SHALOM — Space-borne hyperspectral applicative land and ocean mission

Natale, V. Giancarlo; Kafri, A.; Tidhar, Gil; Chen, M.; Feingersh, Tal; Sagi, E.; Cisbani, A.; Baroni, M.; Labate, Demetrio; Nadler, Ron; Leizer, A.; Signorile, S.; Tempesta, P.; Magliozzi, M.L.; Catallo, C.; Pietropaolo, Andrea

doi:10.1109/whispers.2013.8080667

Cited by 10 publications

(4 citation statements)

References 5 publications

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“…Relatively high signal-to-noise ratios (SNR) of 400:1 for VNIR and around 180:1 for the SWIR spectrometers are targeted (Guanter et al, 2015). With EnMAP and other missions ahead, such as Italian PRISMA (Loizzo et al, 2019), the Hyperspectral Imager Suite (HISUI) (Matsunaga et al, 2017) or the Italian-Israeli Spaceborne Hyperspectral Applicative Land and Ocean Mission SHALOM (Natale et al, 2013), an era of operational large-scale monitoring systems for agricultural applications will enable the exploitation of subtle biochemical traits such as nitrogen (Hank et al, 2019). Worth to mention in this context is the ESA high-priority candidate mission CHIME, a unique visible to shortwave infrared spectrometer supposed to deliver routine hyperspectral observations for enhanced services, among others for agricultural managements (Nieke and Rast, 2018).…”

Section: Introductionmentioning

confidence: 99%

Retrieval of aboveground crop nitrogen content with a hybrid machine learning method

Berger

Verrelst

Féret

et al. 2020

International Journal of Applied Earth Observation and Geoinfor

103

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Hyperspectral acquisitions have proven to be the most informative Earth observation data source for the estimation of nitrogen (N) content, which is the main limiting nutrient for plant growth and thus agricultural production. In the past, empirical algorithms have been widely employed to retrieve information on this biochemical plant component from canopy reflectance. However, these approaches do not seek for a cause-effect relationship based on physical laws. Moreover, most studies solely relied on the correlation of chlorophyll content with nitrogen, and thus neglected the fact that most N is bound in proteins. Our study presents a hybrid retrieval method using a physically-based approach combined with machine learning regression to estimate crop N content. Within the workflow, the leaf optical properties model PROSPECT-PRO including the newly calibrated specific absorption coefficients (SAC) of proteins, was coupled with the canopy reflectance model 4SAIL to PROSAIL-PRO. The latter was then employed to generate a training database to be used for advanced probabilistic machine learning methods: a standard homoscedastic Gaussian process (GP) and a heteroscedastic GP regression that accounts for signal-to-noise relations. Both GP models have the property of providing confidence intervals for the estimates, which sets them apart from other machine learners. Moreover, a GP-based sequential backward band removal algorithm was employed to analyze the band-specific information content of PROSAIL-PRO simulated spectra for the estimation of aboveground N. Data from multiple hyperspectral field campaigns, carried out in the framework of the future satellite mission Environmental Mapping and Analysis Program (EnMAP), were exploited for validation. In these campaigns, corn and winter wheat spectra were acquired to simulate spectral EnMAP data. Moreover, destructive N measurements from leaves, stalks and fruits were collected separately to enable plant-organ-specific validation. The results showed that both GP models can provide accurate aboveground N simulations, with slightly better results of the heteroscedastic GP in terms of model testing and against in situ N measurements from leaves plus stalks, with root mean square error (RMSE) of 2.1 g/m². However, the inclusion of fruit N content for validation deteriorated the results, which can be explained by the inability of the radiation to penetrate the thick tissues of stalks, corn cobs and wheat ears. GP-based band analysis identified optimal spectral settings with ten bands mainly situated in the shortwave infrared (SWIR) spectral region. Use of well-known protein absorption bands from the literature showed comparative results. Finally, the heteroscedastic GP model was successfully applied on airborne hyperspectral data for N mapping. We conclude that GP algorithms, and in particular the heteroscedastic GP, should be implemented for global agricultural monitoring of aboveground N from future imaging spectroscopy data.

