Remote Sensing

Journal Browser

► Journal Browser

Improve Irrigation Management with Remote Sensing Technology

Share This Special Issue

Special Issue Editors

Dr. Kyle R. Douglas-Mankin

E-Mail Website
Guest Editor

Water Management & Systems Research, USDA–Agricultural Research Service, 2150 Centre Ave, Building D, Fort Collins, CO 80526, USA
Interests: agroecosystem modeling; evapotranspiration; hydrology

Dr. Huihui Zhang

E-Mail Website
Guest Editor

Water Management & Systems Research, USDA–Agricultural Research Service, 2150 Centre Ave, Building D, Fort Collins, CO 80526, USA
Interests: remote sensing; irrigation scheduling; precision agriculture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Our world is rapidly changing. Among the most crucial challenges to society is feeding a growing global population in the face of continuously declining water (and irrigation) supplies, changing climate (and increasing irrigation demand), and shifting land resource priorities (pushing agriculture toward more marginal lands).

Advances in remote-sensing data collection provide a great opportunity to monitor crop, soil, water, and ecosystem resource properties and processes and, ultimately, to increase crop production and agroecosystem sustainability. Researchers are challenged to harness these advances to demonstrate utility of these data to inform limited-water agroecosystem monitoring and water management.

This Special Issue includes original and innovative developments demonstrating the use of remote-sensing data to assess crop water status, development, and yield; quantify edaphic and anthropogenic effects on irrigated agroecosystems; and inform irrigation management. Specific topics include but are not limited to:

UAS-based spectral, hyper-spectral, thermal, SAR and LiDAR imaging and image-processing in irrigated agricultural systems;
Variable rate, deficit, and precision irrigation management using remote-sensing data;
Crop water stress indicators;
Crop- and site-specific ET, phenology, soil moisture, and yield data collection;
Soil moisture spatiotemporal distributions and trends in water-limited agriculture;
Integration of remote-sensing data and agricultural system models;
Case studies of real-world adoption of remote sensing for irrigation scheduling;
Quantifying inter-sector competition for irrigation water supply;
Artificial intelligence and machine learning applications;
Novel sensing technologies and multi-platform data fusion.

Dr. Kyle R. Douglas-Mankin
Dr. Huihui Zhang
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

crop water stress
deficit irrigation
evapotranspiration
image processing
precision irrigation
satellite remote sensing
soil water
unmanned aerial systems
variable rate irrigation

Published Papers (1 paper)

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

19 pages, 4817 KiB

Open AccessArticle

A Fixed-Threshold Method for Estimating Fractional Vegetation Cover of Maize under Different Levels of Water Stress

by Yaxiao Niu, Huihui Zhang, Wenting Han, Liyuan Zhang and Haipeng Chen

Remote Sens. 2021, 13(5), 1009; https://doi.org/10.3390/rs13051009 - 7 Mar 2021

Cited by 11 | Viewed by 2606

Abstract

Accurate estimation of fractional vegetation cover (FVC) from digital images taken by commercially available cameras is of great significance in order to monitor the vegetation growth status, especially when plants are under water stress. Two classic threshold-based methods, namely, the intersection method (T₁ method) and the equal misclassification probability method (T₂ method), have been widely applied to Red-Green-Blue (RGB) images. However, the high coverage and severe water stress of crops in the field make it difficult to extract FVC stably and accurately. To solve this problem, this paper proposes a fixed-threshold method based on the statistical analysis of thresholds obtained from the two classic threshold approaches. Firstly, a Gaussian mixture model (GMM), including the distributions of green vegetation and backgrounds, was fitted on four color features: excessive green index, H channel of the Hue-Saturation-Value (HSV) color space, a* channel of the CIE L*a*b* color space, and the brightness-enhanced a* channel (denoted as a*_I). Secondly, thresholds were calculated by applying the T₁ and T₂ methods to the GMM of each color feature. Thirdly, based on the statistical analysis of the thresholds with better performance between T₁ and T₂, the fixed-threshold method was proposed. Finally, the fixed-threshold method was applied to the optimal color feature a*_I to estimate FVC, and was compared with the two classic approaches. Results showed that, for some images with high reference FVC, FVC was seriously underestimated by 0.128 and 0.141 when using the T₁ and T₂ methods, respectively, but this problem was eliminated by the proposed fixed-threshold method. Compared with the T₁ and T₂ methods, for images taken in plots under severe water stress, the mean absolute error of FVC obtained by the fixed-threshold method was decreased by 0.043 and 0.193, respectively. Overall, the FVC estimation using the proposed fixed-threshold method has the advantages of robustness, accuracy, and high efficiency, with a coefficient of determination (R²) of 0.99 and root mean squared error (RMSE) of 0.02. Full article

(This article belongs to the Special Issue Improve Irrigation Management with Remote Sensing Technology)

► Show Figures

Journal Menu

Journal Browser

Improve Irrigation Management with Remote Sensing Technology

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (1 paper)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI