fiber Optic Spectrometer. This in-situ reflectance data was also resampled to match the spectral response of the 4 bands of ALOS AVNIR-2, 3 bands of ASTER VNIR, 4 bands of Landsat 7 ETM+, 5 bands of Landsat 8, and 8 bands of Worldview-2 (WV2). Examination of the spectral reflectance curves showed that the near infra-red region, specifically at 770, 800 and 875 nm, provides the best wavelengths where Sago palms can be distinguished from other palms. The resampling of the in-situ reflectance spectra to match the spectral response of optical sensors made possible the analysis of the differences in reflectance values of Sago and other palms in different bands of the sensors. Overall, the knowledge learned from the analysis can be useful in the actual analysis of optical satellite images, specifically in determining which band to include or to exclude, or whether to use all bands of a sensor in discriminating and mapping Sago palms.

scholarly journals A New Remote Sensing Dryness Index Based on the Near-Infrared and Red Spectral Space

2019 ◽  
Vol 11 (4) ◽  
pp. 456 ◽  
Author(s):  
Jieyun Zhang ◽  
Qingling Zhang ◽  
Anming Bao ◽  
Yujuan Wang
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Moisture Content ◽  
Near Infrared ◽  
Soil Moisture Content ◽  
Spectral Space ◽  
Landsat 8 ◽  
Strong Negative Correlation ◽  
Dryness Index

Soil moisture, as a crucial indicator of dryness, is an important research topic for dryness monitoring. In this study, we propose a new remote sensing dryness index for measuring soil moisture from spectral space. We first established a spectral space with remote sensing reflectance data at the near-infrared (NIR) and red (R) bands. Considering the distribution regularities of soil moisture in this space, we formulated the Ratio Dryness Monitoring Index (RDMI) as a new dryness monitoring indicator. We compared RDMI values with in situ soil moisture content data measured at 0–10 cm depth. Results showed that there was a strong negative correlation (R = −0.89) between the RDMI values and in situ soil moisture content. We further compared RDMI with existing remote sensing dryness indices, and the results demonstrated the advantages of the RDMI. We applied the RDMI to the Landsat-8 imagery to map dryness distribution around the Fukang area on the Northern slope of the Tianshan Mountains, and to the MODIS imagery to detect the spatial and temporal changes in dryness for the entire Xinjiang in 2013 and 2014. Overall, the RDMI index constructed, based on the NIR–Red spectral space, is simple to calculate, easy to understand, and can be applied to dryness monitoring at different scales.

scholarly journals Small-scale spatial variability in bare-ice reflectance at Jamtalferner, Austria

2020 ◽  
Vol 14 (11) ◽  
pp. 4063-4081
Author(s):  
Lea Hartl ◽  
Lucia Felbauer ◽  
Gabriele Schwaizer ◽  
Andrea Fischer
Keyword(s):  
Satellite Data ◽  
Spectral Reflectance ◽  
Full Range ◽  
Feedback Mechanism ◽  
Small Scale ◽  
Landsat 8 ◽  
Dry Ice ◽  
Depth Analysis ◽  
Reflectance Data

Abstract. As Alpine glaciers become snow-free in summer, more dark, bare ice is exposed, decreasing local albedo and increasing surface melting. To include this feedback mechanism in models of future deglaciation, it is important to understand the processes governing broadband and spectral albedo at a local scale. However, few in situ reflectance data have been measured in the ablation zones of mountain glaciers. As a contribution to this knowledge gap, we present spectral reflectance data (hemispherical–conical–reflectance factor) from 325 to 1075 nm collected along several profile lines in the ablation zone of Jamtalferner, Austria. Measurements were timed to closely coincide with a Sentinel-2 and Landsat 8 overpass and are compared to the respective ground reflectance (bottom-of-atmosphere) products. The brightest spectra have a maximum reflectance of up to 0.7 and consist of clean, dry ice. In contrast, reflectance does not exceed 0.2 for dark spectra where liquid water and/or fine-grained debris are present. Spectra can roughly be grouped into dry ice, wet ice, and dirt or rocks, although gradations between these groups occur. Neither satellite captures the full range of in situ reflectance values. The difference between ground and satellite data is not uniform across satellite bands, between Landsat and Sentinel, and to some extent between ice surface types (underestimation of reflectance for bright surfaces, overestimation for dark surfaces). We highlight the need for further, systematic measurements of in situ spectral reflectance properties, their variability in time and space, and in-depth analysis of time-synchronous satellite data.

