scholarly journals First Insights on Soil Respiration Prediction across the Growth Stages of Rainfed Barley Based on Simulated MODIS and Sentinel-2 Spectral Indices

2020 ◽  
Vol 12 (17) ◽  
pp. 2724
Author(s):  
Víctor Cicuéndez ◽  
Manuel Rodríguez-Rastrero ◽  
Laura Recuero ◽  
Margarita Huesca ◽  
Thomas Schmid ◽  
...  
Keyword(s):  
Soil Respiration ◽  
Carbon Cycle ◽  
Near Infrared ◽  
Spatial Scales ◽  
Coefficient Of Determination ◽  
Growth Stages ◽  
Spectral Indices ◽  
Crop Phenology ◽  
Area Index ◽  
Temporal And Spatial

Rainfed agriculture occupies the majority of the world’s agricultural surface and is expected to increase in the near future causing serious effects on carbon cycle dynamics in the context of climate change. Carbon cycle across several temporal and spatial scales could be studied through spectral indices because they are related to vegetation structure and functioning and hence with carbon fluxes, among them soil respiration (Rs). The aim of this work was to assess Rs linked to crop phenology of a rainfed barley crop throughout two seasons based on spectral indices calculated from field spectroscopy data. The relationships between Rs, Leaf Area Index (LAI) and spectral indices were assessed by linear regression models with the adjusted coefficient of determination (Radj2). Results showed that most of the spectral indices provided better information than LAI throughout the studied period and that soil moisture and temperature were relevant variables in specific periods. During vegetative stages, indices based on the visible (VIS) region showed the best relationship with Rs. On the other hand, during reproductive stages indices containing the near infrared-shortwave infrared (NIR-SWIR) spectral region and those related to water content showed the highest relationship. The inter-annual variability found in Mediterranean regions was also observed in the estimated ratio of carbon emission to carbon fixation between years. Our results show the potential capability of spectral information to assess soil respiration linked to crop phenology across several temporal and spatial scales. These results can be used as a basis for the utilization of other remote information derived from satellites or airborne sensors to monitor crop carbon balances.

scholarly journals VIS-NIR, Red-Edge and NIR-Shoulder Based Normalized Vegetation Indices Response to Co-Varying Leaf and Canopy Structural Traits in Heterogeneous Grasslands

2020 ◽  
Vol 12 (14) ◽  
pp. 2254 ◽  
Author(s):  
Hafiz Ali Imran ◽  
Damiano Gianelle ◽  
Duccio Rocchini ◽  
Michele Dalponte ◽  
M. Pilar Martín ◽  
...  
Keyword(s):  
Near Infrared ◽  
Spatial Scales ◽  
Vegetation Indices ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Sharp Change ◽  
Full Potential ◽  
Area Index ◽  
Red Edge ◽  
Temporal And Spatial

Red-edge (RE) spectral vegetation indices (SVIs)—combining bands on the sharp change region between near infrared (NIR) and visible (VIS) bands—alongside with SVIs solely based on NIR-shoulder bands (wavelengths 750–900 nm) have been shown to perform well in estimating leaf area index (LAI) from proximal and remote sensors. In this work, we used RE and NIR-shoulder SVIs to assess the full potential of bands provided by Sentinel-2 (S-2) and Sentinel-3 (S-3) sensors at both temporal and spatial scales for grassland LAI estimations. Ground temporal and spatial observations of hyperspectral reflectance and LAI were carried out at two grassland sites (Monte Bondone, Italy, and Neustift, Austria). A strong correlation (R2 > 0.8) was observed between grassland LAI and both RE and NIR-shoulder SVIs on a temporal basis, but not on a spatial basis. Using the PROSAIL Radiative Transfer Model (RTM), we demonstrated that grassland structural heterogeneity strongly affects the ability to retrieve LAI, with high uncertainties due to structural and biochemical PTs co-variation. The RENDVI783.740 SVI was the least affected by traits co-variation, and more studies are needed to confirm its potential for heterogeneous grasslands LAI monitoring using S-2, S-3, or Gaofen-5 (GF-5) and PRISMA bands.

scholarly journals Estimating Vertical Distribution of Leaf Water Content within Wheat Canopies after Head Emergence

2021 ◽  
Vol 13 (20) ◽  
pp. 4125
Author(s):  
Weiping Kong ◽  
Wenjiang Huang ◽  
Lingling Ma ◽  
Lingli Tang ◽  
Chuanrong Li ◽  
...  
Keyword(s):  
Vertical Distribution ◽  
Water Content ◽  
Near Infrared ◽  
Field Experiments ◽  
Estimation Method ◽  
Middle Layer ◽  
Leaf Water ◽  
Leaf Water Content ◽  
Growth Stages ◽  
Spectral Indices

Monitoring vertical profile of leaf water content (LWC) within wheat canopies after head emergence is vital significant for increasing crop yield. However, the estimation of vertical distribution of LWC from remote sensing data is still challenging due to the effects of wheat spikes and the efficacy of sensor measurement from the nadir direction. Using two-year field experiments with different growth stages after head emergence, N rates, wheat cultivars, we investigated the vertical distribution of LWC within canopies, the changes of canopy reflectance after spikes removal, the relationship between spectral indices and LWC in the upper-, middle- and bottom-layer. The interrelationship among vertical LWC were constructed, and four ratio of reflectance difference (RRD) type of indices were proposed based on the published WI and NDWSI indices to determine vertical distribution of LWC. The results indicated a bell shape distribution of LWC in wheat plants with the highest value appeared at the middle layer, and significant linear correlations between middle-LWC vs. upper-LWC and middle-LWC vs. bottom-LWC (r ≥ 0.92) were identified. The effects of wheat spikes on spectral reflectance mainly occurred in near infrared to shortwave infrared regions, which then decreased the accuracy of LWC estimation. Spectral indices at the middle layer outperformed the other two layers in LWC assessment and were less susceptible to wheat spikes effects, in particular, the newly proposed narrow-band WI-4 and NDWSI-4 indices exhibited great potential in tracking the changes of middle-LWC (R2 = 0.82 and 0.84, respectively). By taking into account the effects of wheat spikes and the interrelationship of vertical LWC within canopies, an indirect induction strategy was developed for modeling the upper-LWC and bottom-LWC. It was found that the indirect induction models based on the WI-4 and NDWSI-4 indices were more effective than the models obtained from conventional direct estimation method, with R2 of 0.78 and 0.81 for the upper-LWC estimation, and 0.75 and 0.74 for the bottom-LWC estimation, respectively.

scholarly journals Inverse modeling of CO<sub>2</sub> sources and sinks using satellite data: a synthetic inter-comparison of measurement techniques and their performance as a function of space and time

2004 ◽  
Vol 4 (2) ◽  
pp. 523-538 ◽  
Author(s):  
S. Houweling ◽  
F.-M. Breon ◽  
I. Aben ◽  
C. Rödenbeck ◽  
M. Gloor ◽  
...  
Keyword(s):  
Near Infrared ◽  
Spatial Scales ◽  
Measurement Techniques ◽  
Data Retrieval ◽  
Sources And Sinks ◽  
Space And Time ◽  
Infra Red ◽  
Temporal And Spatial ◽  
Source And Sink ◽  
Satellite Instruments

