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Agronomy
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Remote Sensing of Agricultural Monitoring
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Remote Sensing of Agricultural Monitoring

A special issue of Agronomy (ISSN 2073-4395).

Deadline for manuscript submissions: closed (30 April 2020) | Viewed by 55215

Special Issue Editors

Prof. Dr. Jesús Delegido Gómez

E-Mail Website
Guest Editor
Image Processing Laboratory, University of Valencia, Valencia, Spain
Interests: remote sensing; biophysical parameters of vegetation (chlorophyll content, leaf area index, water content); agroecosystems; inland water quality
Dr. Juan-Carlos Jiménez-Muñoz

E-Mail Website
Guest Editor
Image Processing Laboratory, University of Valencia, Valencia, Spain
Interests: thermal remote sensing; land surface temperature/emissivity retrieval; temperature trends over tropical forests
Special Issues, Collections and Topics in MDPI journals
Dr. Mª Pilar Cendrero-Mateo

E-Mail Website
Guest Editor
Image Processing Laboratory, University of Valencia, Valencia, Spain
Interests: chlorophyll fluorescence; point and imaging spectroscopy; plant phenotyping; remote sensing of photosynthesis

Special Issue Information

Dear Colleagues,

In recent decades, remote sensing has become an important tool to improve agricultural management. As a consequence of important technological development and the launch of new Earth Observation missions, e.g., ESA’s Copernicus program, open-access high spectral and temporal resolution images are now available which allow obtaining detailed crop maps and agronomic products, such as the crops’ chlorophyll content, leaf area index (LAI), evapotranspiration or soil surface moisture (SSM). Furthermore, in 2022, the Fluorescence Explorer (FLEX) mission will be launched in tandem with Copernicus Sentinel-3, allowing the simultaneous measurements of sun-induced fluorescence (SIF) along with spectrally resolved visible near-infrared (VIS-NIR), and thermal–infrared reflectance. These and other products make it possible to improve our knowledge of crop dynamics and agricultural practices, which are useful for farmers, stakeholders, and policy makers, allowing the early detection of pests/diseases, improving farmers’ nutrients/water management, and consequently increasing the final crop yield.

This Special Issue invites original research to improve our knowledge of agro-ecosystems dynamics using remote sensing techniques. Topics may cover but are not limited to:

  • Algorithms for the early detection of nutrients/water deficits as well as pests/diseases;
  • Development of novel algorithms to improve LAI, Chlorophyll, and other biophysical parameters;
  • Remote sensing studies that use ground, airborne, or satellite approaches to monitor plant dynamics;
  • Remote sensing studies focuses on image fusion combining radar, optical, and thermal images, such as image classification, indices or statistical methods, such as machine learning, temporal analysis or image fusion with radar, optical, and thermal images;
  • Water quality for irrigation by remote sensing.

We invite both theoretical and application-oriented studies to be submitted, mainly those based on operational satellites such as Sentinel-1, 2 and 3 or other current or future hyperspectral missions.

Prof. Dr. Jesús Delegido Gómez
Prof. Dr. Juan Carlos Jiménez-Muñoz
Dr. Mª Pilar Cendrero-Mateo
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. Agronomy 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

  • Biophysical parameters (Chlorophyll content, LAI, water content, fluorescence, evapotranspiration, etc.)
  • Crop classification maps
  • Yield forecasting
  • Multitemporal image analysis
  • Empirical or physics-based methods
  • Vegetation stress detection
  • Sun induced fluorescence

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Published Papers (11 papers)

