Combining optical remote sensing data with in-situ measurements in order to estimate vegetation parameters on agricultural fields and corresponding uncertainties

2017 ◽  
Author(s):  
Katharina Heupel ◽  
Daniel Spengler ◽  
Cornelia Weltzien
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
In Situ Measurements ◽  
Agricultural Fields ◽  
Optical Remote Sensing ◽  
Sensing Data ◽  
Vegetation Parameters

scholarly journals ESTIMATION OF SURFACE SNOW WETNESS USING SENTINEL-2 MULTISPECTRAL DATA

2018 ◽  
Vol IV-5 ◽  
pp. 223-228 ◽  
Author(s):  
D. Varade ◽  
O. Dikshit
Keyword(s):  
Remote Sensing ◽  
Water Resource Management ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
In Situ Measurements ◽  
Physical Relationship ◽  
Sensing Data ◽  
Surface Snow ◽  
Sentinel 2

<p><strong>Abstract.</strong> Snow cover characterization and estimation of snow geophysical parameters is a significant area of research in water resource management and surface hydrological processes. With advances in spaceborne remote sensing, much progress has been achieved in the qualitative and quantitative characterization of snow geophysical parameters. However, most of the methods available in the literature are based on the microwave backscatter response of snow. These methods are mostly based on the remote sensing data available from active microwave sensors. Moreover, in alpine terrains, such as in the Himalayas, due to the geometrical distortions, the missing data is significant in the active microwave remote sensing data. In this paper, we present a methodology utilizing the multispectral observations of Sentinel-2 satellite for the estimation of surface snow wetness. The proposed approach is based on the popular triangle method which is significantly utilized for the assessment of soil moisture. In this case, we develop a triangular feature space using the near infrared (NIR) reflectance and the normalized differenced snow index (NDSI). Based on the assumption that the NIR reflectance is linearly related to the liquid water content in the snow, we derive a physical relationship for the estimation of snow wetness. The modeled estimates of snow wetness from the proposed approach were compared with in-situ measurements of surface snow wetness. A high correlation determined by the coefficient of determination of 0.94 and an error of 0.535 was observed between the proposed estimates of snow wetness and in-situ measurements.</p>

scholarly journals Evaluation of RapidEye-3 Satellite Data for Assessing Water Turbidity of Lake Borabey

Proceedings ◽  
2019 ◽  
Vol 48 (1) ◽  
pp. 14
Author(s):  
Gordana Kaplan ◽  
Zehra Yigit Avdan ◽  
Serdar Goncu ◽  
Ugur Avdan
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Quality Parameters ◽  
In Situ Measurements ◽  
Water Quality Parameters ◽  
Water Turbidity ◽  
Sensing Data

In water resources management, remote sensing data and techniques are essential in watershed characterization and monitoring, especially when no data are available. Water quality is usually assessed through in-situ measurements that require high cost and time. Water quality parameters help in decision making regarding the further use of water-based on its quality. Turbidity is an important water quality parameter and an indicator of water pollution. In the past few decades, remote sensing has been widely used in water quality research. In this study, we compare turbidity parameters retrieved from a high-resolution image with in-situ measurements collected from Borabey Lake, Turkey. Here, the use of RapidEye-3 images (5 m-resolution) allows for detailed assessment of spatio-temporal evaluation of turbidity, through the normalized difference turbidity index (NDTI). The turbidity results were then compared with data from 21 in-situ measurements collected in the same period. The actual water turbidity measurements showed high correlation with the estimated NDTI mean values with an R2 of 0.84. The research findings support the use of remote sensing data of RadipEye-3 to estimate water quality parameters in small water areas. For future studies, we recommend investigating different water quality parameters using high-resolution remote sensing data.

scholarly journals Variability of Net Primary Productivity and Associated Biophysical Drivers in Bahía de La Paz (Mexico)

