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Remote Sensing for Cropping Systems and Bare Soils Monitoring and Optimization

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 38183

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Special Issue Editors

Dr. Ephrem Habyarimana

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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

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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)
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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

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Keywords

Remote sensing
Earth Observation
Environmental performance
Bare soil management
Artificial intelligence
Cropping system optimization

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Published Papers (7 papers)

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Research

14 pages, 1596 KB

Open AccessArticle

On the Use of NDVI to Estimate LAI in Field Crops: Implementing a Conversion Equation Library

by Sofia Bajocco, Fabrizio Ginaldi, Francesco Savian, Danilo Morelli, Massimo Scaglione, Davide Fanchini, Elisabetta Raparelli and Simone Ugo Maria Bregaglio

Remote Sens. 2022, 14(15), 3554; https://doi.org/10.3390/rs14153554 - 25 Jul 2022

Cited by 49 | Viewed by 10968

Abstract

The leaf area index (LAI) is a direct indicator of vegetation activity, and its relationship with the normalized difference vegetation index (NDVI) has been investigated in many research studies. Remote sensing makes available NDVI data over large areas, and researchers developed specific equations to derive the LAI from the NDVI, using empirical relationships grounded in field data collection. We conducted a literature search using “NDVI” AND “LAI” AND “crop” as the search string, focusing on the period 2017–2021. We reviewed the available equations to convert the NDVI into the LAI, aiming at (i) exploring the fields of application of an NDVI-based LAI, (ii) characterizing the mathematical relationships between the NDVI and LAI in the available equations, (iii) creating a software library with the retrieved methods, and (iv) releasing a publicly available software as a service, implementing these equations to foster their reuse by third parties. The literature search yielded 92 articles since 2017, where 139 equations were proposed. We analyzed the mathematical form of both the single equations and ensembles of the NDVI to LAI conversion methods, specific for crop, sensor, and biome. The characterization of the functions highlighted two main constraints when developing an NDVI-LAI conversion function: environmental conditions (i.e., water and light resource, land cover, and climate) and the availability of recurring data during the growing season. We found that the trend of an NDVI-LAI function is usually driven by the ecosystem water availability for the crop rather than by the crop type itself, as well as by the data availability; the data should be adequate in terms of the sample size and temporal resolution for reliably representing the phenomenon under investigation. Our study demonstrated that the choice of the NDVI-LAI equation (or ensemble of equations) should be driven by the trade-off between the scale of the investigation and data availability. The implementation of an extensible and reusable software library publicly queryable via API represents a valid mean to assist researchers in choosing the most suitable equations to perform an NDVI-LAI conversion. Full article

(This article belongs to the Special Issue Remote Sensing for Cropping Systems and Bare Soils Monitoring and Optimization)

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30 pages, 13220 KB

Open AccessArticle

Recognition of the Bare Soil Using Deep Machine Learning Methods to Create Maps of Arable Soil Degradation Based on the Analysis of Multi-Temporal Remote Sensing Data

by Dmitry I. Rukhovich, Polina V. Koroleva, Danila D. Rukhovich and Alexey D. Rukhovich

Remote Sens. 2022, 14(9), 2224; https://doi.org/10.3390/rs14092224 - 6 May 2022

Cited by 17 | Viewed by 4007

Abstract

The detection of degraded soil distribution areas is an urgent task. It is difficult and very time consuming to solve this problem using ground methods. The modeling of degradation processes based on digital elevation models makes it possible to construct maps of potential degradation, which may differ from the actual spatial distribution of degradation. The use of remote sensing data (RSD) for soil degradation detection is very widespread. Most often, vegetation indices (indicative botany) have been used for this purpose. In this paper, we propose a method for constructing soil maps based on a multi-temporal analysis of the bare soil surface (BSS). It is an alternative method to the use of vegetation indices. The detection of the bare soil surface was carried out using the spectral neighborhood of the soil line (SNSL) technology. For the automatic recognition of BSS on each RSD image, computer vision based on deep machine learning (neural networks) was used. A dataset of 244 BSS distribution masks on 244 Landsat 4, 5, 7, and 8 scenes over 37 years was developed. Half of the dataset was used as a training sample (Landsat path/row 173/028). The other half was used as a test sample (Landsat path/row 174/027). Binary masks were sufficient for recognition. For each RSD pixel, value “1” was set when determining the BSS. In the absence of BSS, value “0” was set. The accuracy of the machine prediction of the presence of BSS was 75%. The detection of degradation was based on the average long-term spectral characteristics of the RED and NIR bands. The coefficient C_mean, which is the distance of the point with the average long-term values of RED and NIR from the origin of the spectral plane RED/NIR, was calculated as an integral characteristic of the mean long-term values. Higher long-term average values of spectral brightness served as indicators of the spread of soil degradation. To test the method of constructing soil degradation maps based on deep machine learning, an acceptance sample of 133 Landsat scenes of path/row 173/026 was used. On the territory of the acceptance sample, ground verifications of the maps of the coefficient C_mean were carried out. Ground verification showed that the values of this coefficient make it possible to estimate the content of organic matter in the plow horizon (R² = 0.841) and the thickness of the humus horizon (R² = 0.8599). In total, 80 soil pits were analyzed on an area of 649 ha on eight agricultural fields. Type I error (false positive) of degradation detection was 17.5%, and type II error (false negative) was 2.5%. During the determination of the presence of degradation by ground methods, 90% of the ground data coincided with the detection of degradation from RSD. Thus, the quality of machine learning for BSS recognition is sufficient for the construction of soil degradation maps. The SNSL technology allows us to create maps of soil degradation based on the long-term average spectral characteristics of the BSS. Full article

