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Ranghetti M, Boschetti M, Ranghetti L, Tagliabue G, Panigada C, Gianinetto M, Verrelst J, Candiani G. Assessment of maize nitrogen uptake from PRISMA hyperspectral data through hybrid modelling. EUROPEAN JOURNAL OF REMOTE SENSING 2023; 56:22797254.2022.2117650. [PMID: 38239331 PMCID: PMC7615541 DOI: 10.1080/22797254.2022.2117650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 08/23/2022] [Indexed: 01/22/2024]
Abstract
The spaceborne imaging spectroscopy mission PRecursore IperSpettrale della Missione Applicativa (PRISMA), launched on 22 March 2019 by the Italian Space Agency, opens new opportunities in many scientific domains, including precision farming and sustainable agriculture. This new Earth Observation (EO) data stream requires new-generation approaches for the estimation of important biophysical crop variables (BVs). In this framework, this study evaluated a hybrid approach, combining the radiative transfer model PROSAIL-PRO and several machine learning (ML) regression algorithms, for the retrieval of canopy chlorophyll content (CCC) and canopy nitrogen content (CNC) from synthetic PRISMA data. PRISMA-like data were simulated from two images acquired by the airborne sensor HyPlant, during a campaign performed in Grosseto (Italy) in 2018. CCC and CNC estimations, assessed from the best performing ML algorithms, were used to define two relations with plant nitrogen uptake (PNU). CNC proved to be slightly more correlated to PNU than CCC (R2 = 0.82 and R2 = 0.80, respectively). The CNC-PNU model was then applied to actual PRISMA images acquired in 2020. The results showed that the estimated PNU values are within the expected ranges, and the temporal trends are compatible with plant phenology stages.
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Affiliation(s)
- Marina Ranghetti
- Institute for Electromagnetic Sensing of the Environment (IREA), National Research Council of Italy, Milano, Italy
| | - Mirco Boschetti
- Institute for Electromagnetic Sensing of the Environment (IREA), National Research Council of Italy, Milano, Italy
| | - Luigi Ranghetti
- Institute for Electromagnetic Sensing of the Environment (IREA), National Research Council of Italy, Milano, Italy
| | - Giulia Tagliabue
- Remote Sensing of Environmental Dynamics Laboratory, Dipartimento di Scienze dell’Ambiente e della Terra, Università degli Studi di Milano - Bicocca, Milano, Italy
| | - Cinzia Panigada
- Remote Sensing of Environmental Dynamics Laboratory, Dipartimento di Scienze dell’Ambiente e della Terra, Università degli Studi di Milano - Bicocca, Milano, Italy
| | - Marco Gianinetto
- Institute for Electromagnetic Sensing of the Environment (IREA), National Research Council of Italy, Milano, Italy
- Department of Architecture, Built Environment and Construction Engineering (DABC), Milano, Italy
| | - Jochem Verrelst
- Image Processing Laboratory (IPL), Parc Científic, University of Valencia, Paterna, Valencia, Spain
| | - Gabriele Candiani
- Institute for Electromagnetic Sensing of the Environment (IREA), National Research Council of Italy, Milano, Italy
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Genangeli A, Avola G, Bindi M, Cantini C, Cellini F, Grillo S, Petrozza A, Riggi E, Ruggiero A, Summerer S, Tedeschi A, Gioli B. Low-Cost Hyperspectral Imaging to Detect Drought Stress in High-Throughput Phenotyping. PLANTS (BASEL, SWITZERLAND) 2023; 12:1730. [PMID: 37111953 PMCID: PMC10143644 DOI: 10.3390/plants12081730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 04/13/2023] [Accepted: 04/19/2023] [Indexed: 06/19/2023]
Abstract
Recent developments in low-cost imaging hyperspectral cameras have opened up new possibilities for high-throughput phenotyping (HTP), allowing for high-resolution spectral data to be obtained in the visible and near-infrared spectral range. This study presents, for the first time, the integration of a low-cost hyperspectral camera Senop HSC-2 into an HTP platform to evaluate the drought stress resistance and physiological response of four tomato genotypes (770P, 990P, Red Setter and Torremaggiore) during two cycles of well-watered and deficit irrigation. Over 120 gigabytes of hyperspectral data were collected, and an innovative segmentation method able to reduce the hyperspectral dataset by 85.5% was developed and applied. A hyperspectral index (H-index) based on the red-edge slope was selected, and its ability to discriminate stress conditions was compared with three optical indices (OIs) obtained by the HTP platform. The analysis of variance (ANOVA) applied to the OIs and H-index revealed the better capacity of the H-index to describe the dynamic of drought stress trend compared to OIs, especially in the first stress and recovery phases. Selected OIs were instead capable of describing structural changes during plant growth. Finally, the OIs and H-index results have revealed a higher susceptibility to drought stress in 770P and 990P than Red Setter and Torremaggiore genotypes.
