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Remote Sensing in Agricultural and Environmental Water Monitoring and Impact Assessment (Second Edition)

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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 November 2024) | Viewed by 10431

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Special Issue Editors

Prof. Dr. Shibo Fang

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State Key Laboratory of Sever Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Interests: remote sensing; meteorology; agriculture; environment; climatology
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Prof. Dr. Simon Pearson

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Lincoln Institute for Agri-Food Technology, University of Lincoln, Lincoln LN6 7TS, UK
Interests: agricultural robotics and automation; environmental physiology of fresh produce and ornamental crops; modified atmosphere packaging; farm decision support systems
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Prof. Dr. Edward Hanna

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Lincoln Climate Research Group, School of Geography, University of Lincoln, Lincoln LN6 7FL, UK
Interests: meteorology; climatology; UK extreme weather

Prof. Dr. Lixin Dong

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Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration, Beijing 100081, China
Interests: agricultural meteorology; remote sensing
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Dr. Yanru Yu

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State Key Laboratory of Sever Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Interests: atmospheric temperature; land cover; land surface temperature; climatology; mean square error methods; remote sensing; vegetation; vegetation mapping

Special Issue Information

Dear Colleagues,

We are launching the second Special Issue of Remote Sensing to be released under the title “Remote Sensing in Agricultural and Environmental Water Monitoring and Impact Assessment”.

Due to global climate change and human activities, the frequency and intensity of extreme events, such as droughts and floods, have been increasing significantly in all world regions, with widespread consequences. Monitoring the distribution and variation patterns of drought and floods accurately and in a timely manner will help to address the grand challenges present, thereby enhancing food and water security, ecosystem services, and human living environments. Along with the rapid development of remote sensing technology, a number of satellite-based methods have demonstrated their potential to reflect water information and related disasters over large scales and across different spatial resolution. This Special Issue aims to present original and innovative research in applications of remote sensing in agricultural and ecological drought monitoring, soil moisture detection, flood and water resource extraction, and the impact assessment of water-related disasters. These papers will provide the readers of Remote Sensing with a wide range of examples of satellite data analysis, big data processing, information management and visualization, earth science, computer science, and new principles, methods, and models regarding water sensing and mapping.

Contributions may be related—but not limited— to the following topics:

Agricultural drought monitoring;
Drought impacts on crops;
Ecological drought monitoring;
Soil moisture detection;
Flood monitoring and its impacts;
Patterns of water resources;
The relationship between water-related disasters and climate change.

Prof. Dr. Shibo Fang
Prof. Dr. Simon Pearson
Prof. Dr. Edward Hanna
Prof. Dr. Lixin Dong
Dr. Yanru Yu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

remote sensing
agricultural drought
ecological drought
flood
soil moisture
water resource
impact assessment

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Related Special Issue

Remote Sensing in Agricultural and Environmental Water Monitoring and Impact Assessment in Remote Sensing (7 articles)

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Research

22 pages, 13335 KiB

Open AccessArticle

An Integrated Drought Index (Vapor Pressure Deficit–Soil Moisture–Sun-Induced Chlorophyll Fluorescence Dryness Index, VMFDI) Based on Multisource Data and Its Applications in Agricultural Drought Management

by Caiyun Deng, Li Zhang, Tianhe Xu, Siqi Yang, Jian Guo, Lulu Si, Ran Kang and Hermann Josef Kaufmann

Remote Sens. 2024, 16(24), 4666; https://doi.org/10.3390/rs16244666 - 13 Dec 2024

