scholarly journals Comparison of Spaceborne and UAV-Borne Remote Sensing Spectral Data for Estimating Monsoon Crop Vegetation Parameters

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2886
Author(s):  
Jayan Wijesingha ◽  
Supriya Dayananda ◽  
Michael Wachendorf ◽  
Thomas Astor
Keyword(s):  
Remote Sensing ◽  
Parameter Estimation ◽  
Spectral Data ◽  
Finger Millet ◽  
Chlorophyll Concentration ◽  
Hyperspectral Data ◽  
Sensor Data ◽  
Area Index ◽  
Vegetation Parameter ◽  
Vegetation Parameters

Various remote sensing data have been successfully applied to monitor crop vegetation parameters for different crop types. Those successful applications mostly focused on one sensor system or a single crop type. This study compares how two different sensor data (spaceborne multispectral vs unmanned aerial vehicle borne hyperspectral) can estimate crop vegetation parameters from three monsoon crops in tropical regions: finger millet, maize, and lablab. The study was conducted in two experimental field layouts (irrigated and rainfed) in Bengaluru, India, over the primary agricultural season in 2018. Each experiment contained n = 4 replicates of three crops with three different nitrogen fertiliser treatments. Two regression algorithms were employed to estimate three crop vegetation parameters: leaf area index, leaf chlorophyll concentration, and canopy water content. Overall, no clear pattern emerged of whether multispectral or hyperspectral data is superior for crop vegetation parameter estimation: hyperspectral data showed better estimation accuracy for finger millet vegetation parameters, while multispectral data indicated better results for maize and lablab vegetation parameter estimation. This study’s outcome revealed the potential of two remote sensing platforms and spectral data for monitoring monsoon crops also provide insight for future studies in selecting the optimal remote sensing spectral data for monsoon crop parameter estimation.

scholarly journals Using Remote Sensing Based Metrics to Quantify the Hydrological Response in a City

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1763 ◽  
Author(s):  
Charlotte Wirion ◽  
Willy Bauwens ◽  
Boud Verbeiren
Keyword(s):  
Remote Sensing ◽  
Potential Evapotranspiration ◽  
Remote Sensing Data ◽  
Hyperspectral Data ◽  
Hydrological Response ◽  
Area Index ◽  
Impervious Area ◽  
Urban Catchments ◽  
The City ◽  
Total Impervious Area

We propose a remote-sensing based metric approach to evaluate the hydrological response of highly urbanized areas and apply it to the city of Brussels. The model is set-up using 2 m resolution hyperspectral data. Next, it is upscaled to the city level, using multi-spectral Sentinel-2 data with 20 m resolution. We identify the total impervious area, the vegetation cover and the leaf area index as important metrics to derive a timeseries of spatially distributed net rainfall, runoff and infiltration from rainfall data. For the estimation of the actual evapotranspiration we use the potential evapotranspiration and the available water storage based on the interception, the depression storage and the infiltration. Additionally, we route the runoff to the outlet of selected sub-catchments. An important metric for the routing is the timing to the outlet which is approximated using the total impervious area and the hydrological distance to the outlet. We compare our approach to WetSpa model simulations and reach R 2 values of 98% for net rainfall, 95% for surface runoff, 99% for infiltration and 97% for cumulative evapotranspiration. The routing in the Watermaelbeek catchment is evaluated with discharge observations and reaches NSE values of 0.89 at a 2 m resolution and 0.88 at a 20 m resolution using an hourly timestep. At the timestep of 10 min and a 20 m resolution the NSE is reduced to 0.76. For the Roodebeek catchment we reach an NSE of 0.73 at a spatial resolution of 20 m and an hourly timestep. The results presented in this paper are optimistic for using spatial and temporal metrics retrieved from remote sensing data to quantify the water balance of urban catchments.

scholarly journals Gaussian Processes for Vegetation Parameter Estimation from Hyperspectral Data with Limited Ground Truth

2019 ◽  
Vol 11 (13) ◽  
pp. 1614 ◽  
Author(s):  
Utsav B. Gewali ◽  
Sildomar T. Monteiro ◽  
Eli Saber
Keyword(s):  
Gaussian Processes ◽  
Ground Truth ◽  
Joint Modeling ◽  
Hyperspectral Data ◽  
Sample Collection ◽  
Spectral Correlation ◽  
Covariance Functions ◽  
Vegetation Parameter ◽  
Vegetation Parameters ◽  
Ground Truth Information