Small scale spatial variability of spectral Albedo on Jamtalferner, Austria

2020 ◽  
Author(s):  
Lea Hartl ◽  
Lucia Felbauer ◽  
Gabriele Schwaizer ◽  
Andrea Fischer
Keyword(s):  
Satellite Data ◽  
Liquid Water ◽  
Spectral Reflectance ◽  
Correlation Coefficients ◽  
Landsat 8 ◽  
Dry Ice ◽  
Ablation Area ◽  
In Situ Data ◽  
Sentinel 2

<p>Glacier albedo is one of the most important and most variable parameters affecting surface energy balance and directly impacts ice loss. We present preliminary results from a study aiming to quantify the range and variability of spectral reflectance on a glacier terminus and assess the effects of liquid water and impurities on ablation area reflectance. In a second step of the analysis, in-situ data is compared with Landsat 8 and Sentinel 2 surface reflectance products.</p><p>In-situ spectral reflectance data was collected for wavelengths from 350-1000nm, using a hand-held ASD spectroradiometer. 246 spectra were gathered along 16 profile lines.</p><p>The “brightest” profile has a maximum reflectance of 0.7 and consists of clean, dry ice. At several “dark” profiles, reflectance does not exceed 0.2. At these profiles, liquid water is present, often mixed with fine grained debris. Individual spectra can roughly be grouped into dry ice, wet ice, and dirt/rocks. However, transitions between groups are fluid and in practice these categories cannot always be clearly separated. The spread of reflectance values per profile is generally lower for darker profiles. The reflectance spectra for clean ice exhibit the typical shape found in literature, with highest reflectance values in the lower third of our wavelength range and declining values for wavelengths greater than approximately 580nm. For wet ice surfaces, the spectra follow roughly the same shape as for dry ice, but are strongly dampened in amplitude, with reflectance typically below 0.2.</p><p>For the comparison of in-situ and satellite data, we use a Sentinel 2A scene acquired the same day as the ground measurements and a Landsat 8 scene from the previous day. Both scenes are cloud free over the study area. The wavelength range of the in-situ data overlaps with Landsat 8 bands 1-5 and Sentinel 2 bands 1-9 and 8A, respectively.</p><p>Neither satellite captures the full range of in-situ reflectance values. In all bands in which both satellites overlap, Sentinel values are shifted up against Landsat, in the sense that the maximum values of the Sentinel data are closer to the maximum values measured on the ground, while the minimum Landsat data are closer to the minimum ground values. Comparing the mean of the spectral reflectances measured on the ground with the associated satellite band values yields Pearson correlation coefficients from 0.53 to 0.62 for Landsat and 0.3 to 0.65 for Sentinel. Correlation coefficients decrease significantly for lower resolution satellite bands.</p><p>When binning ground measurements by the associated satellite pixel, the difference between the median/mean ground value and the satellite value tends to decrease with increasing number of ground measurements mapped to unique satellite pixels. While this is expected, the relationship is not obviously linear for our data and differs between the satellites and different bands.</p><p>Further in-situ measurements and analysis of satellite data will be carried out to improve understanding of processes governing ablation area reflectance, satellite derived ablation area reflectance products, and the modelling of feedback mechanisms.</p>

scholarly journals Development of the Statistical Model for Monitoring Salinization in the Mekong Delta of Vietnam Using Remote Sensing Data and In-Situ Measurements