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Research

23 pages, 7735 KiB  
Article
A Methodological Approach for Irrigation Detection in the Frame of Common Agricultural Policy Checks by Monitoring
by Vanessa Paredes-Gómez, Alberto Gutiérrez, Vicente Del Blanco and David A. Nafría
Agronomy 2020, 10(6), 867; https://doi.org/10.3390/agronomy10060867 - 18 Jun 2020
Cited by 10 | Viewed by 4458
Abstract
New needs have arisen from member states and paying agencies (PA) to achieve the compliance assessment from farmers in the frame of the European Common Agricultural Policy (CAP). Traditional field inspection (on-the-spot checks) and computer-aided photointerpretation (CAPI) carried out by each PA over [...] Read more.
New needs have arisen from member states and paying agencies (PA) to achieve the compliance assessment from farmers in the frame of the European Common Agricultural Policy (CAP). Traditional field inspection (on-the-spot checks) and computer-aided photointerpretation (CAPI) carried out by each PA over a sample of 5% of the applicants are being replaced by a 100% sample Copernicus satellite-based system (checks by monitoring, CbM). This new approach will be an integral part of the Area Monitoring System that will be part of the Integrated Administrative Control System (IACS) in the post-2020 CAP. Among all the aid schemes having to be analyzed, there are some specific aids in which the detection of irrigation of certain crops can result in a no-compliance resolution. Apart from that, the knowledge of the truly irrigated area in each campaign has always been data of great interest in irrigation planning, crop yield statistics, and water management, and now more than ever. Although several sources of information exist, there is no consensual methodology for estimating the actual irrigated area. The objective of this study is to propose a methodological approach based mainly on Copernicus Sentinel and IACS data not only to detect the surface of herbaceous crops that have been actually irrigated but also to derive a product suitable to be incorporated into the CAP monitoring process system. This methodology is already being used operationally during the ongoing campaign 2020 by Castile and León PA. Full article
(This article belongs to the Special Issue Remote Sensing of Agricultural Monitoring)
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17 pages, 6487 KiB  
Article
RiceSAP: An Efficient Satellite-Based AquaCrop Platform for Rice Crop Monitoring and Yield Prediction on a Farm- to Regional-Scale
by Watcharee Veerakachen and Mongkol Raksapatcharawong
Agronomy 2020, 10(6), 858; https://doi.org/10.3390/agronomy10060858 - 17 Jun 2020
Cited by 6 | Viewed by 4081
Abstract
Advanced technologies in the agricultural sector have been adopted as global trends in response to the impact of climate change on food sustainability. An ability to monitor and predict crop yields is imperative for effective agronomic decision making and better crop management. This [...] Read more.
Advanced technologies in the agricultural sector have been adopted as global trends in response to the impact of climate change on food sustainability. An ability to monitor and predict crop yields is imperative for effective agronomic decision making and better crop management. This work proposes RiceSAP, a satellite-based AquaCrop processing system for rice whose climatic input is derived from TERRA/MODIS-LST and FY-2/IR-rainfall products to provide crop monitoring and yield prediction services at regional-scale with no need for weather station. The yield prediction accuracy is significantly improved by our proposed recalibration algorithm on the simulated canopy cover (CC) using Sentinel-2 NDVI product. A developed mobile app provides an intuitive interface for collecting farm-scale inputs and providing timely feedbacks to farmers to make informed decisions. We show that RiceSAP could predict yields 2 months before harvest with a mean absolute percentage error (MAPE) of 14.8%, in the experimental field. Further experiments on randomly selected 20 plots with various soil series showed comparable results with an average MAPE of 16.7%. Thus, this work is potentially applicable countrywide; and can be beneficial to all stakeholders in the entire rice supply chain for effective adaptation to climate change. Full article
(This article belongs to the Special Issue Remote Sensing of Agricultural Monitoring)
