Special Issue "Remote Sensing for Cropping Systems and Bare Soils Monitoring and Optimization"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: 28 February 2022.

Special Issue Editors

Dr. Ephrem Habyarimana
E-Mail Website
Guest Editor
CREA Research Center for Cereal and Industrial Crops, 40128 Bologna, Italy
Interests: cereals; molecular breeding; high-throughput phenotyping; GWAS; big data analysis and genomic selection
Special Issues, Collections and Topics in MDPI journals
Dr. Nicolas Greggio
E-Mail Website1 Website2
Guest Editor
Biological, Geological, and Environmental Sciences Department (BiGeA) and Interdepartmental Centre for Environmental Sciences Research, Alma Mater Studiorum – Bologna University, Operative Unit of Ravenna, Via S. Alberto, 163 - 48123 Ravenna, Italy
Interests: environmental geochemistry; potential harmful elements (PHEs) in sediment, soil and water; geoinformatics (GIS); water science; soil science; irrigation and water management; environmental monitoring and impact assessment; circular economy; agricultural residual biomasses (ARB)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

remote sensing (RS) and Earth Observation (EO) information is central for detecting crop type, monitoring crop growth and development, plant health, productivity and managing nutrient optimization programs in agricultural systems. The chlorophyll molecules are the main key enablers in this investigation in virtue of their intrinsic properties converting absorbed solar irradiance into stored chemical energy; chlorophyll is therefore the driver of the plant photosynthetic capacity and primary productivity.

Remote sensing information can also be used for gaining insights into mechanisms plants use to respond to climate change and other adversities across diverse ecosystems, and for optimizing the cropping systems in a more sustainable way. Cropping systems e.g., crop rotations, polyculture, and other agroecological techniques can result in different productivity and effects on soil properties, and can be implemented to sustainably mitigate and adapt to climate change. The challenge remains how remote sensing can detect and repeatably quantify indicators of such cropping systems’ benefits. On the other hand, annual cropping systems are characterized by frequent rotations and periods of bare soils between consecutive cropping seasons. A bare soil is exposed to soil and productivity degrading factors such as erosion, lixiviation, and accelerated soil organic carbon oxidation. The early identification of bare soils is therefore necessary for their optimized management e.g., second crops, cover crops, etc. Sustainable cropping systems and bare soils management are the obliged path to climate change resilient agroecosystems and our capability to feed World’s increasing populations.

This Special Issue is thus aiming at garnering state-of-the-art RS/EO-based research to retrieve and model crop types and yields, bare soils, and cropping systems and relative economic and environmental performances. Implementing AI/machine learning and deriving empirical scenarios on cropping systems and bare soils management optimization is encouraged.

Dr. Ephrem Habyarimana
Dr. Nicolas Greggio
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 papers will be 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 2500 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
  • Earth Observation
  • Environmental performance
  • Bare soil management
  • Artificial intelligence
  • Cropping system optimization

Published Papers (4 papers)