Viewed by 1160

Abstract

To more precisely monitor drought, a new remote sensing-based drought index, the Vapor Pressure Deficit–Soil Moisture–Sun-Induced Chlorophyll fluorescence Dryness Index (VMFDI), with a spatial resolution of 1 km based on vapor pressure deficit (VPD), soil moisture (SM), and sun-induced chlorophyll fluorescence (SIF) data was constructed via a three-dimensional spatial distance model, and it was used to monitor dryness in the Yellow River Basin during 2003–2020. The spatiotemporal variations in and main factors of the VMFDI and agroecosystem responses were analyzed via the Theil–Sen median and Mann–Kendall tests and Liang–Kleeman information flow. The results revealed the following: (1) The VMFDI effectively monitors regional drought and is more sensitive than other indices like the standardized precipitation evapotranspiration index (SPEI) and GRACE drought severity index and single variables. (2) VMFDI values fluctuated seasonally in the Yellow River Basin, peaking in August and reaching their lowest in March. The basin becomes drier in winter but wetter in spring, summer, and autumn, with the middle and lower reaches, particularly Shaanxi and Gansu, being drought-prone. The VMFDI values in the agroecosystem were lower. (3) SM and VPD dominated drought at the watershed and agroecosystem scales, respectively. Key agroecosystem indicators, including greenness (NDVI), gross primary productivity (GPP), water use efficiency (WUE), and leaf area index (LAI), were negatively correlated with drought (p < 0.05). When VPD exceeded a threshold range of 7.11–7.17 ha, the relationships between these indicators and VPD shifted from positive to negative. The specific VPD thresholds in maize and wheat systems were 8.03–8.57 ha and 7.15 ha, respectively. Suggestions for drought risk management were also provided. This study provides a new method and high-resolution data for accurately monitoring drought, which can aid in mitigating agricultural drought risks and promoting high-quality agricultural development. Full article

(This article belongs to the Special Issue Remote Sensing in Agricultural and Environmental Water Monitoring and Impact Assessment (Second Edition))

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22 pages, 5568 KiB

Open AccessArticle

Unlocking the Secrets of Corn: Physiological Responses and Rapid Forecasting in Varied Drought Stress Environments

by Wenlong Song, Kaizheng Xiang, Yizhu Lu, Mengyi Li, Hongjie Liu, Long Chen, Xiuhua Chen and Haider Abbas

Remote Sens. 2024, 16(22), 4302; https://doi.org/10.3390/rs16224302 - 18 Nov 2024

Viewed by 1095

Abstract

Understanding the intricate relationship between drought stress and corn yield is crucial for ensuring food security and sustainable agriculture in the face of climate change. This study investigates the subtle effects of drought stress on corn physiological, morphological, and spectral characteristics at different growth stages, in order to construct a new drought index to characterize drought characteristics, so as to provide valuable insights for maize recovery mechanism and yield prediction. Specific conclusions are as follows. Firstly, the impact of drought stress on corn growth and development shows a gradient effect, with the most significant effects observed during the elongation stage and tasseling stage. Notably, Soil and Plant Analyzer Development (SPAD) and Leaf Area Index (LAI) are significantly affected during the silking stage, while plant height and stem width remain relatively unaffected. Secondly, spectral feature analysis reveals that, from the elongation stage to the silking stage, canopy reflectance exhibits peak–valley variations. Drought severity correlates positively with reflectance in the visible and shortwave infrared bands and negatively with reflectance in the near-infrared band. Canopy spectra during the silking stage are more affected by moderate and severe drought stress. Thirdly, LAI shows a significant positive correlation with yield, indicating its reliability in explaining yield variations. Finally, the yield-related drought index (YI) constructed based on Convolutional Neural Network (CNN), Random Forest (RF) and Multiple Linear Regression (MLR) methods has a good effect on revealing drought characteristics (R = 0.9332, p < 0.001). This study underscores the importance of understanding corn responses to drought stress at various growth stages for effective yield prediction and agricultural management strategies. Full article

(This article belongs to the Special Issue Remote Sensing in Agricultural and Environmental Water Monitoring and Impact Assessment (Second Edition))

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29 pages, 5844 KiB

Open AccessArticle

Early Modeling of the Upcoming Landsat Next Constellation for Soybean Yield Prediction Under Varying Levels of Water Availability

by Luís Guilherme Teixeira Crusiol, Marcos Rafael Nanni, Rubson Natal Ribeiro Sibaldelli, Liang Sun, Renato Herrig Furlanetto, Sergio Luiz Gonçalves, Norman Neumaier and José Renato Bouças Farias

