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The objective of this paper is to investigate the relationship between a wide range of hyperspectral chlorophyll indices and wheat crop chlorophyll content using laboratory measurements. These measurements included the GER-3700 spectroradiometric data, leaf chlorophyll content using the Soil-Plant Analyses Development (SPAD)-502 meter and leaf chlorophyll content estimated from chemical laboratory analysis. The SPAD-502 readings were correlated with leaf chlorophyll content extracted in the laboratory to establish calibration equations for the computation of chlorophyll-ab (Chl-ab) and chlorophyll-a (Chl-a) content. This resulted in a coefficient of determination ([R.sup.2]) of 0.72 for the Chl-ab content and 0.69 for the Chl-a content and a root mean square error (RMSE) of 3.53 and 1.94 [micro]g/[cm.sup.2], respectively. These estimates were used to establish relationships against hyperspectral chlorophyll indices calculated from the GER-3700 data. From the investigated indices, the NPCI showed the best results with [R.sup.2] of 0.84 and RMSE of 11.0. The other indices, such as GNDVI, OSAVI, PSS[R.sub.a], PSN[D.sub.a], CAI, HNDVI, and MTCI did not perform satisfactorily. The better ones, but still showing a relatively week relationship with leaf chlorophyll content, are the indices NDPI, SIPI, PRI and SRPI with [R.sup.2]'s of 0.56, 0.62, 0.54, and 0.57, respectively and RMSEs of 11.06, 10.27, 11.32, and 10.96 [micro]g/[cm.sup.2], respectively. Index Terms--Chlorophyll indices, hyperspectral remote sensing, laboratory measurements, precision agriculture, soil-plant analyses development (SPAD)-502, validation, wheat crop.

The calibration of airborne and satellite remote sensing sensors is a fundamental step for the rigorous validation of products derived from satellite data. Because of the inaccessibility of Earth Observation Satellites on orbit, the direct calibration method based on a test site with ground reference data is often considered necessary. However, the problem of radiometric spatial uniformity and temporal stability of test sites constitutes an important issue in the accuracy achieved in calibration operations and the long-term characterization of satellite sensor radiometry. Generally, the coefficient of variation and semivariograms are the most widely used tools for evaluating the radiometric uniformity and stability of a calibration site. In this study, we analyze for the first time the potential of Getis statistics compared to the coefficient of variation for the study of the radiometric spatial uniformity and temporal stability of the Lunar Lake Playa, Nevada (LLPN) test site. The results obtained show the potential and the importance of the synergy generated by these two methods for analyzing the radiometric temporal stability of the LLPN site. Getis statistics provide an excellent spatial analysis of the site while the coefficient of variation provides complementary information on the temporal evolution of the site. Index Terms--Coefficient of variation (CV), Getis statistics, Lunar Lake Playa, Nevada (LLPN), optical sensor, radiometric calibration, test sites.