Abstract. Currently two polar orbiting satellite instruments measure CO2 concentrations in the Earth's atmosphere, while other missions are planned for the coming years. In the future such instruments might become powerful tools for monitoring changes in the atmospheric CO2 abundance and to improve our quantitative understanding of the leading processes controlling this. At the moment, however, we are still in an exploratory phase where first experiences are collected and promising new space-based measurement concepts are investigated. This study assesses the potential of some of these concepts to improve CO2 source and sink estimates obtained from inverse modelling. For this purpose the performance of existing and planned satellite instruments is quantified by synthetic simulations of their ability to reduce the uncertainty of the current source and sink estimates in comparison with the existing ground-based network of sampling sites. Our high resolution inversion of sources and sinks (at 8°x10°) allows us to investigate the variation of instrument performance in space and time and at various temporal and spatial scales. The results of our synthetic tests clearly indicate that the satellite performance increases with increasing sensitivity of the instrument to CO2 near the Earth's surface, favoring the near infra-red technique. Thermal infrared instruments, on the contrary, reach a better global coverage, because the performance in the near infrared is reduced over the oceans owing to a low surface albedo. Near infra-red sounders can compensate for this by measuring in sun-glint, which will allow accurate measurements over the oceans, at the cost, however, of a lower measurement density. Overall, the sun-glint pointing near infrared instrument is the most promising concept of those tested. We show that the ability of satellite instruments to resolve fluxes at smaller temporal and spatial scales is also related to surface sensitivity. All the satellite instruments performed relatively well over the continents resulting mainly from the larger prior flux uncertainties over land than over the oceans. In addition, the surface networks are rather sparse over land increasing the additional benefit of satellite measurements there. Globally, challenging satellite instrument precisions are needed to compete with the current surface network (about 1ppm for weekly and 8°x10° averaged SCIAMACHY columns). Regionally, however, these requirements relax considerably, increasing to 5ppm for SCIAMACHY over tropical continents. This points not only to an interesting research area using SCIAMACHY data, but also to the fact that satellite requirements should not be quantified by only a single number. The applicability of our synthetic results to real satellite instruments is limited by rather crude representations of instrument and data retrieval related uncertainties. This should receive high priority in future work.

scholarly journals Spatial and seasonal variability of heterotrophic and autotrophic soil respiration in a winter wheat stand

2010 ◽  
Vol 7 (6) ◽  
pp. 9137-9173 ◽  
Author(s):  
N. Prolingheuer ◽  
B. Scharnagl ◽  
A. Graf ◽  
H. Vereecken ◽  
M. Herbst
Keyword(s):  
Soil Respiration ◽  
Winter Wheat ◽  
Spatial Variability ◽  
Soil Temperature ◽  
Water Content ◽  
Sampling Point ◽  
Soil Organisms ◽  
Area Index ◽  
Temporal And Spatial Variability ◽  
Temporal And Spatial

Abstract. Soil respiration (Rs), the sum of respiration by soil organisms (Rh) and roots (Ra), is known to be highly variable in both, space and time. There is less information available about the behaviour of Rh and Ra in time and particularly in space. The objective of this study was to quantify the contribution of each component to the temporal and spatial variability of soil respiration in a winter wheat stand. We measured soil respiration from March to July 2009 by closed-dynamic chambers for 61 sampling points in a 50×50 m plot in a winter wheat stand close to Jülich, Germany. Each sampling point was equipped with a 7 cm soil collar to measure total Rs and a 50 cm soil collar to exclude roots and to measure Rh only. Ra was assumed to equal Rs−Rh. Simultaneously, soil temperature and soil water content were measured in 6 cm depth. Biweekly the temporal development of the leaf area index was measured. On average, the heterotrophic contribution to Rs was 69% and thus higher than the autotrophic contribution. Seasonal changes of soil temperature and especially water content explained well the temporal variability of Rs (r2=0.74) and Ra (r2=0.80). Spatial variability of Ra was on average much higher (CV=88%) than the spatial variability of Rh (CV=30%). However, Rh was mainly randomly distributed in space, whereas Ra showed spatial autocorrelation. Spatial correlation and cross-variograms showed a significant spatial dependence of Rs on Ra. From our results we concluded that spatial variability of soil respiration in a winter wheat stand represented mainly the spatial variability of the autotrophic component.

scholarly journals Retrieval of Leaf Area Index Using Sentinel-2 Imagery in a Mixed Mediterranean Forest Area

2020 ◽  
Vol 9 (11) ◽  
pp. 622
Author(s):  
Irene Chrysafis ◽  
Georgios Korakis ◽  
Apostolos P. Kyriazopoulos ◽  
Giorgos Mallinis
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Model Development ◽  
Mixed Forest ◽  
Selection Procedure ◽  
Coefficient Of Determination ◽  
Spectral Indices ◽  
Area Index ◽  
Spectral Bands ◽  
Sentinel 2