Remote Sens. 2024, 16(22), 4184; https://doi.org/10.3390/rs16224184 - 9 Nov 2024

Viewed by 1567

Abstract

The upcoming Landsat Next will provide more frequent land surface observations at higher spatial and spectral resolutions that will greatly benefit the agricultural sector. Early modeling of the upcoming Landsat Next products for soybean yield prediction is essential for long-term satellite monitoring strategies. In this context, this article evaluates the contribution of Landsat Next’s improved spectral resolution for soybean yield prediction under varying levels of water availability. Ground-based hyperspectral data collected over five cropping seasons at the Brazilian Agricultural Research Corporation were resampled to Landsat Next spectral resolution. The spectral dataset (n = 384) was divided into calibration and external validation datasets and investigated using three strategies for soybean yield prediction: (1) using the reflectance from each spectral band; (2) using existing and new vegetation indices developed based on three general equations: Normalized Difference Vegetation Index (NDVI-like), Band Ratio Vegetation Index (RVI-like), and Band Difference Vegetation Index (DVI-like), replacing the traditional spectral bands by all possible combinations between two bands for index calculation; and (3) using a partial least squares regression (PLSR) model composed of all Landsat Next spectral bands, in comparison to PLSR models using Landsat OLI and Sentienel-2 MSI bands. The results show the distribution of the new spectral bands over the most prominent changes in leaf reflectance due to water deficit, particularly in the visible and shortwave infrared spectrum. (1) Band 18 (centered at 1610 nm) had the highest correlation with yield (R² = 0.34). (2) A new vegetation index, called Normalized Difference Shortwave Vegetation Index (NDSWVI), is proposed and calculated from bands 19 and 20 (centered at 2028 and 2108 nm). NDSWVI showed the best performance (R² = 0.37) compared to traditional existing and new vegetation indices. (3) The PLSR model gave the best results (R² = 0.65), outperforming the Landsat OLI and Sentinel-2 MSI sensors. The improved spectral resolution of Landsat Next is expected to contribute to improved crop monitoring, especially for soybean crops in Brazil, increasing the sustainability of the production systems and strengthening food security in Brazil and globally. Full article

(This article belongs to the Special Issue Remote Sensing in Agricultural and Environmental Water Monitoring and Impact Assessment (Second Edition))

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27 pages, 5800 KiB

Open AccessArticle

Multimodal Deep Learning Integration of Image, Weather, and Phenotypic Data Under Temporal Effects for Early Prediction of Maize Yield

by Danial Shamsuddin, Monica F. Danilevicz, Hawlader A. Al-Mamun, Mohammed Bennamoun and David Edwards

Remote Sens. 2024, 16(21), 4043; https://doi.org/10.3390/rs16214043 - 30 Oct 2024

Cited by 2 | Viewed by 2156

Abstract

Maize (Zea mays L.) has been shown to be sensitive to temperature deviations, influencing its yield potential. The development of new maize hybrids resilient to unfavourable weather is a desirable aim for crop breeders. In this paper, we showcase the development of a multimodal deep learning model using RGB images, phenotypic, and weather data under temporal effects to predict the yield potential of maize before or during anthesis and silking stages. The main objective of this study was to assess if the inclusion of historical weather data, maize growth captured through imagery, and important phenotypic traits would improve the predictive power of an established multimodal deep learning model. Evaluation of the model performance when training from scratch showed its ability to accurately predict ~89% of hybrids with high-yield potential and demonstrated enhanced explanatory power compared with previously published models. Shapley Additive explanations (SHAP) analysis indicated the top influential features include plant density, hybrid placement in the field, date to anthesis, parental line, temperature, humidity, and solar radiation. Including weather historical data was important for model performance, significantly enhancing the predictive and explanatory power of the model. For future research, the use of the model can move beyond maize yield prediction by fine-tuning the model on other crop data, serving as a potential decision-making tool for crop breeders to determine high-performing individuals from diverse crop types. Full article

(This article belongs to the Special Issue Remote Sensing in Agricultural and Environmental Water Monitoring and Impact Assessment (Second Edition))

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22 pages, 5975 KiB

Open AccessArticle

Evaluating Daily Water Stress Index (DWSI) Using Thermal Imaging of Neem Tree Canopies under Bare Soil and Mulching Conditions

by Thayná A. B. Almeida, Abelardo A. A. Montenegro, Rodes A. B. da Silva, João L. M. P. de Lima, Ailton A. de Carvalho and José R. L. da Silva

Remote Sens. 2024, 16(15), 2782; https://doi.org/10.3390/rs16152782 - 30 Jul 2024