This paper assesses the reflectance difference values between the respective spectral bands in the visible and near-infrared (VNIR) of Sentinel 2A/2B Multi-Spectral Instrument (MSI) and Landsat 8/9 Operational Land Imager (OLI) sensors for seagrass, algae, and mixed species discrimination and monitoring in a shallow marine environment southeast of Bahrain Island in the Arabian Gulf. To achieve these, a field survey was conducted to collect samples of seawater, underwater sediments, seagrass (Halodule uninervis and Halophila stipulacea), and algae (green and brown). In addition, an experimental mode was established in a goniometric laboratory to simulate the marine environment, and spectral measurements were performed using an Analytical Spectral Devices (ASD) spectroradiometer. Measured spectra and their transformation using the continuum-removed reflectance spectral (CRRS) approach were analyzed to assess spectral separability among separate or mixed species at varying coverage rates. Afterward, the spectra were resampled and convolved in the solar-reflective spectral bands of MSI and OLI sensors and converted into water vegetation indices (WVIs) to investigate the potential of red, green, and blue bands for seagrass and algae species discrimination. The results of spectral and CRRS analyses highlighted the importance of the blue, green, and near-infrared (NIR) wavelengths for seagrass and algae detection and likely discrimination based on hyperspectral measurements. However, when resampled and convolved in MSI and OLI bands, spectral information loses the specific and unique absorption features and becomes more generalized and less precise. Therefore, relying on the multispectral bandwidth of MSI and OLI sensors, it is difficult or even impossible to differentiate or to map seagrass and algae individually at the species level. Instead of the red band, the integration of the blue or the green band in WVI increases their power to discriminate submerged aquatic vegetation (SAV), particularly the water adjusted vegetation index (WAVI), water enhanced vegetation index (WEVI), and water transformed difference vegetation index (WTDVI). These results corroborate the spectral and the CRRS analyses. However, despite the power of blue wavelength to penetrate deeper into the water, it also leads to a relative overestimation of dense SAV coverage due to more scattering in this part of the spectrum. Furthermore, statistical fits (p<0.05) between the reflectance in the respective VNIR bands of MSI and OLI revealed excellent linear relationships (R2 of 0.999) with insignificant root mean square difference (RMSD) (≤ 0.0015). Important agreement (0.63 ≤ R2 ≤ 0.96) was also obtained between respective WVI regardless of the integrated spectral bands (i.e., red, green, and blue), yielding insignificant RMSD (≤ 0.01). Accordingly, these results pointed out that MSI and OLI sensors are spectrally similar, and their data can be used jointly to monitor accurately the spatial distribution of SAV and its dynamic in time and space in shallow marine environments, provided that rigorous data pre-processing issues are addressed. [ABSTRACT FROM AUTHOR]

Landscape spatial metrics were investigated to assess changes of multi-temporal vegetation cover within the Tlemcen national park (TNP) in north-western Algeria over 31 years. Five landscape metrics, namely number of patches (NP), landscape shape index (LSI), class area (CA), largest patch index (LPI), and percentage of landscape (PLAND) were applied and analysed to assess the fragmentation in the TNP at landscape and class levels. Three Landsat images acquired at three periods of time (1987, 1999 and 2018) with different sensors, namely Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+) and Operational Land Imager (OLI) were used. After rigorous pre-processing steps, these images were transformed and thresholded into Normalised Difference Vegetation Index (NDVI) maps that represent the major vegetation cover classes in TNP. The obtained results revealed that during the study period the TNP has undergone a progressive and significant landscape fragmentation. [ABSTRACT FROM AUTHOR]

This paper assesses the reflectance difference values between the homologous visible and near-infrared (VNIR) spectral bands of Sentinel-MSI-2A/2B and Landsat-OLI-8/9 sensors for seagrass, algae, and mixed species discrimination and monitoring in a shallow marine environment southeastern of Bahrain in the Arabian Gulf. To achieve these, a field survey was conducted to collect samples of seawater, underwater sediments, seagrass (Halodule uninebell.netrvis and Halophila stipulacea) and algae (green and brown). As well, an experimental mode was established in a Goniometric-Laboratory to simulate the marine environment, and spectral measurements were performed using an ASD spectroradiometer over each separate and different case of seagrass and algae mixed species at different coverage rate (0, 10, 30, 75, and 100 %) considering the bottom sediments with clear and dark colors. All measured spectra were analyzed and transformed using continuum-removed reflectance spectral (CRRS) approach to assess spectral separability among separate or mixed species at varying coverage rates. Afterward, the spectra were resampled and convolved in the solar-reflective spectral bands of MSI and OLI sensors and converted into water vegetation indices (WVI) to investigate the potential of red, green, and blue bands for seagrass and algae species discrimination. For comparison and sensor differences quantification, statistical fits (p < 0.05) were conducted between reflectances in homologous bands and also between homologous WVI; as well as the coefficient of determination (R²) and root mean square difference (RMSD) were calculated. The results of spectral and CRRS analyses highlighted the importance of the blue, green, and NIR wavelengths for seagrass and algae detection and probable discrimination based on hyperspectral measurements. However, when resampled and convolved in MSI and OLI bands, spectral information loses the specific and unique absorption features and becomes more generalized and less precise. Therefore, relying on the multispectral bandwidth of MSI and OLI sensors, it is difficult or even impossible to differentiate or to map seagrass and algae individually at the species level. Additionally, instead of the red band, the integration of the blue or the green bands in WVI increases their discriminating power of submerged aquatic vegetation (SAV), particularly Water Adjusted Vegetation Index (WAVI), Water Enhance Vegetation Index (WEVI), and Water Transformed Vegetation Index (WTDVI) indices. These results corroborate the spectral analysis and the CRRS transformations that the blue and green electromagnetic radiation allows better marine vegetation differentiation. However, despite the power of blue wavelength to penetrate deeper into the water, it also leads to a relative overestimation of dense SAV coverage due to the higher scattering in this part of the spectrum. Furthermore, statistical fits between the reflectance in the VNIR homologous bands of SMI and OLI revealed excellent linear relationships (R² of 0.999) with insignificant RMSD (≤ 0.0015). Important agreements (0.63 ≤ R² ≤ 0.96) were also obtained between homologous WVI regardless of the integrated spectral bands (i.e., red, green, and blue), yielding insignificant RMSD (≤ 0.01). Accordingly, these results pointed out that MSI and OLI sensors are spectrally similar, and their data can be used jointly to monitor accurately the spatial distribution of SAV and its dynamic in time and space in shallow marine environment, provided that rigorous data pre-processing issues are addressed. [ABSTRACT FROM AUTHOR]