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27 pages, 8925 KiB  
Article
Assessment of Multi-Date Sentinel-1 Polarizations and GLCM Texture Features Capacity for Onion and Sunflower Classification in an Irrigated Valley: An Object Level Approach
by Gabriel Rodrigo Caballero, Gabriel Platzeck, Alejandro Pezzola, Alejandra Casella, Cristina Winschel, Samanta Soledad Silva, Emilia Ludueña, Nieves Pasqualotto and Jesús Delegido
Agronomy 2020, 10(6), 845; https://doi.org/10.3390/agronomy10060845 - 13 Jun 2020
Cited by 24 | Viewed by 6528
Abstract
The objective of this work is to evaluate the capacity of the C-band Synthetic Aperture Radar (SAR) time series imagery, acquired by the European satellite Sentinel-1 (S1), for the agriculture crop classification and its reliability to differentiate onion from sunflower, among others. The [...] Read more.
The objective of this work is to evaluate the capacity of the C-band Synthetic Aperture Radar (SAR) time series imagery, acquired by the European satellite Sentinel-1 (S1), for the agriculture crop classification and its reliability to differentiate onion from sunflower, among others. The work then focused on classifying land cover in intensively cultivated agricultural regions. The study was developed in the Bonaerense Valley of the Colorado River (BVCR), Buenos Aires Province in Argentina, backed up by the field truth of 1634 field samples. In addition to the onion and sunflower crops, there are other crops present in the study area such as cereals, alfalfa, potatoes and maize, which are considered as the image background in the classification process. The field samples database was used for training and supporting a supervised classification with two machine learning algorithms—Random Forest (RF) and Support Vector Machine (SVM)—obtaining high levels of accuracy in each case. Different S1 SAR time-series features were used to assess the performance of S1 crop classification in terms of polarization VH+VV, Grey Level Co-occurrence Matrix (GLCM) image texture and a combination of both. The analysis of SAR data and their features was carried out at OBIA lot level (Object Based Image Analysis) showing an optimal strategy to counteract the effect of the residual and inherent speckle noise of the radar signal. In the process of differentiating onion and sunflower crops, the analysis of the VH+VV stack with the SVM algorithm delivered the best statistical classification results in terms of Overall Accuracy (OA) and Kappa Index, (Kp) when other crops (image background) were not considered (OA = 95.35%, Kp = 0.89). Certainly, the GLCM texture analysis derived from the S1 SAR images is a valuable source of information for obtaining very good classification results. When differentiating sunflower from onion considering also other crops present in the BVCR, the GLCM stack proved to be the most suitable dataset analyzed in this work (OA = 89.98%, Kp = 0.66 for SVM algorithm). This working methodology is applicable to other irrigated valleys in Argentina dedicated to intensive crops. There are also variables inherent to each lot, soil, crop and agricultural producer that differ according to the study area and that should be considered for each case in the future. Full article
(This article belongs to the Special Issue Remote Sensing of Agricultural Monitoring)
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23 pages, 7142 KiB  
Article
Mapping Productivity and Essential Biophysical Parameters of Cultivated Tropical Grasslands from Sentinel-2 Imagery
by Amparo Cisneros, Peterson Fiorio, Patricia Menezes, Nieves Pasqualotto, Shari Van Wittenberghe, Gustavo Bayma and Sandra Furlan Nogueira
Agronomy 2020, 10(5), 711; https://doi.org/10.3390/agronomy10050711 - 15 May 2020
Cited by 15 | Viewed by 3948
Abstract
Nitrogen (N) is the main nutrient element that maintains productivity in forages; it is inextricably linked to dry matter increase and plant support capacity. In recent years, high spectral and spatial resolution remote sensors, e.g., the European Space Agency (ESA)’s Sentinel satellite missions, [...] Read more.