Leaf area index (LAI) is a crucial biophysical indicator for assessing and monitoring the structure and functions of forest ecosystems. Improvements in remote sensing instrumental characteristics and the availability of more efficient statistical algorithms, elevate the potential for more accurate models of vegetation biophysical properties including LAI. The aim of this study was to assess the spectral information of Sentinel-2 MSI satellite imagery for the retrieval of LAI over a mixed forest ecosystem located in northwest Greece. Forty-eight field plots were visited for the collection of ground LAI measurements using an ACCUPAR LP-80: PAR & LAI Ceptometer. Spectral bands and spectral indices were used for LAI model development using the Gaussian processes regression (GPR) algorithm. A variable selection procedure was applied to improve the model’s prediction accuracy, and variable importance was investigated for identifying the most informative variables. The model resulting from spectral indices’ variables selection produced the most precise predictions of LAI with a coefficient of determination of 0.854. Shortwave infrared bands and the normalized canopy index (NCI) were identified as the most important features for LAI prediction.

scholarly journals Combining Spectral and Texture Features of UAV Images for the Remote Estimation of Rice LAI throughout the Entire Growing Season

2021 ◽  
Vol 13 (15) ◽  
pp. 3001
Author(s):  
Kaili Yang ◽  
Yan Gong ◽  
Shenghui Fang ◽  
Bo Duan ◽  
Ningge Yuan ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Morphological Changes ◽  
Texture Features ◽  
Growing Season ◽  
Estimation Accuracy ◽  
Coefficient Of Determination ◽  
Spectral Information ◽  
Growth Stages ◽  
Area Index ◽  
Texture Information

Leaf area index (LAI) estimation is very important, and not only for canopy structure analysis and yield prediction. The unmanned aerial vehicle (UAV) serves as a promising solution for LAI estimation due to its great applicability and flexibility. At present, vegetation index (VI) is still the most widely used method in LAI estimation because of its fast speed and simple calculation. However, VI only reflects the spectral information and ignores the texture information of images, so it is difficult to adapt to the unique and complex morphological changes of rice in different growth stages. In this study we put forward a novel method by combining the texture information derived from the local binary pattern and variance features (LBP and VAR) with the spectral information based on VI to improve the estimation accuracy of rice LAI throughout the entire growing season. The multitemporal images of two study areas located in Hainan and Hubei were acquired by a 12-band camera, and the main typical bands for constituting VIs such as green, red, red edge, and near-infrared were selected to analyze their changes in spectrum and texture during the entire growing season. After the mathematical combination of plot-level spectrum and texture values, new indices were constructed to estimate rice LAI. Comparing the corresponding VI, the new indices were all less sensitive to the appearance of panicles and slightly weakened the saturation issue. The coefficient of determination (R2) can be improved for all tested VIs throughout the entire growing season. The results showed that the combination of spectral and texture features exhibited a better predictive ability than VI for estimating rice LAI. This method only utilized the texture and spectral information of the UAV image itself, which is fast, easy to operate, does not need manual intervention, and can be a low-cost method for monitoring crop growth.

scholarly journals Inverse modeling of CO<sub>2</sub> sources and sinks using satellite data: A synthetic inter-comparison of measurement techniques and their performance as a function of space and time

2003 ◽  
Vol 3 (5) ◽  
pp. 5237-5274
Author(s):  
S. Houweling ◽  
F.-M. Breon ◽  
I. Aben ◽  
C. Rödenbeck ◽  
M. Gloor ◽  
...  
Keyword(s):  
Near Infrared ◽  
Spatial Scales ◽  
Measurement Techniques ◽  
Data Retrieval ◽  
Sources And Sinks ◽  
Space And Time ◽  
Infra Red ◽  
Temporal And Spatial ◽  
Source And Sink ◽  
Satellite Instruments