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Affiliation(s)
- Andrea Genangeli
- Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Piazzale delle Cascine 18, 50144 Florence, Italy; (A.G.); (M.B.)
| | - Giovanni Avola
- Institute of Bioeconomy (IBE), National Research Council (CNR), Via Caproni 8, 50145 Florence, Italy; (G.A.); (C.C.); (E.R.)
| | - Marco Bindi
- Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Piazzale delle Cascine 18, 50144 Florence, Italy; (A.G.); (M.B.)
| | - Claudio Cantini
- Institute of Bioeconomy (IBE), National Research Council (CNR), Via Caproni 8, 50145 Florence, Italy; (G.A.); (C.C.); (E.R.)
| | - Francesco Cellini
- Centro Ricerche Metapontum Agrobios-Agenzia Lucana di Sviluppo e di Innovazione in Agricoltura (ALSIA), S.S. Jonica 106, km 448,2, 75010 Metaponto di Bernalda, Italy; (F.C.); (A.P.); (S.S.)
| | - Stefania Grillo
- D1 National Research Council of Italy, Institute of Biosciences and Bioresources, Via Università 133, 80055 Portici, Italy; (S.G.); (A.R.); (A.T.)
| | - Angelo Petrozza
- Centro Ricerche Metapontum Agrobios-Agenzia Lucana di Sviluppo e di Innovazione in Agricoltura (ALSIA), S.S. Jonica 106, km 448,2, 75010 Metaponto di Bernalda, Italy; (F.C.); (A.P.); (S.S.)
| | - Ezio Riggi
- Institute of Bioeconomy (IBE), National Research Council (CNR), Via Caproni 8, 50145 Florence, Italy; (G.A.); (C.C.); (E.R.)
| | - Alessandra Ruggiero
- D1 National Research Council of Italy, Institute of Biosciences and Bioresources, Via Università 133, 80055 Portici, Italy; (S.G.); (A.R.); (A.T.)
| | - Stephan Summerer
- Centro Ricerche Metapontum Agrobios-Agenzia Lucana di Sviluppo e di Innovazione in Agricoltura (ALSIA), S.S. Jonica 106, km 448,2, 75010 Metaponto di Bernalda, Italy; (F.C.); (A.P.); (S.S.)
| | - Anna Tedeschi
- D1 National Research Council of Italy, Institute of Biosciences and Bioresources, Via Università 133, 80055 Portici, Italy; (S.G.); (A.R.); (A.T.)
| | - Beniamino Gioli
- Institute of Bioeconomy (IBE), National Research Council (CNR), Via Caproni 8, 50145 Florence, Italy; (G.A.); (C.C.); (E.R.)