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Research

Article
Assimilation of Wheat and Soil States into the APSIM-Wheat Crop Model: A Case Study
Remote Sens. 2022, 14(1), 65; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14010065 - 24 Dec 2021
Viewed by 339
Abstract
Optimised farm crop productivity requires careful management in response to the spatial and temporal variability of yield. Accordingly, combination of crop simulation models and remote sensing data provides a pathway for providing the spatially variable information needed on current crop status and the [...] Read more.
Optimised farm crop productivity requires careful management in response to the spatial and temporal variability of yield. Accordingly, combination of crop simulation models and remote sensing data provides a pathway for providing the spatially variable information needed on current crop status and the expected yield. An ensemble Kalman filter (EnKF) data assimilation framework was developed to assimilate plant and soil observations into a prediction model to improve crop development and yield forecasting. Specifically, this study explored the performance of assimilating state observations into the APSIM-Wheat model using a dataset collected during the 2018/19 wheat season at a farm near Cora Lynn in Victoria, Australia. The assimilated state variables include (1) ground-based measurements of Leaf Area Index (LAI), soil moisture throughout the profile, biomass, and soil nitrate-nitrogen; and (2) remotely sensed observations of LAI and surface soil moisture. In a baseline scenario, an unconstrained (open-loop) simulation greatly underestimated the wheat grain with a relative difference (RD) of −38.3%, while the assimilation constrained simulations using ground-based LAI, ground-based biomass, and remotely sensed LAI were all found to improve the RD, reducing it to −32.7%, −9.4%, and −7.6%, respectively. Further improvements in yield estimation were found when: (1) wheat states were assimilated in phenological stages 4 and 5 (end of juvenile to flowering), (2) plot-specific remotely sensed LAI was used instead of the field average, and (3) wheat phenology was constrained by ground observations. Even when using parameters that were not accurately calibrated or measured, the assimilation of LAI and biomass still provided improved yield estimation over that from an open-loop simulation. Full article
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Article
Soil Salinity Inversion in Coastal Corn Planting Areas by the Satellite-UAV-Ground Integration Approach
Remote Sens. 2021, 13(16), 3100; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163100 - 05 Aug 2021
Cited by 3 | Viewed by 768
Abstract
Soil salinization is a significant factor affecting corn growth in coastal areas. How to use multi-source remote sensing data to achieve the target of rapid, efficient and accurate soil salinity monitoring in a large area is worth further study. In this research, using [...] Read more.
Soil salinization is a significant factor affecting corn growth in coastal areas. How to use multi-source remote sensing data to achieve the target of rapid, efficient and accurate soil salinity monitoring in a large area is worth further study. In this research, using Kenli District of the Yellow River Delta as study area, the inversion of soil salinity in a corn planting area was carried out based on the integration of ground imaging hyperspectral, unmanned aerial vehicles (UAV) multispectral and Sentinel-2A satellite multispectral images. The UAV and ground images were fused, and the partial least squares inversion model was constructed by the fused UAV image. Then, inversion model was scaled up to the satellite by the TsHARP method, and finally, the accuracy of the satellite-UAV-ground inversion model and results was verified. The results show that the band fusion of UAV and ground images effectively enrich the spectral information of the UAV image. The accuracy of the inversion model constructed based on the fused UAV images was improved. The inversion results of soil salinity based on the integration of satellite-UAV-ground were highly consistent with the measured soil salinity (R2 = 0.716 and RMSE = 0.727), and the inversion model had excellent universal applicability. This research integrated the advantages of multi-source data to establish a unified satellite-UAV-ground model, which improved the ability of large-scale remote sensing data to finely indicate soil salinity. Full article
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Article
Methodology for the Definition of Durum Wheat Yield Homogeneous Zones by Using Satellite Spectral Indices
Remote Sens. 2021, 13(11), 2036; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13112036 - 21 May 2021
Viewed by 629
Abstract
One of the main questions facing precision agriculture is the evaluation of different algorithms for the delineation of homogeneous management zones. In the present study, a new approach based on the use of time series of satellite imagery, collected during two consecutive growing [...] Read more.
One of the main questions facing precision agriculture is the evaluation of different algorithms for the delineation of homogeneous management zones. In the present study, a new approach based on the use of time series of satellite imagery, collected during two consecutive growing seasons, was proposed. Texture analysis performed using the Gray-Level Co-Occurrence Matrix (GLCM) was used to integrate and correct the sum of the vegetation indices maps (NDVI and MCARI2) and define the homogenous productivity zones on ten durum wheat fields in southern Italy. The homogenous zones identified through the method that integrates the GLCM indices with the spectral indices studied showed a greater accuracy (0.18–0.22 Mg ha−1 for ∑NDVIs + GLCM and 0.05–0.49 Mg ha−1 for ∑MCARI2s + GLCM) with respect to the methods that considered only the sum of the indices. Best results were also obtained with respect to the homogeneous zones derived by using yield maps of the previous year or vegetation indices acquired in a single day. Therefore, the survey methods based on the data collected over the entire study period provided the best results in terms of estimated yield; the addition of clustering analysis performed with the GLCM method allowed to further improve the accuracy of the estimate and better define homogeneous productivity zones of durum wheat fields. Full article
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Article
Quantification of Changes in Rice Production for 2003–2019 with MODIS LAI Data in Pursat Province, Cambodia
Remote Sens. 2021, 13(10), 1971; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13101971 - 18 May 2021
Cited by 1 | Viewed by 650
Abstract
Rice is not merely a staple food but an important source of income in Cambodia. Rapid socioeconomic development in the country affects farmers’ management practices, and rice production has increased almost three-fold over two decades. However, detailed information about the recent changes in [...] Read more.
Rice is not merely a staple food but an important source of income in Cambodia. Rapid socioeconomic development in the country affects farmers’ management practices, and rice production has increased almost three-fold over two decades. However, detailed information about the recent changes in rice production is quite limited and mainly obtained from interviews and statistical data. Here, we analyzed MODIS LAI data (MCD152H) from 2003 to 2019 to quantify rice production changes in Pursat Province, one of the great rice-producing areas in Cambodia. Although the LAI showed large variations, the data clearly indicate that a major shift occurred in approximately 2010 after applying smoothing methods (i.e., hierarchical clustering and the moving average). This finding is consistent with the results of the interviews with the farmers, which indicate that earlier-maturing cultivars had been adopted. Geographical variations in the LAI pattern were illustrated at points analyzed along a transverse line from the mountainside to the lakeside. Furthermore, areas of dry season cropping were detected by the difference in monthly averaged MODIS LAI data between January and April, which was defined as the dry season rice index (DSRI) in this study. Consequently, three different types of dry season cropping areas were recognized by nonhierarchical clustering of the annual LAI transition. One of the cropping types involved an irrigation-water-receiving area supported by canal construction. The analysis of the peak LAI in the wet and dry seasons suggested that the increase in rice production was different among cropping types and that the stagnation of the improvements and the limitation of water resources are anticipated. This study provides valuable information about differences and changes in rice cropping to construct sustainable and further-improved rice production strategies. Full article
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

