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Land
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Advances in Remote Sensing for Crop Monitoring and Yield Estimation
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Advances in Remote Sensing for Crop Monitoring and Yield Estimation

A special issue of Land (ISSN 2073-445X).

Deadline for manuscript submissions: closed (15 June 2022) | Viewed by 15594

Special Issue Editors

Dr. Bin Chen

E-Mail Website
Guest Editor
Department of Land, Air and Water Resources, University of California, Davis, 133 Veihmeyer Hall, One Shields Ave, CA 95616-8627, USA
Interests: remote sensing; data fusion and applications; agricultural monitoring; urban studies; environmental heath
Special Issues, Collections and Topics in MDPI journals
Dr. Yufang Jin

E-Mail Website
Guest Editor
Department of Land, Air and Water Resources, University of California Davis, 133 Veihmeyer Hall, One Shields Ave., Davis, CA 95616-8627, USA
Interests: remote sensing; drivers and consequences of wildland fires; crop monitoring and precision agriculture; eco-hydrology; vegetation-climate-fire-human interaction; machine learning; UAV applications; geospatial technology
Special Issues, Collections and Topics in MDPI journals
Dr. Le Yu

E-Mail Website
Guest Editor
Department for Earth System Science, Tsinghua University, Beijing 100084, China
Interests: land use change mapping; climate modelling; cropland mapping; uncertainty analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Global food security will remain a worldwide concern, especially in the face of challenges from climate change, population growth, water scarcity, environmental degradation, and biodiversity loss. Improving yields and maintaining agricultural sustainability through argoecological approaches and scientific farming management are of the utmost importance. The use of remote sensing in monitoring crop conditions and production estimates has proven to be very useful and supportive for agricultural management from local to regional, continental, and global scales. Many previous efforts have been made to advance the monitoring of crop conditions including the blooming and phenology cycle, health and productivity, drought and heat stress, and other processes. These remote sensing based indicators of critical crop conditions are always integrated with crop characteristics, climatic and soil variables, and auxiliary variables to build yield prediction models for cost-effective estimates of crop productions at different spatial-temporal scales.

Nowadays, the emerging satellite missions, remote sensing sensors, geospatial big data, and the development of artificial intelligence and machine learning have provided further new opportunities for a better understanding of the crop’s physical and biophysical process. This Special Issue calls for innovative data, methods, and analysis techniques for remote sensing-based crop monitoring and yield estimations. Acceptable topics include, but are not limited to, crop condition monitoring, crop phenology, crop stress detection, remote sensing indicators of crops, crop yield prediction, controls on yield potentials, drivers of yield variability, and multi-source data integration for sustainable agriculture.

Being at the boundary between remote sensing science and land science, the “Advances in Remote Sensing for Crop Monitoring and Yield Estimation” Special Issue is jointly organized between “Remote Sensing” and “Land” journals.

You may choose our Joint Special Issue in Remote Sensing.

Dr. Bin Chen
Dr. Yufang Jin
Prof. Dr. Le 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. Land is an international peer-reviewed open access monthly 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 2600 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 agriculture
  • Crop yield estimation and prediction
  • Crop types and cropping intensity
  • Crop phenology, stress, and health status
  • Controls and drivers of yield variability
  • Remote sensing spectral indices for crops
  • Multi-scale monitoring and mapping of crop yields
  • Multi-source data fusion for sustainable agriculture

Published Papers (4 papers)