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Pour AB, Ranjbar H, Sekandari M, Abd El-Wahed M, Hossain MS, Hashim M, Yousefi M, Zoheir B, Wambo JDT, Muslim AM. Remote sensing for mineral exploration. GEOSPATIAL ANALYSIS APPLIED TO MINERAL EXPLORATION 2023:17-149. [DOI: 10.1016/b978-0-323-95608-6.00002-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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Evaluation of Agricultural Bare Soil Properties Retrieval from Landsat 8, Sentinel-2 and PRISMA Satellite Data. REMOTE SENSING 2022. [DOI: 10.3390/rs14030714] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
The PRISMA satellite is equipped with an advanced hyperspectral Earth observation technology capable of improving the accuracy of quantitative estimation of bio-geophysical variables in various Earth Science Applications and in particular for soil science. The purpose of this research was to evaluate the ability of the PRISMA hyperspectral imager to estimate topsoil properties (i.e., organic carbon, clay, sand, silt), in comparison with current satellite multispectral sensors. To investigate this expectation, a test was carried out using topsoil data collected in Italy following two approaches. Firstly, PRISMA, Sentinel-2 and Landsat 8 spectral simulated datasets were obtained from the spectral resampling of a laboratory soil library. Subsequently, bare soil reflectance data were obtained from two experimental areas in Italy, using real satellites images, at dates close to each other. The estimation models of soil properties were calibrated employing both Partial Least Square Regression and Cubist Regression algorithms. The results of the study revealed that the best accuracies in retrieving topsoil properties were obtained by PRISMA data, using both laboratory and real datasets. Indeed, the resampled spectra of the hyperspectral imager provided the best Ratio of Performance to Inter-Quartile distance (RPIQ) for clay (4.87), sand (3.80), and organic carbon (2.59) estimation, for the spectral soil library datasets. For the bare soil reflectance obtained from real satellite imagery, a higher level of prediction accuracy was obtained from PRISMA data, with RPIQ ± SE values of 2.32 ± 0.07 for clay, 3.85 ± 0.19 for silt, and 3.51 ± 0.16 for soil organic carbon. The results for the PRISMA hyperspectral satellite imagery with the Cubist Regression provided the best performance in the prediction of silt, sand, clay and SOC. The same variables were better estimated using PLSR models in the case of the resampled hyperspectral data. The statistical accuracy in the retrieval of SOC from real and resampled PRISMA data revealed the potential of the actual hyperspectral satellite. The results supported the expected good ability of the PRISMA imager to estimate topsoil properties.
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Berger K, Hank T, Halabuk A, Rivera-Caicedo JP, Wocher M, Mojses M, Gerhátová K, Tagliabue G, Dolz MM, Venteo ABP, Verrelst J. Assessing Non-Photosynthetic Cropland Biomass from Spaceborne Hyperspectral Imagery. REMOTE SENSING 2021; 13:4711. [PMID: 36082004 PMCID: PMC7613388 DOI: 10.3390/rs13224711] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Non-photosynthetic vegetation (NPV) biomass has been identified as a priority variable for upcoming spaceborne imaging spectroscopy missions, calling for a quantitative estimation of lignocellulosic plant material as opposed to the sole indication of surface coverage. Therefore, we propose a hybrid model for the retrieval of non-photosynthetic cropland biomass. The workflow included coupling the leaf optical model PROSPECT-PRO with the canopy reflectance model 4SAIL, which allowed us to simulate NPV biomass from carbon-based constituents (CBC) and leaf area index (LAI). PROSAIL-PRO provided a training database for a Gaussian process regression (GPR) algorithm, simulating a wide range of non-photosynthetic vegetation states. Active learning was employed to reduce and optimize the training data set. In addition, we applied spectral dimensionality reduction to condense essential information of non-photosynthetic signals. The resulting NPV-GPR model was successfully validated against soybean field data with normalized root mean square error (nRMSE) of 13.4% and a coefficient of determination (R2) of 0.85. To demonstrate mapping capability, the NPV-GPR model was tested on a PRISMA hyperspectral image acquired over agricultural areas in the North of Munich, Germany. Reliable estimates were mainly achieved over senescent vegetation areas as suggested by model uncertainties. The proposed workflow is the first step towards the quantification of non-photosynthetic cropland biomass as a next-generation product from near-term operational missions, such as CHIME.