2021 ◽  
Vol 13 (9) ◽  
pp. 1644
Author(s):  
Rafael Cervantes-Duarte ◽  
Eduardo González-Rodríguez ◽  
René Funes-Rodríguez ◽  
Alejandro Ramos-Rodríguez  ◽  
María Yesenia Torres-Hernández ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Primary Productivity ◽  
Net Primary Productivity ◽  
Remote Sensing Data ◽  
In Situ Measurements ◽  
Hydrographic Data ◽  
La Paz ◽  
Sensing Data ◽  
Spatio Temporal

The use of information of net primary productivity (NPP) from remote ocean color sensors is increasingly common in marine sciences. The resulting information has been used to explain variations in productivity at different spatio-temporal scales and in the presence of climate phenomena, such as the El Niño Southern Oscillation, and global warming. Satellite remote sensing data were analyzed in Bahía de La Paz (BLP), Mexico, to determine the spatio-temporal variation in NPP. In addition, in situ hydrographic data were obtained to characterize the water properties in the bay. The satellite data agree with in situ measurements, validating the satellite observations over this region. The NPP generally presented seasonal variation with maximum values in winter-spring and minimum values in summer–autumn. The variance explained by NPP from the measured variables was ranked as Chl-a < DEN < SST < PAR < WSC. The highest NPP values generally occurred when subtropical subsurface (SsStW) water was relatively shallow. Due to divergence and mixing processes, this water provided nutrients to the euphotic zone, and consequently an increase in NPP and changes in plankton biomass were observed. The annual trends of the variation in hydrographic data with respect to that of remote sensing data were similar; however, it is necessary to increase the number of data validation studies. The remote sensing and in situ measurements allowed for the main biophysical variables that modulate NPP in different time scales to be identified. The satellite-derived NPP data classifies the BLP as a high productivity zone with 432 g C m−2 year−1. The use of satellite NPP data is satisfactory and should be incorporated into marine primary productivity studies.

scholarly journals Deriving CME density from remote sensing data and comparison to in‐situ measurements

2020 ◽  
Author(s):  
M. Temmer ◽  
L. Holzknecht ◽  
M. Dumbović ◽  
B. Vršnak ◽  
N. Sachdeva ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
In Situ Measurements ◽  
Sensing Data

scholarly journals Sensitive Feature Evaluation for Soil Moisture Retrieval Based on Multi-Source Remote Sensing Data with Few In-Situ Measurements: A Case Study of the Continental U.S.

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2003
Author(s):  
Ling Zhang ◽  
Zixuan Zhang ◽  
Zhaohui Xue ◽  
Hao Li
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Feature Selection ◽  
Soil Moisture ◽  
Remote Sensing Data ◽  
In Situ Measurements ◽  
Feature Subset ◽  
Selection Methods ◽  
Sensing Data

Soil moisture (SM) plays an important role for understanding Earth’s land and near-surface atmosphere interactions. Existing studies rarely considered using multi-source data and their sensitiveness to SM retrieval with few in-situ measurements. To solve this issue, we designed a SM retrieval method (Multi-MDA-RF) using random forest (RF) based on 29 features derived from passive microwave remote sensing data, optical remote sensing data, land surface models (LSMs), and other auxiliary data. To evaluate the importance of different features to SM retrieval, we first compared 10 filter or embedded type feature selection methods with sequential forward selection (SFS). Then, RF was employed to establish a nonlinear relationship between the in-situ SM measurements from sparse network stations and the optimal feature subset. The experiments were conducted in the continental U.S. (CONUS) using in-situ measurements during August 2015, with only 5225 training samples covering the selected feature subset. The experimental results show that mean decrease accuracy (MDA) is better than other feature selection methods, and Multi-MDA-RF outperforms the back-propagation neural network (BPNN) and generalized regression neural network (GRNN), with the R and unbiased root-mean-square error (ubRMSE) values being 0.93 and 0.032 cm3/cm3, respectively. In comparison with other SM products, Multi-MDA-RF is more accurate and can well capture the SM spatial dynamics.

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