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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 15781

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Special Issue Editors

Dr. Raffaella Matarrese

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Guest Editor

Italian National Research Council - Water Research Institute, viale de Blasio 5, 70131 Bari, Italy
Interests: hyperspectral & multispectral image processing; satellite and airborne data acquisitions; environmental monitoring; precision farming; soil properties; vegetation stress indexes; water quality; atmospheric corrections; climate change; drought

Dr. Andrea Guerriero

E-Mail Website
Guest Editor

Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Via Edoardo Orabona, 4, 70125 Bari, Italy
Interests: Image processing; HPC e grid computing; data mining on satellite and airborne images; environmental monitoring and early warning; patterns retrieval

Special Issue Information

Dear Colleagues,

In recent years remote sensing techniques have had an exponential evolution thanks to technological progress: starting with the spread of multispectral cameras for satellite applications (Landasat and many others), continuing with the hyperspectral spaceborne (Hyperion, … and the new PRISMA) and airborne (AVIRIS, MIVIS, CASI) sensors and nowadays with multispectral and hyperspectral cameras for unmanned aerial vehicles.

In the last decades the purchase and the management of such images have been limiting factors to their use. Today the policy of space agencies has changed, allowing free download of high-resolution hyperspectral images. The cost of airborne or UAV sensors is also much more affordable than before. Furthermore, technological innovation makes easier to manage the image processing chain.

The availability of hyperspectral time series data for unmanned aerial vehicle, airplane and satellite systems provides insights on the spatial and temporal patterns of a variety of important biosphere/geospherе processes constituting a fundamental tool for systematic environmental monitoring. In this special issue we try to focus broadly on how the classic remote sensing products used for the study of the environment can be improved with hyperspectral data and how to use simultaneously hyperspectral data at different spatial and temporal resolutions.

Topics of interest include, but are not limited to:

climate change and environmental research
precision farming
inland, coastal and open waters status
raw material exploration and mining

Dr. Raffaella Matarrese
Dr. Andrea Guerriero
Guest Editors

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Keywords

Environmental monitoring
Climate change impact assessment
Drought and desertification
Groundwater monitoring
Water resources management
Soil degradation and soil properties
Precision farming
Water quality
Chlorophyll-a, suspended sediment, cyanobacter

Published Papers (6 papers)

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Research

22 pages, 8560 KiB

Open AccessArticle

Quantitative Study of the Effect of Water Content on Soil Texture Parameters and Organic Matter Using Proximal Visible—Near Infrared Spectroscopy

by Anas El Alem, Amal Hmaissia, Karem Chokmani and Athyna N. Cambouris

Remote Sens. 2022, 14(15), 3510; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14153510 - 22 Jul 2022

Cited by 2 | Viewed by 1494

Abstract

Continuous monitoring of soil quality is a challenging task in agricultural activity. To meet this need, scientists have succeeded in developing a quick and inexpensive method to characterize soil properties. Thus, spectroscopy has become a promising method for quantifying soil parameters. However, this method remains sensitive to several factors such as water content (WC). The present study aims to quantify the effect of WC on the estimation of soil texture parameters (sand, silt, and clay) and organic matter (OM) using spectroscopy. Reflectance measurements in the laboratory on 68 soil samples were performed by varying the WC in each sample. The analysis revealed a significant influence of WC on spectra acquired from visible to near infrared (V/NIR) spectroscopy data and that spectra can be divided into two classes. To quantify the effect of WC, calibration/validation steps were performed on soil texture parameters and OM with and without taking WC into account. Calibration was performed using the partial least square regression algorithm, and the validation was assessed using four statistical evaluation indices (R², Nash criterion (Nash), root-mean-square error (RMSE), and BIAS). Results showed a systematic increase in the accuracy of all studied soil particles when the WC is considered. Clay and OM were less influenced, while silt and sand were much more influenced by the WC. The study also highlighted that estimates of soil texture parameters using V/NIR data achieved relatively higher levels of accuracy (R² > 0.80 and Nash > 0.80) than OM estimation (R² = 0.83 and Nash = 0.78). Full article

(This article belongs to the Special Issue Application of Hyperspectral Data in Ecological Environment)

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20 pages, 6805 KiB

Open AccessArticle

Evaluating the Capability of Satellite Hyperspectral Imager, the ZY1–02D, for Topsoil Nitrogen Content Estimation and Mapping of Farmlands in Black Soil Area, China

by Zhengyuan Xu, Shengbo Chen, Bingxue Zhu, Liwen Chen, Yinghui Ye and Peng Lu

Remote Sens. 2022, 14(4), 1008; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14041008 - 18 Feb 2022