An important application of airborne- and satellite-based hyperspectral imaging is the mapping of the spatial distribution of vegetation biophysical and biochemical parameters in an environment. Statistical models, such as Gaussian processes, have been very successful for modeling vegetation parameters from captured spectra, however their performance is highly dependent on the amount of available ground truth. This is a problem because it is generally expensive to obtain ground truth information due to difficulties and costs associated with sample collection and analysis. In this paper, we present two Gaussian processes based approaches for improving the accuracy of vegetation parameter retrieval when ground truth is limited. The first is the adoption of covariance functions based on well-established metrics, such as, spectral angle and spectral correlation, which are known to be better measures of similarity for spectral data owing to their resilience to spectral variabilities. The second is the joint modeling of related vegetation parameters by multitask Gaussian processes so that the prediction accuracy of the vegetation parameter of interest can be improved with the aid of related vegetation parameters for which a larger set of ground truth is available. We experimentally demonstrate the efficacy of the proposed methods against existing approaches on three real-world hyperspectral datasets and one synthetic dataset.

Comparison of UAV RGB Imagery and Hyperspectral Remote-sensing Data for Monitoring Winter-wheat Growth

2021 ◽  
Author(s):  
Haikuan Feng ◽  
Huilin Tao ◽  
Chunjiang Zhao ◽  
Zhenhai Li ◽  
Guijun Yang
Keyword(s):  
Remote Sensing ◽  
Spectral Index ◽  
Vegetation Index ◽  
Crop Growth ◽  
Hyperspectral Data ◽  
Growth Stages ◽  
Growth Monitoring ◽  
Estimation Model ◽  
Area Index ◽  
Simple Ratio

Abstract Background: Although crop-growth monitoring is important for agricultural managers, it has always been a difficult research topic. However, unnamed aerial vehicles (UAVs) equipped with RGB and hyperspectral cameras can now acquire high-resolution remote-sensing images, which facilitates and accelerates such monitoring. Results: To explore the effect of monitoring a single crop-growth indicator and multiple indicators, this study combines six growth indicators (plant nitrogen content, above-ground biomass, plant water content, chlorophyll, leaf area index, and plant height) into a new comprehensive growth index (CGI). We investigate the performance of RGB imagery and hyperspectral data for monitoring crop growth based on multi-time estimation of the CGI. The CGI is estimated from the vegetation indices based on UAV hyperspectral data treated by linear, nonlinear, and multiple linear regression (MLR), partial least squares (PLSR), and random forest (RF). The results show that (1) the RGB-imagery indices red reflectance (r), the excess-red index(EXR), the vegetation atmospherically resistant index(VARI), and the modified green-red vegetation index(MGRVI) , as well as the spectral indices consisting of the linear combination index (LCI), the modified simple ratio index(MSR), the simple ratio vegetation index(SR), and the normalized difference vegetation index (NDVI)are more strongly correlated with the CGI than a single growth-monitoring indicator (2) The CGI estimation model is constructed by comparing a single RGB-imagery index and a spectral index, and the optimal RGB-imagery index corresponding to each of the four growth stage in order is r, r, r, EXR; the optimal spectral index is LCI for all four growth stages. (3) The MLR, PLSR, and RF methods are used to estimate the CGI. The MLR method produces the best estimates. (4) Finally, the CGI is more accurately estimated using the UAV hyperspectral indices than using the RGB-image indices.Conclusions: UAVs carrying RGB cameras and hyperspectral cameras have high inversion CGI accuracy and can judge the overall growth of wheat can provide a reference for monitoring the growth of wheat.

scholarly journals Multi-mission satellite remote sensing data for improving land hydrological models via data assimilation

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
M. Khaki ◽  
H.-J. Hendricks Franssen ◽  
S. C. Han
Keyword(s):  
Remote Sensing ◽  
Parameter Estimation ◽  
Soil Moisture ◽  
Data Assimilation ◽  
Satellite Remote Sensing ◽  
Model Parameters ◽  
Parameter Uncertainties ◽  
Area Index ◽  
Mission Satellite ◽  
Model Predictions