Sound navigating and ranging (SONAR) detection systems can provide valuable information for navigation and security, especially in shallow coastal areas. The last few years have seen an important increase in the volume of bathymetric data produced by Multi-Beam Echo-sounder Systems (MBES). Recently, the General Bathymetric Chart of the Oceans (GEBCO) released these MBES dataset preprocessed and processed with Computer Aided Resource Information System (CARIS) for public domain use. For the first time, this research focuses on the validation of these released MBES-CARIS dataset performance and robustness for bathymetric mapping of shallow water at the regional scale in the Kingdom of Bahrain (Arabian Gulf). The data were imported, converted and processed in a GIS environment. Only area that covers the Bahrain national water boundary was extracted, avoiding the land surfaces. As the released dataset were stored in a node-grid points uniformly spaced with approximately 923 m and 834 m in north and west directions, respectively, simple kriging was used for densification and bathymetric continuous surface map derivation with a 30 by 30 m pixel size. In addition to dataset cross-validation, 1200 bathymetric points representing different water depths between 0 and -30 m were selected randomly and extracted from a medium scale (1:100,000) nautical map, and they were used for validation purposes. The cross-validation results showed that the modeled semi-variogram was adjusted appropriately assuring satisfactory results. Moreover, the validation results by reference to the nautical map showed that when we consider the total validation points with different water depths, linear statistical regression analysis at a 95% confidence level (p < 0.05) provide a good coefficient of correlation (R² = 0.95), a good index of agreement (D = 0.82), and a root mean square error (RMSE) of 1.34 m. However, when we consider only the validation points (~800) with depth lower than -10 m, both R² and D decreased to 0.79 and 0.52, respectively, while the RMSE increased to 1.92 m. Otherwise, when we consider exclusively shallow water points (~400) with a depth higher than -10 m, the results showed a very significant R² (0.97), a good D (0.84) and a low RMSE (0.51 m). Certainly, the released MBES-CARIS data are more appropriate for shallow water bathymetric mapping. However, for the relatively deeper areas the obtained results are relatively less accurate because probably the MBSE did not cover the bottom in several deeper pockmarks as the rapid change in depth. Possibly the steep slopes and the rough seafloor affect the integrity of the acquired raw data. Moreover, the interpolation of the missed areas' values between MBSE acquisition data points may not reflect the true depths of these areas. It is possible also that the nautical map used for validation was not established with a good accuracy in the deeper regions. [ABSTRACT FROM AUTHOR]