1.Title: Quantification of changes in rice production for 2003-2019 with MODIS LAI data in Pursat province, Cambodia

Author:Yu Iwahashi, Rongling Ye, Satoru Kobayashi, Hor Sanara, Kim Soben and Koki Homma

2.

Title: Remote sensing identification of “bare soil” periods in intensive-rotation agriculture areas: current situation, valorization and future perspective

Authors: Nicolas Greggio, Martina Cimatti, Andrea Baraldi, Contin Andrea, Diego Marazza

Abstract: Due to population growth, the demand for food and energy is expected to double by 2050 resulting in competitive growth among resources. In regions where wide agronomic rotation practices are adopted, soils remain often unproductive in the period between two main crops, causing leaching of nutrients, erosion, acceleration organic matter degradation and a production gap. Changes in soil management practices and policies are key to meeting the growing demand of sustainable food and energy, while limiting sectorial GHG emissions and the risks of soil depletion. In this sense, Remote Sensing data are becoming crucial for precision agriculture (drones) and for soil characterization and monitoring (satellites) in order to increase sustainable agricultural yield and limit resource consumption and degradation.
Annual crops and rotation activities lead to the periodic presence of "bare soil" – unproductive soil- over time. At this time, no studies provided a dedicated regional framework to draw attention to this issue and no data regarding their temporal and territorial extension exist.
This study aims to map cultivated but non-productive soils ("Bare Soil", BS) using Earth Observation approach (EO) during 2017 taken as reference year for a pilot area identified in the Emilia Romagna Region (Northern Italy). Once identified, the BSs were studied to define their territorial and temporal extension through the year providing the first available quantification of BSs in relevant agricultural-oriented region. Subsequently, an energy valorisation scenario is proposed with non-food crops as a production optimization strategy.
The applied methodology is an automatic workflow that involves the use of Sentinel-2 images, an optical multispectral satellite with a return time of 5 days, with a ground spatial resolution of 20 m. The images pre-processed with Satellite Image Automatic Mapper ™ (SIAM ™) were later managed on the QGIS platform for BSs mapping and calculation and subsequently validated by ground-truths provided by the Regional Agriculture Authority.
The results show that about 10 to 15% of the total Utilized Agricultural Area in Emilia Romagna is BS between March and June (35000 hectares) and between July and October (25000 hectares). The size of most of the BSs plots varies from 0.5 to 2 ha, however about 30% of the BSs plots have extensions greater than 3 ha, sufficient to justify their agronomic exploitation.

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3. Title: Comparing fixed and variable rate nitrogen fertilization of barley
Authors: Carolina Fabbri1, Anna Dalla Marta1, MarcoNapoli1, Simone Orlandini1,2, Marco Mancini1,2, Leonardo Verdi1

Affiliations:

1 Department of Agriculture, Food, Environment and Forestry (DAGRI) - University of Florence, Piazzale delle Cascine, 18–50144 Florence, Italy
2 Foundation for Climate and Sustainability (FCS) – Via Caproni, 8, 50145 Firenze, Italy
* Corresponding: [email protected]; Tel.: +39 0552755741

Abstract: Climate-changing gas emissions from nitrogen (N) fertilization is one of the major environmental issue in agriculture. Precision and variable rate fertilization techniques, which aim to match inputs to site-specific field conditions  accounting for the spatial variability of fertility, are considered valid mitigation techniques. Nevertheless,  their real effectiveness in reducing GHGs emissions to the atmosphere has not yet been sufficiently investigated. In this study, different N management techniques were compared in terms of GHGs reducing potential, crop yield response and nitrogen use efficiency (NUE). To this aim, four fertilization treatments were conducted on barley cultivated in 1 m3 tanks: conventional N fertilization (fixed rate), variable rate with granular fertilizer and with foliar liquid supplement, and control (no fertilization). The N variable rates were determined with the Greenseeker (Trimble), a handheld proximal sensor used to assess the health of crops. The results demonstrated some advantages in both variable rate techniques. In fact, they have determined crop yields similar to those obtained from conventional fertilization, but with a significant reduction in the quantity of N distributed and a consequent increase in the NUE. Furthermore, the application of foliar nitrogen resulted in an increase of grain proteins, which can positively affect the quality of the harvested product. However, the analysis of the average and cumulative GHGs emissions measured during the experiment did not show significant differences between the treatments, suggesting that, in our experimental conditions, variable rate fertilization of barley had no mitigating effects on climate-altering atmospheric emissions.