Abstract. Currently two polar orbiting satellite instruments measure CO2 concentrations in the Earth's atmosphere, while other missions are planned for the coming years. In the future such instruments might become powerful tools for monitoring changes in the atmospheric CO2 abundance and to improve our quantitative understanding of the leading processes controlling this. At the moment, however, we are still in an exploratory phase where first experiences are collected and promising new space-based measurement concepts are investigated. This study assesses the potential of some of these concepts to improve CO2 source and sink estimates obtained from inverse modelling. For this purpose the performance of existing and planned satellite instruments is quantified by synthetic simulations of their ability to reduce the uncertainty of the current source and sink estimates in comparison with the existing ground-based network of sampling sites. Our high resolution inversion of sources and sinks (at 8º x 10º allows us to investigate the variation of instrument performance in space and time and at various temporal and spatial scales. The results of our synthetic tests clearly indicate that the satellite performance increases with increasing sensitivity of the instrument to CO2 near the Earth's surface, favoring the near infra-red technique. Thermal infrared instruments, on the contrary, reach a better global coverage, because the performance in the near infrared is reduced over the oceans owing to a low surface albedo. Near infra-red sounders can compensate for this by measuring in sun-glint, which will allow accurate measurements over the oceans, at the cost, however, of a lower measurement density. Overall, the sun-glint pointing near infrared instrument is the most promising concept of those tested. We show that the ability of satellite instruments to resolve fluxes at smaller temporal and spatial scales is also related to surface sensitivity. All the satellite instruments performed relatively well over the continents resulting mainly from the larger prior flux uncertainties over land than over the oceans. In addition, the surface networks are rather sparse over land increasing the additional benefit of satellite measurements there. Globally, rather challenging satellite instrument precisions are needed to compete with the surface network (about 1 ppmv for weekly and 8° × 10° averaged SCIAMACHY columns). Regionally, however, these requirements relax considerably, increasing to 5 ppmv for SCIAMACHY over tropical continents. This points not only to an interesting research area using SCIAMACHY data, but also to the fact that satellite requirements should not be quantified by only a single number. The applicability of our synthetic results to real satellite instruments is limited by rather crude representations of instrument and data retrieval related uncertainties. This should receive high priority in future work.

Plant and soil influences on estimating biomass of wheat in plant breeding plots using field spectral radiometers

1996 ◽  
Vol 47 (7) ◽  
pp. 1017 ◽  
Author(s):  
SM Bellairs ◽  
NC Turner ◽  
PT Hick ◽  
RCG Smith
Keyword(s):  
Vegetation Index ◽  
Coefficient Of Determination ◽  
Wheat Crop ◽  
Growth Stages ◽  
Experimental Conditions ◽  
Area Index ◽  
Operational Conditions ◽  
Normalised Difference Vegetation Index ◽  
Early Vigour ◽  
Dark Soil

Field spectral radiometers were used to estimate the biomass of wheat at early growth stages, as wheat breeders require a rapid, non-destructive technique to rank wheat genotypes for early vigour. Under experimental conditions, good relationships were obtained between reflectance and biomass prior to the wheat crop achieving a green area index of 1.5. When used above different soil types, good results were achieved on very uniform dark and light soils under experimental conditions, but greater differentiation between plots differing in biomass was achieved on darker soils. Similarly, under operational conditions in wheat breeders' plots, the best results were achieved against a dark soil background. Structural differences between plants also influenced solar radiation reflectance. At the Merredin site with the dark soil background, where the best correlation between reflectance and biomass was achieved, the relationship was much stronger for the more uniform genotypes at the second stage of selection than for the more heterogeneous genotypes at the first stage of selection. On these plots, the vegetation spectral indices NDVI (normalised difference vegetation index) and TSAVI (transformed soil-adjusted vegetation index) had a coefficient of determination 90-95% as good as the best regression using two wavebands. To optimise the field spectroradiometry technique for estimating early biomass, it should be applied at a weed-free site, with a uniform dark soil background and on material that is relatively homogenous in structure. We conclude that, unless these precautions are taken, the technique will have limited utility in breeding programs.