Soil management practices that maintain crop residue cover and reduce tillage improve soil structure, increase organic matter content in the soil, positively influence water infiltration, evaporation and soil temperature, and play an important role in fixing CO2 in the soil. Consequently, good residue management practices on agricultural land have many positive impacts on soil quality, crop production quality and decrease the rate of soil erosion. Several studies have been undertaken to develop and test methods to derive information on crop residue cover and soil tillage using empirical and semi-empirical methods in combination with remote sensing data. However, these methods are generally not sufficiently rigorous and accurate for characterizing the spatial variability of crop residue cover in agricultural fields. The goal of this research is to investigate the potential of hyperspectral Hyperion (Earth Observing-1, EO-1) data and constrained linear spectral mixture analysis (CLSMA) for percent crop residue cover estimation and mapping. Hyperion data were acquired together with ground-reference measurements for validation purposes at the beginning of the agricultural season (prior to spring crop planting) in Saskatchewan (Canada). At this time, only bare soil and crop residue were present with no crop cover development. In order to extract the crop residue fraction, the images were preprocessed, and then unmixed considering the entire spectral range (427 nm-2355 nm) and the pure spectra (endmember). The results showed that the correlation between ground-reference measurements and extracted fractions from the Hyperion data using CLMSA showed that the model was overall a very good predictor for crop residue percent cover (index of agreement (D) of 0.94, coefficient of determination (R2) of 0.73 and root mean square error (RMSE) of 8.7%) and soil percent cover (D of 0.91, R2 of 0.68 and RMSE of 10.3%). This performance of Hyperion is mainly due to the spectral band characteristics, especially the availability of contiguous narrow bands in the short-wave infrared (SWIR) region, which is sensitive to the residue (lignin and cellulose absorption features). [ABSTRACT FROM AUTHOR]

The aim of this study is to map the areas exposed to water erosion risks in the High Atlas Mountains of Morocco around the Hassan-I dam. The methodology is based on the analysis of the water power index (WPI) as a hydrological parameter, the vegetation cover, and the litho-logical units. The WPI was derived from a Digital Elevation Model (DEM) and the litho-logical units and vegetation cover were derived from Advanced Land Imager sensor on the Earth Observing-1 satellite platform. The image was corrected from radiometric and atmospheric effects, and geometrically rectified using a DEM and grounds control points. These variables were integrated in a Geographical Information Systems environment, and Multi-Criteria Analyses were used to derive the water erosion risks map pointing out the most exposed areas requiring the implementation of suitable conservation measures. The validation of the obtained results shows the simplicity and the potential of this approach for water erosion risks mapping. [ABSTRACT FROM PUBLISHER]

The calibration of airborne and satellite remote sensing sensors is a fundamental step for the rigorous validation of products derived from satellite data. Because of the inaccessibility of Earth Observation Satellites on orbit, the direct calibration method based on a test site with ground reference data is often considered necessary. However, the problem of radiometric spatial uniformity and temporal stability of test sites constitutes an important issue in the accuracy achieved in calibration operations and the long-term characterization of satellite sensor radiometry. Generally, the coefficient of variation and semivariograms are the most widely used tools for evaluating the radiometric uniformity and stability of a calibration site. In this study, we analyze for the first time the potential of Getis statistics compared to the coefficient of variation for the study of the radiometric spatial uniformity and temporal stability of the Lunar Lake Playa, Nevada (LLPN) test site. The results obtained show the potential and the importance of the synergy generated by these two methods for analyzing the radiometric temporal stability of the LLPN site. Getis statistics provide an excellent spatial analysis of the site while the coefficient of variation provides complementary information on the temporal evolution of the site. [ABSTRACT FROM AUTHOR]