Abstract Satellite remote sensing offers valuable tools to study Earth and hydrological processes and improve land surface models. This is essential to improve the quality of model predictions, which are affected by various factors such as erroneous input data, the uncertainty of model forcings, and parameter uncertainties. Abundant datasets from multi-mission satellite remote sensing during recent years have provided an opportunity to improve not only the model estimates but also model parameters through a parameter estimation process. This study utilises multiple datasets from satellite remote sensing including soil moisture from Soil Moisture and Ocean Salinity Mission and Advanced Microwave Scanning Radiometer Earth Observing System, terrestrial water storage from the Gravity Recovery And Climate Experiment, and leaf area index from Advanced Very-High-Resolution Radiometer to estimate model parameters. This is done using the recently proposed assimilation method, unsupervised weak constrained ensemble Kalman filter (UWCEnKF). UWCEnKF applies a dual scheme to separately update the state and parameters using two interactive EnKF filters followed by a water balance constraint enforcement. The performance of multivariate data assimilation is evaluated against various independent data over different time periods over two different basins including the Murray–Darling and Mississippi basins. Results indicate that simultaneous assimilation of multiple satellite products combined with parameter estimation strongly improves model predictions compared with single satellite products and/or state estimation alone. This improvement is achieved not only during the parameter estimation period ($$\sim $$ ∼  32% groundwater RMSE reduction and soil moisture correlation increase from $$\sim $$ ∼  0.66 to $$\sim $$ ∼  0.85) but also during the forecast period ($$\sim $$ ∼  14% groundwater RMSE reduction and soil moisture correlation increase from $$\sim $$ ∼  0.69 to $$\sim $$ ∼  0.78) due to the effective impacts of the approach on both state and parameters.

The Use of Remote Sensing to Assess the Effects of Water Stress on Wheat

1991 ◽  
Vol 27 (4) ◽  
pp. 423-429 ◽  
Author(s):  
R. K. Mahey ◽  
Rajwant Singh ◽  
S. S. Sidhu ◽  
R. S. Narang
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Grain Yield ◽  
Spectral Data ◽  
Growth And Development ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spectral Response ◽  
Canopy Cover ◽  
Area Index

SUMMARYGround-based radiometric measurements in the red and infrared bands were used to monitor the growth and development of wheat under irrigated and stressed conditions throughout the 1987–88 and 1988–89 growth cycles. Spectral data were correlated with plant height, leaf area index, total fresh and total dry biomass, plant water content and grain yield. The radiance ratio (R) and normalized difference vegetation index (NDVI) were highly and linearly correlated with yield, establishing the potential which remote sensing has for predicting grain yield. The correlation for R and NDVI was at a maximum between 75 and 104 days after sowing, corresponding with maximum green crop canopy cover. The differences in spectral response over time between irrigated and unirrigated crops allowed detection of water stress effects on the crop, indicating that a hand-held radiometer can be used to collect spectral data which can supply information on wheat growth and development.Efectos de lafalta de agua en el trigo

scholarly journals Improving evapotranspiration in land surface models by using biophysical parameters derived from MSG/SEVIRI satellite

2011 ◽  
Vol 8 (5) ◽  
pp. 9113-9171 ◽  
Author(s):  
N. Ghilain ◽  
A. Arboleda ◽  
G. Sepulcre-Cantò ◽  
O. Batelaan ◽  
J. Ardö ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Surface Model ◽  
Spatial And Temporal Variability ◽  
Land Surface Models ◽  
Area Index ◽  
Continental Scale ◽  
Surface Models ◽  
Vegetation Parameters ◽  
The Impact

Abstract. Vegetation parameters derived from the geostationary satellite MSG/SEVIRI have been distributed at a daily frequency since 2007 over Europe, Africa and part of South America, through the LSA-SAF facility. We propose here a method to handle two new remote sensing products from LSA-SAF, leaf area index and Fractional Vegetation Cover, noted LAI and FVC respectively, for land surface models at MSG/SEVIRI scale. The developed method relies on an ordinary least-square technique and a land cover map to estimate LAI for each model plant functional types of the model spatial unit. The method is conceived to be applicable for near-real time applications at continental scale. Compared to monthly vegetation parameters from a vegetation database commonly used in numerical weather predictions (ECOCLIMAP-I), the new remote sensing products allows a better monitoring of the spatial and temporal variability of the vegetation, including inter-annual signals, and a decreased uncertainty on LAI to be input into land surface models. We assess the impact of using LSA-SAF vegetation parameters compared to ECOCLIMAP-I in the land surface model H-TESSEL at MSG/SEVIRI scale. Comparison with in-situ observations in Europe and Africa shows that the results on evapotranspiration are mostly improved, and especially in semi-arid climates. At last, the use of LSA-SAF and ECOCLIMAP-I is compared with simulations over a North-South Transect in Western Africa using LSA-SAF radiation forcing derived from remote sensing, and differences are highlighted.

scholarly journals Estimación de variables biofísicas del pastizal en un ecosistema de dehesa a partir de espectro-radiometría de campo e imágenes hiperespectrales aeroportadas