Cited by 5 | Viewed by 1937

Abstract

Soil nitrogen (N) content plays a vital role in agriculture and biogeochemical processes, ranging from the N fertilization management for intensive agricultural production to the patterns of N cycling in agroecological systems. While proximal sensing in laboratory settings can achieve ideal soil N estimation accuracy, the estimation and mapping by using remote sensing methods in a large spatial scale diplays low ability. A new hyperspectral imager with 166 spectral channels, the ZY1-02D, makes possible the detection of subtle but important spectral features of soil. This study aimed at exploring the capability of the ZY1-02D to estimate and map the topsoil N content of the black soil-covered farmlands in northeast China. To this aim, 646 soil samples from study sites were collected, processed, spectrally and geochemically measured for the soil N sensitive bands detection and partial least squares regression (PLSR) calibration and validation. The sensitive bands detection results showed an appealing regularity of the variability and stable tendency of the soil N sensitive spectral bands with the change of the sample size. Based on this, we compared the estimation capacity of the models developed with the full wavelength spectra and the models developed with the sensitive bands. The estimation based on ZY1-02D full wavelength spectral reflectance were robust, with R² of 0.64 in validation. Further, the results of model developed with the sensitive bands showed better validation accuracy with R² of 0.66 and were applied to create a map of topsoil N content of farmlands in the northeast China black soil area. The results demonstrated that sensitive bands modelling could enhance the accuracy of the estimation and simplify model, and what is more, showed the ideal capability of ZY1-02D for soil N content estimation at the regional scale. Full article

(This article belongs to the Special Issue Application of Hyperspectral Data in Ecological Environment)

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22 pages, 7944 KiB

Open AccessArticle

Estimation of Soil Salinization by Machine Learning Algorithms in Different Arid Regions of Northwest China

by Xiaofang Jiang, Hanchen Duan, Jie Liao, Pinglin Guo, Cuihua Huang and Xian Xue

Remote Sens. 2022, 14(2), 347; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14020347 - 12 Jan 2022

Cited by 11 | Viewed by 2443

Abstract

Hyperspectral data has attracted considerable attention in recent years due to its high accuracy in monitoring soil salinization. At present, most existing research focuses on the saline soil in a single area without comparative analysis between regions. The regional differences in the hyperspectral characteristics of saline soil are still unclear. Thus, we chose Golmud in the cold–dry Qaidam Basin (QB–G) and Gaotai–Minghua in the relatively warm–dry Hexi Corridor (HC–GM) as the study areas, and used the deep extreme learning machine (DELM) and sine cosine algorithm–Elman (SCA–Elman) to predict soil salinity, and then selected the most suitable algorithm in these two regions. A total of 79 (QB–G) and 86 (HC–GM) soil samples were collected and tested to obtain their electrical conductivity (EC) and corresponding hyperspectral reflectance (R). We utilized the land surface parameters that affect the soil based on Landsat 8 and digital elevation model (DEM) data, selected the variables using the light gradient boosting machine (LightGBM), and built SCA–Elman and DELM from the hyperspectral reflectance data combined with land surface parameters. The results revealed the following: (1) The soil hyperspectral reflectance in QB–G was higher than that in HC–GM. The soils of QB–G are mainly the chloride type and those of HC–GM mainly belong to the sulfate type, having lower reflectance. (2) The accuracies of some of the SCA–Elman and DELM models in QB–G (the highest MAEv, RMSEv, and

R_{v}^{2}

were 0.09, 0.12 and 0.75, respectively) were higher than those in HC–GM (the highest MAEv, RMSEv, and

R_{v}^{2}

were 0.10, 0.14 and 0.73, respectively), which has flatter terrain and less obvious surface changes. The surface parameters in QB–G had higher correlation coefficients with EC due to the regular altitude change and cold–dry climate. (3) Most of the SCA–Elman results (the mean

R_{v}^{2}

in HC-GM and QB-G were 0.62 and 0.60, respectively) in all areas performed better than the DELM results (the mean

R_{v}^{2}

in HC–GM and QB–G were 0.51 and 0.49, respectively). Therefore, SCA–Elman was more suitable for the soil salinity prediction in HC–GM and QB–G. This can provide a reference for soil salinization monitoring and model selection in the future. Full article

(This article belongs to the Special Issue Application of Hyperspectral Data in Ecological Environment)

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13 pages, 5431 KiB

Open AccessArticle

Inversion Evaluation of Rare Earth Elements in Soil by Visible-Shortwave Infrared Spectroscopy

by Zhaoqiang Huang, Wenxuan Huang, Sheng Li, Bin Ni, Yalong Zhang, Mingwei Wang, Maolin Chen and Fuxiao Zhu

Remote Sens. 2021, 13(23), 4886; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13234886 - 01 Dec 2021