Nitrogen (N) is the main nutrient element that maintains productivity in forages; it is inextricably linked to dry matter increase and plant support capacity. In recent years, high spectral and spatial resolution remote sensors, e.g., the European Space Agency (ESA)’s Sentinel satellite missions, have become freely available for agricultural science, and have proven to be powerful monitoring tools. The use of vegetation indices has been essential for crop monitoring and biomass estimation models. The objective of this work is to test and demonstrate the applicability of different vegetation indices to estimate the biomass productivity, the foliar nitrogen content (FNC), the plant height and the leaf area index (LAI) of several tropical grasslands species submitted to different nitrogen (N) rates in an experimental area of São Paulo, Brazil. Field reflectance data of Panicum maximum and Urochloa brizantha species’ cultivars were taken and convoluted to the Sentinel-2 satellite bands. Subsequently, different vegetation indices (Normalized Difference Vegetation Index (NDI), Three Band Index (TBI), Difference light Height (DLH), Three Band Dall’Olmo (DO), and Normalized Area Over reflectance Curve (NAOC)) were tested for the experimental grassland areas, and composed of Urochloa decumbens and Urochloa brizantha grass species, which were sampled and destructively analyzed. Our results show the use of different relevant Sentinel-2 bands in the visible (VIS)–near infrared (NIR) regions for the estimation of the different biophysical parameters. The FNC obtained the best correlation for the TBI index combining blue, green and red bands with a determination coefficient (R2) of 0.38 and Root Mean Square Error (RMSE) of 3.4 g kg−1. The estimation of grassland productivity based on red-edge and NIR bands showed a R2 = 0.54 and a RMSE = 1800 kg ha−1. For the LAI, the best index was the NAOC (R2 = 0.57 and RMSE = 1.4 m2 m−2). High values of FNC, productivity and LAI based on different sets of Sentinel-2 bands were consistently obtained for areas under N fertilization. Full article
(This article belongs to the Special Issue Remote Sensing of Agricultural Monitoring)
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16 pages, 18655 KiB  
Article
Optimizing Gaussian Process Regression for Image Time Series Gap-Filling and Crop Monitoring
by Santiago Belda, Luca Pipia, Pablo Morcillo-Pallarés and Jochem Verrelst
Agronomy 2020, 10(5), 618; https://doi.org/10.3390/agronomy10050618 - 27 Apr 2020
Cited by 30 | Viewed by 4668
Abstract
Image processing entered the era of artificial intelligence, and machine learning algorithms emerged as attractive alternatives for time series data processing. Satellite image time series processing enables crop phenology monitoring, such as the calculation of start and end of season. Among the promising [...] Read more.
Image processing entered the era of artificial intelligence, and machine learning algorithms emerged as attractive alternatives for time series data processing. Satellite image time series processing enables crop phenology monitoring, such as the calculation of start and end of season. Among the promising algorithms, Gaussian process regression (GPR) proved to be a competitive time series gap-filling algorithm with the advantage of, as developed within a Bayesian framework, providing associated uncertainty estimates. Nevertheless, the processing of time series images becomes computationally inefficient in its standard per-pixel usage, mainly for GPR training rather than the fitting step. To mitigate this computational burden, we propose to substitute the per-pixel optimization step with the creation of a cropland-based precalculations for the GPR hyperparameters θ . To demonstrate our approach hardly affects the accuracy in fitting, we used Sentinel-2 LAI time series over an agricultural region in Castile and Leon, North-West Spain. The performance of image reconstructions were compared against the standard per-pixel GPR time series processing. Results showed that accuracies were on the same order (RMSE 0.1767 vs. 0.1564 [ m 2 / m 2 ] , 12% RMSE degradation) whereas processing time accelerated about 90 times. We further evaluated the alternative option of using the same hyperparameters for all the pixels within the complete scene. It led to similar overall accuracies over crop areas and computational performance. Crop phenology indicators were also calculated for the three different approaches and compared. Results showed analogous crop temporal patterns, with differences in start and end of growing season of no more than five days. To the benefit of crop monitoring applications, all the gap-filling and phenology indicators retrieval techniques have been implemented into the freely downloadable GUI toolbox DATimeS. Full article