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Research

27 pages, 4988 KiB  
Article
Prediction of Crop Yield for New Mexico Based on Climate and Remote Sensing Data for the 1920–2019 Period
by Kamini Yadav and Hatim M. E. Geli
Land 2021, 10(12), 1389; https://doi.org/10.3390/land10121389 - 15 Dec 2021
Cited by 8 | Viewed by 2975
Abstract
Agricultural production systems in New Mexico (NM) are under increased pressure due to climate change, drought, increased temperature, and variable precipitation, which can affect crop yields, feeds, and livestock grazing. Developing more sustainable production systems requires long-term measurements and assessment of climate change [...] Read more.
Agricultural production systems in New Mexico (NM) are under increased pressure due to climate change, drought, increased temperature, and variable precipitation, which can affect crop yields, feeds, and livestock grazing. Developing more sustainable production systems requires long-term measurements and assessment of climate change impacts on yields, especially over such a vulnerable region. Providing accurate yield predictions plays a key role in addressing a critical sustainability gap. The goal of this study is the development of effective crop yield predictions to allow for a better-informed cropland management and future production potential, and to develop climate-smart adaptation strategies for increased food security. The objectives were to (1) identify the most important climate variables that significantly influence and can be used to effectively predict yield, (2) evaluate the advantage of using remotely sensed data alone and in combination with climate variables for yield prediction, and (3) determine the significance of using short compared to long historical data records for yield prediction. This study focused on yield prediction for corn, sorghum, alfalfa, and wheat using climate and remotely sensed data for the 1920–2019 period. The results indicated that the use of normalized difference vegetation index (NDVI) alone is less accurate in predicting crop yields. The combination of climate and NDVI variables provided better predictions compared to the use of NDVI only to predict wheat, sorghum, and corn yields. However, the use of a climate only model performed better in predicting alfalfa yield. Yield predictions can be more accurate with the use of shorter data periods that are based on region-specific trends. The identification of the most important climate variables and accurate yield prediction pertaining to New Mexico’s agricultural systems can aid the state in developing climate change mitigation and adaptation strategies to enhance the sustainability of these systems. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)
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18 pages, 14982 KiB  
Article
Crop Intensity Mapping Using Dynamic Time Warping and Machine Learning from Multi-Temporal PlanetScope Data
by Raihan Rafif, Sandiaga Swahyu Kusuma, Siti Saringatin, Giara Iman Nanda, Pramaditya Wicaksono and Sanjiwana Arjasakusuma
Land 2021, 10(12), 1384; https://doi.org/10.3390/land10121384 - 14 Dec 2021
Cited by 9 | Viewed by 2691
Abstract
Crop intensity information describes the productivity and the sustainability of agricultural land. This information can be used to determine which agricultural lands should be prioritized for intensification or protection. Time-series data from remote sensing can be used to derive the crop intensity information; [...] Read more.
Crop intensity information describes the productivity and the sustainability of agricultural land. This information can be used to determine which agricultural lands should be prioritized for intensification or protection. Time-series data from remote sensing can be used to derive the crop intensity information; however, this application is limited when using medium to coarse resolution data. This study aims to use 3.7 m-PlanetScope™ Dove constellation data, which provides daily observations, to map crop intensity information for agricultural land in Magelang District, Indonesia. Two-stage histogram matching, before and after the monthly median composites, is used to normalize the PlanetScope data and to generate monthly data to map crop intensity information. Several methods including Time-Weighted Dynamic Time Warping (TWDTW) and the machine-learning algorithms: Random Forest (RF), Extremely Randomized Trees (ET), and Extreme Gradient Boosting (XGB) are employed in this study, and the results are validated using field survey data. Our results show that XGB generated the highest overall accuracy (OA) (95 ± 4%), followed by RF (92 ± 5%), ET (87 ± 6%), and TWDTW (81 ± 8%), for mapping four-classes of cropping intensity, with the near-infrared (NIR) band being the most important variable for identifying cropping intensity. This study demonstrates the potential of PlanetScope data for the production of cropping intensity maps at detailed resolutions. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)
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15 pages, 4128 KiB  
Article
Mapping Highland Barley on the Qinghai–Tibet Combing Landsat OLI Data and Object-Oriented Classification Method
by Weidong Ma, Wei Jia, Peng Su, Xingyun Feng, Fenggui Liu and Jing’ai Wang
Land 2021, 10(10), 1022; https://doi.org/10.3390/land10101022 - 29 Sep 2021
Cited by 6 | Viewed by 2084
Abstract
In this paper, we use the extraction method of multi-factors fusion to extract the Highland barley cultivation area on Qinghai–Tibet Plateau. The study results indicate that: (1) the method (extracting through multi-factors fusion) is efficient during the extracting process and is highly accurate [...] Read more.
In this paper, we use the extraction method of multi-factors fusion to extract the Highland barley cultivation area on Qinghai–Tibet Plateau. The study results indicate that: (1) the method (extracting through multi-factors fusion) is efficient during the extracting process and is highly accurate in extraction results. This extraction scheme allows for not only the spatial heterogeneity of different physical geographic units, but also the impact of multi-factors on crop cultivation; (2) according to our research, the total Highland barley cultivation area on Qinghai–Tibet Plateau is about 2.74 × 105 ha. Based on the statistics, we draw the first distribution map of the Highland barley cultivation area on Qinghai–Tibet Plateau, which upgrades its spatial distribution pattern from administrative unit to patch unit; (3) Highland barley in various divisions has a distinct spatial heterogeneity in elevation. On the whole, the Highland barley on the plateau is planted at an elevation of 2500–4500 m, up to 5200 m. Due to the impact of topography diversity, temperature, moisture, light, arable land and irrigation conditions, its cultivation area at the same elevation varies in different divisions. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)
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21 pages, 4053 KiB  
Article
Selection of Independent Variables for Crop Yield Prediction Using Artificial Neural Network Models with Remote Sensing Data
by Patryk Hara, Magdalena Piekutowska and Gniewko Niedbała
Land 2021, 10(6), 609; https://doi.org/10.3390/land10060609 - 07 Jun 2021
Cited by 53 | Viewed by 6601
Abstract
Knowing the expected crop yield in the current growing season provides valuable information for farmers, policy makers, and food processing plants. One of the main benefits of using reliable forecasting tools is generating more income from grown crops. Information on the amount of [...] Read more.
Knowing the expected crop yield in the current growing season provides valuable information for farmers, policy makers, and food processing plants. One of the main benefits of using reliable forecasting tools is generating more income from grown crops. Information on the amount of crop yielding before harvesting helps to guide the adoption of an appropriate strategy for managing agricultural products. The difficulty in creating forecasting models is related to the appropriate selection of independent variables. Their proper selection requires a perfect knowledge of the research object. The following article presents and discusses the most commonly used independent variables in agricultural crop yield prediction modeling based on artificial neural networks (ANNs). Particular attention is paid to environmental variables, such as climatic data, air temperature, total precipitation, insolation, and soil parameters. The possibility of using plant productivity indices and vegetation indices, which are valuable predictors obtained due to the application of remote sensing techniques, are analyzed in detail. The paper emphasizes that the increasingly common use of remote sensing and photogrammetric tools enables the development of precision agriculture. In addition, some limitations in the application of certain input variables are specified, as well as further possibilities for the development of non-linear modeling, using artificial neural networks as a tool supporting the practical use of and improvement in precision farming techniques. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)
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