2017 ◽  
pp. 13 ◽  
Author(s):  
J. R. Melendo-Vega ◽  
M. P. Martín ◽  
L. Vilar del Hoyo ◽  
J. Pacheco-Labrador ◽  
P. Echavarría ◽  
...  
Keyword(s):  
Vegetation Indices ◽  
Hyperspectral Data ◽  
Biophysical Parameters ◽  
Area Index ◽  
Linear Relationships ◽  
Spectral Vegetation Indices ◽  
Vegetation Parameters ◽  
Fuel Moisture Content ◽  
Canopy Water ◽  
Compact Airborne Spectrographic Imager

<p>The aim of this paper is the estimation of biophysical vegetation parameters from its optical properties. The variables Fuel Moisture Content (<em>FMC</em>), Canopy Water Content (<em>CWC</em>), Leaf Area Index (<em>LAI</em>), dry matter (Cm) and AboveGround Biomass (AGB) were estimated in the laboratory from vegetation samples collected simultaneously with the acquisition of spectral data from the Compact Airborne Spectrographic Imager (CASI) sensor and the field spectroradiometer ASD FieldSpec® 3. Spectral vegetation indices found in the literature were computed from hyperspectral data. Their linear relationships with the biophysical variables measured in the field were analysed. Results show consistent relationships between analysed biophysical parameters and spectral indices, mainly those using SWIR and red-egde bands which reveal the importance of these spectral regions for the estimation of biophysical variables in herbaceous covers. Determination coefficients (<em>R<sup>2</sup></em>) above 0.91 and <em>RRMSE</em> of 21.4% have been obtained for the spectral indexes calculated whit ASD data, and 0.91 <em>R<sup>2 </sup></em>and <em>RRMSE</em> of 15.5% for the spectral indexes calculated whit CASI data.</p>

scholarly journals RETRIVAL OF BIO-PHYSICAL PARAMETERS IN SUNFLOWER CROP (<i>HELIANTHUS ANNUUS</i>) USING FIELD BASED HYPERSPECTRAL REMOTE SENSING

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 623-629
Author(s):  
P. K. Routh ◽  
N. C. Sarkar ◽  
P. K. Das ◽  
D. Debnath ◽  
S. Bandyopadhyay ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Chlorophyll Content ◽  
Hyperspectral Remote Sensing ◽  
Hyperspectral Data ◽  
Health Study ◽  
Physical Parameters ◽  
Absorption Bands ◽  
Area Index ◽  
Sunflower Crop ◽  
Gas Exchange System

<p><strong>Abstract.</strong> Information on several crop bio-physical parameters is important as inputs for crop growth modelling, leaf stress analysis, crop health study and productivity point of view. Conventionally, biophysical parameters are measured in laboratory methods which are time consuming, laborious and destructive in nature. With the advent of remote sensing technology, the limitations of conventional methods can be overcome. Moreover, due to its narrow absorption bands at different wavelength, use of hyperspectral remote sensing becomes very useful in retrieving several bio-physical parameters. In the present study, field as well as laboratory based spectro-radiometer observations were carried out at Agronomy Department of VisvaBharati University, West Bengal, on Sunflower crop at its peak vegetation stage towards retrieving different bio-physical parameters, specifically leaf area index (LAI), chlorophyll content index (CCI), fluorescence etc. Different foliar boron (no boron, 0.15% and 0.20%) and irrigation (4&amp;ndash;6 irrigations) treatments, i.e. total nine treatments with three replications, were applied on sunflower crop during different phenological stages to achievemaximum ranges of the bio-physical parameters. The LAI, CCI and fluorescence parameters were collected using canopy analyzer,chlorophyll content meter and portable gas exchange system, respectively. In each of the treatments, total four hyperspectral measurements were collected, which were further corrected for noise and smoothened using Savitzky-Golay filtering. Total thirty-four narrow band indices were computed based on the hyperspectral data, and the regression analysis was carried out among the indices and bio-physical parameters. The regression parameters were further deployed on the hyperspectral indices to retrieve the bio-physical parameters. The Gitelson &amp; Merzylak-1 (GM-1) and Carter Indices-1 (CI-1) were found to the best indices for retrieving the LAI and CCI, respectively with correlation correlation (r) values of 0.87 and 0.80. On the other hand, Normalized Phaenophytinization Index (NPQI) and GM-1 were found to best for retrieving the Fv/Fm (dark) and Fvˈ/Fmˈ (light) with correlation(r)values of 0.92 and 0.76, respectively. Hence, the hyperspectral remote sensing be successfully utilized for retrieving several bio-physical parameters both at field (canopy level) and laboratory (leaf level) conditions.</p>

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