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Affiliation(s)
- Katja Berger
- Department of Geography, Ludwig-Maximilians-Universitat Munchen (LMU), Luisenstr. 37, 80333 Munich, Germany
| | - Tobias Hank
- Department of Geography, Ludwig-Maximilians-Universitat Munchen (LMU), Luisenstr. 37, 80333 Munich, Germany
| | - Andrej Halabuk
- Institute of Landscape Ecology, Slovak Academy of Sciences, Branch Nitra, 949 01 Nitra, Slovakia
| | | | - Matthias Wocher
- Department of Geography, Ludwig-Maximilians-Universitat Munchen (LMU), Luisenstr. 37, 80333 Munich, Germany
| | - Matej Mojses
- Institute of Landscape Ecology, Slovak Academy of Sciences, Branch Nitra, 949 01 Nitra, Slovakia
| | - Katarina Gerhátová
- Institute of Landscape Ecology, Slovak Academy of Sciences, Branch Nitra, 949 01 Nitra, Slovakia
| | - Giulia Tagliabue
- Remote Sensing of Environmental Dynamics Lab, University Milano-Bicocca, 20126 Milano, Italy
| | - Miguel Morata Dolz
- Image Processing Laboratory (IPL), Parc Cientific, Universitat de Valencia, 46980 Paterna, Spain
| | - Ana Belen Pascual Venteo
- Image Processing Laboratory (IPL), Parc Cientific, Universitat de Valencia, 46980 Paterna, Spain
| | - Jochem Verrelst
- Image Processing Laboratory (IPL), Parc Cientific, Universitat de Valencia, 46980 Paterna, Spain
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Abstract
This research reports the findings of a Landsat Next expert review panel that evaluated the use of narrow shortwave infrared (SWIR) reflectance bands to measure ligno-cellulose absorption features centered near 2100 and 2300 nm, with the objective of measuring and mapping non-photosynthetic vegetation (NPV), crop residue cover, and the adoption of conservation tillage practices within agricultural landscapes. Results could also apply to detection of NPV in pasture, grazing lands, and non-agricultural settings. Currently, there are no satellite data sources that provide narrowband or hyperspectral SWIR imagery at sufficient volume to map NPV at a regional scale. The Landsat Next mission, currently under design and expected to launch in the late 2020’s, provides the opportunity for achieving increased SWIR sampling and spectral resolution with the adoption of new sensor technology. This study employed hyperspectral data collected from 916 agricultural field locations with varying fractional NPV, fractional green vegetation, and surface moisture contents. These spectra were processed to generate narrow bands with centers at 2040, 2100, 2210, 2260, and 2230 nm, at various bandwidths, that were subsequently used to derive 13 NPV spectral indices from each spectrum. For crop residues with minimal green vegetation cover, two-band indices derived from 2210 and 2260 nm bands were top performers for measuring NPV (R2 = 0.81, RMSE = 0.13) using bandwidths of 30 to 50 nm, and the addition of a third band at 2100 nm increased resistance to atmospheric correction residuals and improved mission continuity with Landsat 8 Operational Land Imager Band 7. For prediction of NPV over a full range of green vegetation cover, the Cellulose Absorption Index, derived from 2040, 2100, and 2210 nm bands, was top performer (R2 = 0.77, RMSE = 0.17), but required a narrow (≤20 nm) bandwidth at 2040 nm to avoid interference from atmospheric carbon dioxide absorption. In comparison, broadband NPV indices utilizing Landsat 8 bands centered at 1610 and 2200 nm performed poorly in measuring fractional NPV (R2 = 0.44), with significantly increased interference from green vegetation.
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