Cited by 7 | Viewed by 1879

Abstract

According to historical information, more than 300 metal smelting enterprises have been in the southwest of Xiongan for 300 years; however, these polluting enterprises have been gradually closed with the increased intensity of environmental protection. In the paper, 264 soil samples were collected and analyzed in the range of 400 nm–2500 nm by the spectra vista corporation (SVC), and the spectral noise was smoothed by the Savitzky–Golay filter. In order to enhance the spectral differences and curve shapes, mathematical transformations, such as the standard normal variate (SNV), first-order differential (FD), second-order differential (SD), multiple scattering correction (MSC), and continuum removal (CR), were performed on the data, and the correlation between spectral transformation and contents of REEs was analyzed. Moreover, three machine learning models—partial least-squares (PLS), random forest (RF), back propagation neural network (BPNN)—were used to predict the contents of REEs. Experimental results prove that REEs are combined with spectral active substances, such as organic compounds, clay minerals, and iron oxide, and it is possible to determine the contents of REEs using the reflection spectrum. The R² between the predicted values and measured contents reached 0.986 by using BPNN after FD transformation. More importantly, the predicted values basically agree with the actual situation for CASI/SASI airborne hyperspectral images, and this is an effective technique to obtain the contents of REEs in soil at the study area. Full article

(This article belongs to the Special Issue Application of Hyperspectral Data in Ecological Environment)

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19 pages, 4426 KiB

Open AccessArticle

Assessing Stream Thermal Heterogeneity and Cold-Water Patches from UAV-Based Imagery: A Matter of Classification Methods and Metrics

by Johannes Kuhn, Roser Casas-Mulet, Joachim Pander and Juergen Geist

Remote Sens. 2021, 13(7), 1379; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13071379 - 03 Apr 2021

Cited by 21 | Viewed by 3524

Abstract

Understanding stream thermal heterogeneity patterns is crucial to assess and manage river resilience in light of climate change. The dual acquisition of high-resolution thermal infrared (TIR) and red–green–blue-band (RGB) imagery from unmanned aerial vehicles (UAVs) allows for the identification and characterization of thermally differentiated patches (e.g., cold-water patches—CWPs). However, a lack of harmonized CWP classification metrics (patch size and temperature thresholds) makes comparisons across studies almost impossible. Based on an existing dual UAV imagery dataset (River Ovens, Australia), we present a semi-automatic supervised approach to classify key riverscape habitats and associated thermal properties at a pixel-scale accuracy, based on spectral properties. We selected five morphologically representative reaches to (i) illustrate and test our combined classification and thermal heterogeneity assessment method, (ii) assess the changes in CWP numbers and distribution with different metric definitions, and (iii) model how climatic predictions will affect thermal habitat suitability and connectivity of a cold-adapted fish species. Our method was successfully tested, showing mean thermal differences between shaded and sun-exposed fluvial mesohabitats of up to 0.62 °C. CWP metric definitions substantially changed the number and distance between identified CWPs, and they were strongly dependent on reach morphology. Warmer scenarios illustrated a decrease in suitable fish habitats, but reach-scale morphological complexity helped sustain such habitats. Overall, this study demonstrates the importance of method and metric definitions to enable spatio-temporal comparisons between stream thermal heterogeneity studies. Full article

(This article belongs to the Special Issue Application of Hyperspectral Data in Ecological Environment)

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20 pages, 5290 KiB

Open AccessArticle

Remote Soil Moisture Measurement from Drone-Borne Reflectance Spectroscopy: Applications to Hydroperiod Measurement in Desert Playas

by Joseph S. Levy and Jessica T. E. Johnson

Remote Sens. 2021, 13(5), 1035; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13051035 - 09 Mar 2021

Cited by 7 | Viewed by 3600

Abstract

The extent, timing, and magnitude of soil moisture in wetlands (the hydropattern) is a primary physical control on biogeochemical processes in desert environments. However, determining playa hydropatterns is challenged by the remoteness of desert basin sites and by the difficulty in determining soil moisture from remotely sensed data at fine spatial and temporal scales (hundreds of meters to kilometers, and hours to days). Therefore, we developed a new, reflectance-based soil moisture index (continuum-removed water index, or CRWI) that can be determined via hyperspectral imaging from drone-borne platforms. We compared its efficacy at remotely determining soil moisture content to existing hyperspectral and multispectral soil moisture indices. CRWI varies linearly with in situ soil moisture content (R² = 0.89, p < 0.001) and is comparatively insensitive to soil clay content (R² = 0.4, p = 0.01), soil salinity (R² = 0.82, p < 0.001), and soil grain size distribution (R² = 0.67, p < 0.001). CRWI is negatively correlated with clay content, indicating it is not sensitive to hydrated mineral absorption features. CRWI has stronger correlation with surface soil moisture than other hyperspectral and multispectral indices (R² = 0.69, p < 0.001 for WISOIL at this site). Drone-borne reflectance measurements allow monitoring of soil moisture conditions at the Alvord Desert playa test site over hectare-scale soil plots at measurement cadences of minutes to hours. CRWI measurements can be used to determine surface soil moisture at a range of desert sites to inform management decisions and to better reveal ecosystem processes in water-limited environments. Full article

(This article belongs to the Special Issue Application of Hyperspectral Data in Ecological Environment)

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