Biomass and leaf-area index maps derived from SPOT images for Toolik Lake and Imnavait Creek areas, Alaska

Polar Record ◽  
1995 ◽  
Vol 31 (177) ◽  
pp. 147-154 ◽  
Author(s):  
Margaret M. Shippert ◽  
Donald A. Walker ◽  
Nancy A. Auerbach ◽  
Brad E. Lewis
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Spatial Scales ◽  
Regression Equations ◽  
Area Index ◽  
Tundra Ecosystem ◽  
Toolik Lake

AbstractA new emphasis on understanding natural systems at large spatial scales has led to an interest in deriving ecological variables from satellite reflectance images. The normalized difference vegetation index (NDVI) is a measure of canopy greenness that can be derived from reflectances at near-infrared and red wavelengths. For this study we investigated the relationships between NDVI and leaf-area index (LAI), intercepted photosynthetically active radiation (IPAR), and biomass in an Arctic tundra ecosystem. Reflectance spectra from a portable field spectrometer, LAI, IPAR, and biomass data were collected for 180 vegetation samples near Toolik Lake and Imnavait Creek, Alaska, during July and August 1993. NDVI values were calculated from red and near-infrared reflectances of the field spectrometer spectra. Strong linear relationships are seen between mean NDVI for major vegetation categories and mean LAI and biomass. The relationship between mean NDVI and mean IPAR for these categories is not significant. Average NDVI values for major vegetation categories calculated from a SPOT image of the study area were found to be highly linearly correlated to average field NDVI measurements for the same categories. This indicates that in this case it is appropriate to apply equations derived for field-based NDVI measurements to NDVI images. Using the regression equations for those relationships, biomass and LAI images were calculated from the SPOT NDVI image. The resulting images show expected trends in LAI and biomass across the landscape.

scholarly journals Assessment of UAV-Onboard Multispectral Sensor for Non-Destructive Site-Specific Rapeseed Crop Phenotype Variable at Different Phenological Stages and Resolutions

2020 ◽  
Vol 12 (3) ◽  
pp. 397 ◽  
Author(s):  
Sadeed Hussain ◽  
Kaixiu Gao ◽  
Mairaj Din ◽  
Yongkang Gao ◽  
Zhihua Shi ◽  
...  
Keyword(s):  
Leaf Area ◽  
Polynomial Regression ◽  
Dry Weight ◽  
Empirical Method ◽  
Coefficient Of Determination ◽  
Leaf Mass Per Area ◽  
Growth Stages ◽  
Area Index ◽  
Non Destructive ◽  
Different Growth Stages

Unmanned aerial vehicles (UAVs) equipped with spectral sensors have become useful in the fast and non-destructive assessment of crop growth, endurance and resource dynamics. This study is intended to inspect the capabilities of UAV-onboard multispectral sensors for non-destructive phenotype variables, including leaf area index (LAI), leaf mass per area (LMA) and specific leaf area (SLA) of rapeseed oil at different growth stages. In addition, the raw image data with high ground resolution (20 cm) were resampled to 30, 50 and 100 cm to determine the influence of resolution on the estimation of phenotype variables by using vegetation indices (VIs). Quadratic polynomial regression was applied to the quantitative analysis at different resolutions and growth stages. The coefficient of determination (R2) and root mean square error results indicated the significant accuracy of the LAI estimation, wherein the highest R2 values were attained by RVI = 0.93 and MTVI2 = 0.89 at the elongation stage. The noise equivalent of sensitivity and uncertainty analyses at the different growth stages accounted for the sensitivity of VIs, which revealed the optimal VIs of RVI, MTVI2 and MSAVI in the LAI estimation. LMA and SLA, which showed significant accuracies at (R2 = 0.85, 0.81) and (R2 = 0.85, 0.71), were estimated on the basis of the predicted leaf dry weight and LAI at the elongation and flowering stages, respectively. No significant variations were observed in the measured regression coefficients using different resolution images. Results demonstrated the significant potential of UAV-onboard multispectral sensor and empirical method for the non-destructive retrieval of crop canopy variables.

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