This paper examines the climate change impact on the spatiotemporal soil salinity dynamics during the last 30 years (1987–2017) in the arid landscape. The state of Kuwait, located at the northwest Arabian Peninsula, was selected as a pilot study area. To achieve this, a Landsat- Operational Land Imager (OLI) image acquired thereabouts simultaneously to a field survey was preprocessed and processed to derive a soil salinity map using a previously developed semi-empirical predictive model (SEPM). During the field survey, 100 geo-referenced soil samples were collected representing different soil salinity classes (non-saline, low, moderate, high, very high and extreme salinity). The laboratory analysis of soil samples was accomplished to measure the electrical conductivity (EC-Lab) to validate the selected and used SEPM. The results are statistically analyzed (p ˂ 0.05) to determine whether the differences are significant between the predicted salinity (EC-Predicted) and the measured ground truth (EC-Lab). Subsequently, the Landsat serial time's datasets acquired over the study area with the Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) and OLI sensors during the last three decades over the intervals (1987, 1992, 1998, 2000, 2002, 2006, 2009, 2013, 2016 and 2017) were radiometrically calibrated. Likewise, the datasets were atmospherically and spectrally normalized by applying a semi-empirical line approach (SELA) based on the pseudo-invariant targets. Afterwards, a series of soil salinity maps were derived through the application of the SEPM on the images sequence. The trend of salinity changes was statistically tested according to climatic variables (temperatures and precipitations). The results revealed that the EC-Predicted validation display a best fits in comparison to the EC-Lab by indicating a good index of agreement (D = 0.84), an excellent correlation coefficient (R2 = 0.97) and low overall root mean square error (RMSE) (13%). This also demonstrates the validity of SEPM to be applicable to the other images acquired multi-temporally. For cross-calibration among the Landsat serial time's datasets, the SELA performed significantly with an RMSE ≤ ± 5% between all homologous spectral reflectances bands of the considered sensors. This accuracy is considered suitable and fits well the calibration standards of TM, ETM+ and OLI sensors for multi-temporal studies. Moreover, remarkable changes of soil salinity were observed in response to changes in climate that have warmed by more than 1.1 °C with a drastic decrease in precipitations during the last 30 years over the study area. Thus, salinized soils have expanded continuously in space and time and significantly correlated to precipitation rates (R2 = 0.73 and D = 0.85). [ABSTRACT FROM AUTHOR]

Depending on the band position on the electromagnetic spectrum, optical and electronic characteristics, sensors collect the reflected energy by the Earth’s surface and the atmosphere. Currently, the availability of the new generation of medium resolution, such as the Multi-Spectral Instrument (MSI) on board the Sentinel-2 satellite, offers new opportunities for long-term high-temporal frequency for Earth’s surfaces observation and monitoring. This paper focuses on the analysis and the comparison of the visible, the near-infrared (VNIR), and the shortwave infrared (SWIR) spectral bands of the MSI for soil salinity discrimination in an arid landscape. To achieve these, a field campaign was organized, and 160 soil samples were collected with various degrees of soil salinity, including non-saline soil samples. The bidirectional reflectance factor was measured above each soil sample in a goniometric laboratory using an ASD (Analytical Spectral Devices) spectroradiometer. In the laboratory work, pHs, electrical conductivity (EC-Lab), and the major soluble cations (Na+, K+, Ca2++, and Mg2+) and anions (CO32−, HCO3−, Cl−, and SO42−) were measured using extraction from a saturated soil paste, and the sodium adsorption ratio (SAR) was calculated using a standard procedure. These parameters, in addition to the field observations, were used to interpret and investigate the spectroradiometric measurements and their relevant transformations using the continuum removed reflectance spectrum (CRRS) and the first derivative (FD). Moreover, the acquired spectra over all the soil samples were resampled and convolved in the solar-reflective spectral bands using the Canadian Modified Herman transfer radiative code (CAM5S) and the relative spectral response profiles characterizing the Sentinel-MSI band filters. The statistical analyses conducted were based on the second-order polynomial regression (p < 0.05) between the measured EC-Lab and the reflectances in the MSI convolved spectral bands. The results obtained indicate the limitation of VNIR bands and the potential of SWIR domain for soil salinity classes’ discrimination. The CRRS and the FD analyses highlighted a serious spectral-signal confusion between the salt and the soil optical properties (i.e., color and brightness) in the VNIR bands. Likewise, the results stressed the independence of the SWIR domain vis-a-vis these soil artifacts and its capability to differentiate significantly among several soil salinity classes. Moreover, the statistical fit between each MSI individual spectral band and EC-Lab corroborates this trend, which revealed that only the SWIR bands were correlated significantly (R2 of 50% and 64%, for SWIR-1 and SWIR-2, respectively), while the R2 between the VNIR bands and EC-Lab remains less than 9%. According to the convergence of these four independent analysis methods, it is concluded that the Sentinel-MSI SWIR bands are excellent candidates for an integration in soil salinity modeling and monitoring at local, regional, and global scales. [ABSTRACT FROM AUTHOR]