Keywords: Proximal sensors; precision agriculture; GHG; NUE; nutrient management

 

4. Title: Crop and Bare Soils Monitoring through data with different spatial resolution and identical spectral band width: a statistical comparison between Sentinel-2 satellite and Unmanned Aerial System products

Dubbini M.1, Palumbo N. 1, De Giglio M. 1, Barbarella M. 2, Zucca F. 3, Tornato A. 4

1 DiSCi, Geography Sec., University of Bologna, Piazza San Giovanni in Monte 2, 40124, Bologna, Italy

2 DICAM, University of Bologna, Viale Risorgimento, 2, 40136, Bologna, Italy

3 Department of Earth and Environmental Sciences, University of Pavia, Pavia, 27100, Italy

4 Italian National Institute for Environmental Protection and Research (ISPRA), Via Vitaliano Brancati 48, Roma, 00144, Italy

Abstract: The growing need for sustainable management approaches of crops and bare soils requires measurements at multiple scale (space and time) field system level, that became increasingly accurate, in order to continuously provide precise indications for the optimization of agronomic practices and to follow their impact over time. Precision farming relies more and more on Earth Observation (EO), and proximal sensing tools are among the most advisable instruments in that arena. In the last decade, UAS platforms became increasingly flexible and low-cost elements, capable of providing on-demand high spatial, temporal and spectral data, fundamentals to achieve the targets of smart agriculture.

At the same time, the availability of systematic, free and open medium resolution satellite images (just to cite LANDSAT and Sentinel Copernicus) allows to increase the amount of available data and improve their quality for the purpose of monitoring crop and soils.

Agricultural practices, such as crop rotations and polyculture, greatly benefits from the establishment and availability of through EO derived time series. This information, at least for scenarios characterized by medium-small agricultural plots, eventually suffers from an only average spatial resolution capacity and from the possible impact of measurement jumps, linked to adverse weather conditions, with obvious limitations in the products of the EO derived data like classifications, vegetation index, etc.

In this context the proximal and satellite remote sensing data cooperation seems indeed a good practice for present and future.

The primary purpose of this work is the development of a sound protocol based on statistical comparison between satellite data Copernicus Sentinel2 MSI (S2) and a multispectral sensor mounted on a UAV, featured by a spectral deployment identical to S2. The experimental dataset, based on simultaneously acquired proximal and S2 data, concerns an agricultural field of Pisa (Tuscany), cultivated with Maize. To understand how the two systems, comparable but quite different in terms of resolution and atmosphere impacts, can effectively cooperate to create a value-added product, statistical tests have been applied on bands and derived Vegetation & Soil index.

In general, as expected, due to the mentioned impacts, the outcomes show a heterogeneous behavior with difference between the coincident bands as well for the derived indices, modulated in same manner by the phenological status (eg during the canopies developments).

Analytical approaches and perspectives for correct integration are presented

5. Shedding light on the use of NDVI to estimate leaf area index of field crops 

The integration between remote sensing data and crop models has been increasingly performed in the last decades and demonstrated to be effective in enhancing models accuracy in reproducing plant biomass accumulation and final yield from farm to regional scale. Leaf Area Index (LAI) is the most used variable to update in-season simulation dynamics, and a plethora of research studies focused on the use of Normalized Difference Vegetation Index (NDVI) to derive LAI. Remote sensing makes available NDVI data over large areas, which are converted into LAI by means of generic algorithms (e.g. by LAI from MODIS), or by dedicated elaboration tools (e.g. ESA SNAP algorithm); in parallel, many researchers developed specific equations to derive LAI from NDVI, using empirical relationships grounded on field data collection. We reviewed here the available methods to convert NDVI into LAI, focusing on maize and wheat crop. The literature search focused on papers published in ISI journals since 2000s, yielding more than 100 papers where the conversion NDVI to LAI has been performed. We analyse the mathematical form of the equations, the remote sensing sensors used, the performance of the method, the integration with crop models and the scale of application, aiming at identifying the properties of the distribution of available functions. We proposed specific ensembles of NDVI to LAI conversion methods, specific for maize and wheat, and we quantified the uncertainty associated. The available equations have been implemented in a software component and released to third parties as API, and the accuracy of the ensemble has been tested using independent datasets of reference LAI data collected in field experiments. This work provides a quantitative assessment of the state-of-the-art of the methodologies used to estimate LAI from NDVI on wheat and maize, and releases a public available software as a service for researchers who need to integrate remote sensing data with crop simulation models.

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