Proceedings ◽  
2018 ◽  
Vol 2 (10) ◽  
pp. 565
Author(s):  
Nguyen Nguyen Vu ◽  
Le Van Trung ◽  
Tran Thi Van
Keyword(s):  
Statistical Model ◽  
Mekong Delta ◽  
Remote Sensing Data ◽  
Principal Component ◽  
Measured Data ◽  
In Situ Measurements ◽  
Landsat 8 ◽  
Spectral Bands ◽  
Component Band

This article presents the methodology for developing a statistical model for monitoring salinity intrusion in the Mekong Delta based on the integration of satellite imagery and in-situ measurements. We used Landsat-8 Operational Land Imager and Thermal Infrared Sensor (Landsat- 8 OLI and TIRS) satellite data to establish the relationship between the planetary reflectance and the ground measured data in the dry season during 2014. The three spectral bands (blue, green, red) and the principal component band were used to obtain the most suitable models. The selected model showed a good correlation with the exponential function of the principal component band and the ground measured data (R2 > 0.8). Simulation of the salinity distribution along the river shows the intrusion of a 4 g/L salt boundary from the estuary to the inner field of more than 50 km. The developed model will be an active contribution, providing managers with adaptation and response solutions suitable for intrusion in the estuary as well as the inner field of the Mekong Delta.

scholarly journals Mapping High Spatiotemporal-Resolution Soil Moisture by Upscaling Sparse Ground-Based Observations Using a Bayesian Linear Regression Method for Comparison with Microwave Remotely Sensed Soil Moisture Products

2021 ◽  
Vol 13 (2) ◽  
pp. 228
Author(s):  
Jian Kang ◽  
Rui Jin ◽  
Xin Li ◽  
Yang Zhang
Keyword(s):  
Soil Moisture ◽  
Large Scale ◽  
Spatial Scales ◽  
Microwave Remote Sensing ◽  
In Situ Measurements ◽  
Spatial Density ◽  
Linear Regression Method ◽  
Spatiotemporal Resolution ◽  
Pixel Scale

In recent decades, microwave remote sensing (RS) has been used to measure soil moisture (SM). Long-term and large-scale RS SM datasets derived from various microwave sensors have been used in environmental fields. Understanding the accuracies of RS SM products is essential for their proper applications. However, due to the mismatched spatial scale between the ground-based and RS observations, the truth at the pixel scale may not be accurately represented by ground-based observations, especially when the spatial density of in situ measurements is low. Because ground-based observations are often sparsely distributed, temporal upscaling was adopted to transform a few in situ measurements into SM values at a pixel scale of 1 km by introducing the temperature vegetation dryness index (TVDI) related to SM. The upscaled SM showed high consistency with in situ SM observations and could accurately capture rainfall events. The upscaled SM was considered as the reference data to evaluate RS SM products at different spatial scales. In regard to the validation results, in addition to the correlation coefficient (R) of the Soil Moisture Active Passive (SMAP) SM being slightly lower than that of the Climate Change Initiative (CCI) SM, SMAP had the best performance in terms of the root-mean-square error (RMSE), unbiased RMSE and bias, followed by the CCI. The Soil Moisture and Ocean Salinity (SMOS) products were in worse agreement with the upscaled SM and were inferior to the R value of the X-band SM of the Advanced Microwave Scanning Radiometer 2 (AMSR2). In conclusion, in the study area, the SMAP and CCI SM are more reliable, although both products were underestimated by 0.060 cm3 cm−3 and 0.077 cm3 cm−3, respectively. If the biases are corrected, then the improved SMAP with an RMSE of 0.043 cm3 cm−3 and the CCI with an RMSE of 0.039 cm3 cm−3 will hopefully reach the application requirement for an accuracy with an RMSE less than 0.040 cm3 cm−3.

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