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Section: Introductionmentioning

confidence: 99%

Retrieval of aboveground crop nitrogen content with a hybrid machine learning method

Berger

Verrelst

Féret

et al. 2020

International Journal of Applied Earth Observation and Geoinfor

103

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“…With the advance of operating EO systems, e.g., Copernicus, the free and open-access earth observation data policy as well as cloud-based access and processing (e.g., DIAS), an earth observation-based Soil surveying System becomes feasible nowadays [19]. This is made even more possible by the forthcoming availability of high signal-to-noise ratio space-borne imagery spectrometers in addition to PRISMA, such as EnMAP [20], HISUI [21], SHALOM [22], NASA-SBG (Surface Biology and Geology), HySpex2 [23] and the Sentinel-10/CHIME (Copernicus Hyperspectral Imaging Mission for the Environment) [24].…”

Section: Introductionmentioning

confidence: 99%

An Analysis of Bare Soil Occurrence in Arable Croplands for Remote Sensing Topsoil Applications

et al. 2021

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A better comprehension of soil properties and processes permits a progress in agricultural management effectiveness, together with a diminution of environmental damage and more beneficial use of resources. This research investigated the usage of multispectral (Sentinel-2 MSI) satellite data at the farm/regional level, for the identification of agronomic bare soil presence, utilizing bands of the spectral range from visible to shortwave infrared. The research purpose was to assess the frequency of cloud-free bare soil time-series images available during the year in typical agricultural areas, needed for the development of digital soil mapping (DSM) approaches for agricultural applications, using hyperspectral satellite missions such as current PRISMA and the planned EnMAP or CHIME. The research exploited the Google Earth Engine platform, by processing all available cloud-free Sentinel-2 images throughout a time span of four years. Two main results were obtained: (i) bare soil frequency, indicating where and when a pixel (or an agricultural field) was detected as bare surface in three representative agricultural areas of Italy, and (ii) a temporal sensitivity analysis, providing the acquisition frequency of useful bare soil images applicable for the retrieval of soil variables of interest. It was shown that, in order to provide for an effective agricultural soil monitoring capability, a revisit frequency in the range of five to seven days is required, which is less than the planned specifications e.g., of PRISMA or CHIME hyperspectral missions, but could be addressed by combining data from the two sensors.

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“…The Soil Composite Mapping Processor (SCMaP) was devised by [64] to surmount the obstacle of insufficient soil exposure and to map soil properties in Germany. Further research advancement and improvement is expected as high signal-to-noise ratio spectrometers on space-borne imagery become available, along with the PRISMA, such as EnMAP [65], HISUI [66], SHALOM [67], HySpex [68], and the Sentinel-10/CHIME [69].…”

Section: Future Directionsmentioning

confidence: 99%

“…EnMAP, HISUI, SHALOM, HySpex2, and the Sentinel-10/CHIME could help to remotely mapping soil erodibility [65][66][67]69,206]; • GNSS and UAV platforms now provide an economical method for evaluating soil moisture content on a broad level [76,88];…”

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confidence: 99%

Unlocking the Potential of Remote Sensing in Wind Erosion Studies: A Review and Outlook for Future Directions

et al. 2023

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Remote sensing (RS) has revolutionized field data collection processes and provided timely and spatially consistent acquisition of data on the terrestrial landscape properties. This research paper investigates the relationship between Wind Erosion (WE) and Remote Sensing (RS) techniques. By examining, analyzing, and reviewing recent studies utilizing RS, we underscore the importance of wind erosion research by exploring indicators that influence the detection, evaluation, and modeling of wind erosion. Furthermore, it identifies research gaps particularly in soil erodibility estimation, soil moisture monitoring, and surface roughness assessment using RS. Overall, this research enhances our understanding of WE and RS and offers insights into future research directions. To conduct this study, we employed a two-fold approach. First, we utilized a non-systematic review approach by accessing the Global Applications of Soil Erosion Modelling Tracker (GASEMT) database. Subsequently, we conducted a systematic review of the relevant literature on wind erosion and remote sensing in the core collection of the Web of Science (WoS) database. Additionally, we employed the VOSviewer bibliometric software to generate a cooperative keyword network analysis, facilitating the advancements and identifying emerging areas of WE and RS research. With a non-systematic review, we focused on examining the current state and potential of remote sensing for mapping and analyzing following indicators of wind erosion modelling: (1) soil erodibility; (2) soil moisture; (3) surface roughness; (4) vegetation cover; (5) wind barriers; and (6) wind erosion mapping. Our study highlights the widespread utilization of freely available RS data, such as MODIS and Landsat, for WE modeling. However, we also acknowledge the limitations of high resolution sensors due to their high costs. RS techniques offer an efficient and cost-effective approach for mapping erosion at various scales and call for a more comprehensive and detailed assessment of soil erosion at regional scales. These findings provide valuable guidance for future research endeavors in this domain.

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SHALOM — Space-borne hyperspectral applicative land and ocean mission

Cited by 10 publications

References 5 publications

Retrieval of aboveground crop nitrogen content with a hybrid machine learning method

Retrieval of aboveground crop nitrogen content with a hybrid machine learning method

An Analysis of Bare Soil Occurrence in Arable Croplands for Remote Sensing Topsoil Applications

Unlocking the Potential of Remote Sensing in Wind Erosion Studies: A Review and Outlook for Future Directions

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