Cited by 2 | Viewed by 1960

Abstract

Water stress on crops can severely disrupt crop growth and reduce yields, requiring the accurate and prompt diagnosis of crop water stress, especially in semiarid regions. Infrared thermal imaging cameras are effective tools to monitor the spatial distribution of canopy temperature (Tc), which is the basis of the daily water stress index (DWSI) calculation. This research aimed to evaluate the variability of plant water stress under different soil cover conditions through geostatistical techniques, using detailed thermographic images of Neem canopies in the Brazilian northeastern semiarid region. Two experimental plots were established with Neem cropped under mulch and bare soil conditions. Thermal images of the leaves were taken with a portable thermographic camera and processed using Python language and the OpenCV database. The application of the geostatistical technique enabled stress indicator mapping at the leaf scale, with the spherical and exponential models providing the best fit for both soil cover conditions. The results showed that the highest levels of water stress were observed during the months with the highest air temperatures and no rainfall, especially at the apex of the leaf and close to the central veins, due to a negative water balance. Even under extreme drought conditions, mulching reduced Neem physiological water stress, leading to lower plant water stress, associated with a higher soil moisture content and a negative skewness of temperature distribution. Regarding the mapping of the stress index, the sequential Gaussian simulation method reduced the temperature uncertainty and the variation on the leaf surface. Our findings highlight that mapping the Water Stress Index offers a robust framework to precisely detect stress for agricultural management, as well as soil cover management in semiarid regions. These findings underscore the impact of meteorological and planting conditions on leaf temperature and baseline water stress, which can be valuable for regional water resource managers in diagnosing crop water status more accurately. Full article

(This article belongs to the Special Issue Remote Sensing in Agricultural and Environmental Water Monitoring and Impact Assessment (Second Edition))

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16 pages, 6039 KiB

Open AccessArticle

Spatiotemporal Variation in Water Deficit- and Heatwave-Driven Flash Droughts in Songnen Plain and Its Ecological Impact

by Jiahao Sun, Yanfeng Wu, Qingsong Zhang, Lili Jiang, Qiusheng Ma, Mo Chen, Changlei Dai and Guangxin Zhang

Remote Sens. 2024, 16(8), 1408; https://doi.org/10.3390/rs16081408 - 16 Apr 2024

Cited by 1 | Viewed by 1611

Abstract

The phenomenon of flash droughts, marked by their fast onset, limited predictability, and formidable capacity for devastation, has elicited escalating concern. Despite this growing interest, a comprehensive investigation of the spatiotemporal dynamics of flash drought events within zones of ecological sensitivity, alongside their consequential ecological ramifications, remains elusive. The Songnen Plain, distinguished as both an important granary for commodity crops and an ecological keystone within China, emerges as an indispensable locus for the inquiry into the dynamics of flash droughts and their repercussions on terrestrial biomes. Through the application of daily soil moisture raster datasets encompassing the years 2002 to 2022, this investigation delves into the spatiotemporal progression of two distinct categories of flash droughts—those instigated by heatwaves and those precipitated by water deficits—within the Songnen Plain. Moreover, the ecosystem’s response, with a particular focus on gross primary productivity (GPP), to these climatic variables was investigated. Flash drought phenomena have been observed to manifest with a relative frequency of approximately one event every three years within the Songnen Plain, predominantly lasting for periods of 28–30 days. The incidence of both heatwave-induced and water deficit-induced flash droughts was found to be comparable, with a pronounced prevalence during the summer and autumn. Nevertheless, droughts caused by water scarcity demonstrated a more extensive distribution and a heightened frequency of occurrence, whereas those rooted in heatwaves were less frequent but exhibited a propensity for localization in specific sectors. The sensitivity of GPP to these meteorological anomalies was pronounced, with an average response rate surpassing 70%. This spatial distribution of the response rate revealed elevated values in the northwestern segment of the Songnen Plain and diminished values towards the southeastern sector. Intriguingly, GPP’s reaction pace to the onset of heatwave-driven flash droughts was observed to be more rapid in comparison to that during periods of water scarcity. Additionally, the spatial distribution of water use efficiency during both the development and recovery periods of flash droughts largely deviated from that of base water use efficiency. The insights from this study hold profound implications for the advancement of regional drought surveillance and adaptive management. Full article

(This article belongs to the Special Issue Remote Sensing in Agricultural and Environmental Water Monitoring and Impact Assessment (Second Edition))

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