(This article belongs to the Special Issue Remote Sensing of Agricultural Monitoring)
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16 pages, 3661 KiB  
Article
Mapping Maize Cropping Patterns in Dak Lak, Vietnam Through MODIS EVI Time Series
by Ha Thi Thu Nguyen, Loc Van Nguyen, C.A.J.M (Kees) de Bie, Ignacio A. Ciampitti, Duc Anh Nguyen, Minh Van Nguyen, Luciana Nieto, Rai Schwalbert and Long Viet Nguyen
Agronomy 2020, 10(4), 478; https://doi.org/10.3390/agronomy10040478 - 1 Apr 2020
Cited by 12 | Viewed by 5531
Abstract
Land use maps specifying up-to-date acreage information on maize (Zea mays L.) cropping patterns are required by many stakeholders in Vietnam. Government statistics, however, lag behind by one year, and the official land use maps are only updated at 5-year intervals. The [...] Read more.
Land use maps specifying up-to-date acreage information on maize (Zea mays L.) cropping patterns are required by many stakeholders in Vietnam. Government statistics, however, lag behind by one year, and the official land use maps are only updated at 5-year intervals. The aim of this study was to apply the Savitzky–Golay algorithm to reconstruct noisy Enhanced Vegetation Index (EVI) time series (2003–2018) from Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MOD13Q1) to allow timely detection of changes in maize crop phenology, and then to employ a linear kernel Support Vector Machine (SVM) classifier on the reconstructed EVI time series to prepare the present-day maize cropping pattern map of Dak Lak province of Vietnam. The method was able to specify the spatial extent of areas cropped to maize with an overall map accuracy of 79% and could also differentiate the areas cropped to maize just once versus twice annually. The by-district mapped maize acreage shows a good agreement with the official governmental data, with a 0.93 correlation coefficient (r) and a root mean square deviation (RMSD) of 1624 ha. Full article
(This article belongs to the Special Issue Remote Sensing of Agricultural Monitoring)
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15 pages, 2866 KiB  
Article
Combined Use of Multi-Temporal Landsat-8 and Sentinel-2 Images for Wheat Yield Estimates at the Intra-Plot Spatial Scale
by Remy Fieuzal, Vincent Bustillo, David Collado and Gerard Dedieu
Agronomy 2020, 10(3), 327; https://doi.org/10.3390/agronomy10030327 - 28 Feb 2020
Cited by 24 | Viewed by 3609
Abstract
The objective of this study is to address the capabilities of multi-temporal optical images to estimate the fine-scale yield variability of wheat, over a study site located in southwestern France. The methodology is based on the Landsat-8 and Sentinel-2 satellite images acquired after [...] Read more.
The objective of this study is to address the capabilities of multi-temporal optical images to estimate the fine-scale yield variability of wheat, over a study site located in southwestern France. The methodology is based on the Landsat-8 and Sentinel-2 satellite images acquired after the sowing and before the harvest of the crop throughout four successive agricultural seasons, the reflectance constituting the input variables of a statistical algorithm (random forest). The best performances are obtained when the Normalized Difference Vegetation Index (NDVI) is combined with the yield maps collected during the crop rotation, the agricultural season 2014 showing the lower level of performances with a coefficient of determination (R2) of 0.44 and a root mean square error (RMSE) of 8.13 quintals by hectare (q.h−1) (corresponding to a relative error of 12.9%), the three other years being associated with values of R2 close or upper to 0.60 and RMSE lower than 7 q.h−1 (corresponding to a relative error inferior to 11.3%). Moreover, the proposed approach allows estimating the crop yield throughout the agricultural season, by using the successive images acquired from the sowing to the harvest. In such cases, early and accurate yield estimates are obtained three months before the end of the crop cycle. At this phenological stage, only a slight decrease in performance is observed compared to the statistic obtained just before the harvest. Full article
(This article belongs to the Special Issue Remote Sensing of Agricultural Monitoring)
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12 pages, 4462 KiB  