(This article belongs to the Special Issue Remote Sensing for Cropping Systems and Bare Soils Monitoring and Optimization)

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21 pages, 3005 KB

Open AccessArticle

Sentinel-2 Data and Unmanned Aerial System Products to Support Crop and Bare Soil Monitoring: Methodology Based on a Statistical Comparison between Remote Sensing Data with Identical Spectral Bands

by Marco Dubbini, Nicola Palumbo, Michaela De Giglio, Francesco Zucca, Maurizio Barbarella and Antonella Tornato

Remote Sens. 2022, 14(4), 1028; https://doi.org/10.3390/rs14041028 - 20 Feb 2022

Cited by 3 | Viewed by 3692

Abstract

The growing need for sustainable management approaches of crops and bare soils requires measurements at a multiple scale (space and time) field system level, which have become increasingly accurate. In this context, proximal and satellite remote sensing data cooperation seems good practice for the present and future. The primary purpose of this work is the development of a sound protocol based on a statistical comparison between Copernicus Sentinel-2 MIS satellite data and a multispectral sensor mounted on an Unmanned Aerial Vehicle (UAV), featuring spectral deployment identical to Sentinel-2. The experimental dataset, based on simultaneously acquired proximal and Sentinel-2 data, concerns an agricultural field in Pisa (Tuscany), cultivated with corn. To understand how the two systems, comparable but quite different in terms of spatial resolution and atmosphere impacts, can effectively cooperate to create a value-added product, statistical tests were applied on bands and the derived Vegetation and Soil index. Overall, as expected, due to the mentioned impacts, the outcomes show a heterogeneous behavior with a difference between the coincident bands as well for the derived indices, modulated in the same manner by the phenological status (e.g., during the canopy developments) or by vegetation absence. Instead, similar behavior between two sensors occurred during the maturity phase of crop plants. Full article

(This article belongs to the Special Issue Remote Sensing for Cropping Systems and Bare Soils Monitoring and Optimization)

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21 pages, 54772 KB

Open AccessArticle

Assimilation of Wheat and Soil States into the APSIM-Wheat Crop Model: A Case Study

by Yuxi Zhang, Jeffrey P. Walker, Valentijn R. N. Pauwels and Yuval Sadeh

Remote Sens. 2022, 14(1), 65; https://doi.org/10.3390/rs14010065 - 24 Dec 2021

Cited by 25 | Viewed by 5001

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 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

(This article belongs to the Special Issue Remote Sensing for Cropping Systems and Bare Soils Monitoring and Optimization)

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17 pages, 7270 KB

Open AccessArticle

Soil Salinity Inversion in Coastal Corn Planting Areas by the Satellite-UAV-Ground Integration Approach

by Guanghui Qi, Chunyan Chang, Wei Yang, Peng Gao and Gengxing Zhao

Remote Sens. 2021, 13(16), 3100; https://doi.org/10.3390/rs13163100 - 5 Aug 2021

Cited by 44 | Viewed by 5070

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 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

(This article belongs to the Special Issue Remote Sensing for Cropping Systems and Bare Soils Monitoring and Optimization)

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21 pages, 6878 KB

Open AccessArticle

Methodology for the Definition of Durum Wheat Yield Homogeneous Zones by Using Satellite Spectral Indices

by Elio Romano, Simone Bergonzoli, Ivano Pecorella, Carlo Bisaglia and Pasquale De Vita

Remote Sens. 2021, 13(11), 2036; https://doi.org/10.3390/rs13112036 - 21 May 2021

Cited by 23 | Viewed by 3111

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 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

(This article belongs to the Special Issue Remote Sensing for Cropping Systems and Bare Soils Monitoring and Optimization)

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17 pages, 8645 KB

Open AccessArticle

Quantification of Changes in Rice Production for 2003–2019 with MODIS LAI Data in Pursat Province, Cambodia

by Yu Iwahashi, Rongling Ye, Satoru Kobayashi, Kenjiro Yagura, Sanara Hor, Kim Soben and Koki Homma

Remote Sens. 2021, 13(10), 1971; https://doi.org/10.3390/rs13101971 - 18 May 2021

Cited by 17 | Viewed by 3917

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 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

(This article belongs to the Special Issue Remote Sensing for Cropping Systems and Bare Soils Monitoring and Optimization)

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