Article
An Assessment of the Spatial and Temporal Distribution of Soil Salinity in Combination with Field and Satellite Data: A Case Study in Sujawal District
by Kashif Ali Solangi, Altaf Ali Siyal, Yanyou Wu, Bilawal Abbasi, Farheen Solangi, Imran Ali Lakhiar and Guiyao Zhou
Agronomy 2019, 9(12), 869; https://doi.org/10.3390/agronomy9120869 - 10 Dec 2019
Cited by 23 | Viewed by 5984
Abstract
Soil salinization is a serious environmental issue that significantly influences crop yield and soil fertility, especially in coastal areas. Numerous studies have been conducted on the salinity status in Pakistan. Information about the geospatial and temporal distribution of salinity in the Sujawal district [...] Read more.
Soil salinization is a serious environmental issue that significantly influences crop yield and soil fertility, especially in coastal areas. Numerous studies have been conducted on the salinity status in Pakistan. Information about the geospatial and temporal distribution of salinity in the Sujawal district is still lacking. The present study examines the soil salinity status and the impact of seawater intrusion in the entire district from 1990 to 2017 using field and remote sensing (RS) data. In addition, 210 soil samples at different depths (0–20, 20–40, and 40–60 cm) were collected from randomly selected locations for lab measurements of physiochemical properties. The results showed that the soil texture classes were mainly fine to medium particles. The samples collected at the 0–20 cm depth were mostly dominated by three textural classes of soil: clay at 19.5%, clay loam at 25.6%, and loam at 32.9%. The electrical conductivity (EC) of 65.7% soil samples collected from the top layer exceeded the normal range. The quantitative results indicated that the exchangeable sodium percentage (ESP) ranged between 1.38 and 64.58, and 72.2% of the top layer soil samples had ESP >15, while 81.5% of soil samples were in the normal range of soil pH. Furthermore, the results indicated that the vegetation decreased by 8.6% from 1990 to 2017, while barren land and water bodies increased significantly, by approximately 4.4% and 4.2%, respectively. The extreme and high salinity classes were characterized by high contents of soluble salt on the surface in the Jati and Shah Bandar subdistricts. In addition, the soil EC values at the 0–20 cm depth were significantly correlated with the salinity index (S1). Therefore, it was concluded that more than 50% of the top layer of soil was affected by salinity due to seawater intrusion, low rainfall, climate change, and erratic river flow. It is suggested that remote sensing (RS) data are more suitable for the detection of the soil salinity status of a region and impose a lower cost compared to other conventional approaches. However, this study could provide significant knowledge to land managers, policymakers, and government officials to allow them to take action to implement salinity control measures in the study area. Full article
(This article belongs to the Special Issue Remote Sensing of Agricultural Monitoring)
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14 pages, 2026 KiB  
Article
The Utility of the Upcoming HyspIRI’s Simulated Spectral Settings in Detecting Maize Gray Leafy Spot in Relation to Sentinel-2 MSI, VENµS, and Landsat 8 OLI Sensors
by Mbulisi Sibanda, Onisimo Mutanga, Timothy Dube, John Odindi and Paramu L. Mafongoya
Agronomy 2019, 9(12), 846; https://doi.org/10.3390/agronomy9120846 - 4 Dec 2019
Cited by 7 | Viewed by 2944
Abstract
Considering the high maize yield loses caused by incidences of disease, as well as incomprehensive monitoring initiatives in crop farming, there is a need for spatially explicit, cost-effective, and consistent approaches for monitoring, as well as for forecasting, food-crop diseases, such as maize [...] Read more.
Considering the high maize yield loses caused by incidences of disease, as well as incomprehensive monitoring initiatives in crop farming, there is a need for spatially explicit, cost-effective, and consistent approaches for monitoring, as well as for forecasting, food-crop diseases, such as maize Gray Leaf Spot. Such approaches are valuable in reducing the associated economic losses while fostering food security. In this study, we sought to investigate the utility of the forthcoming HyspIRI sensor in detecting disease progression of Maize Gray Leaf Spot infestation in relation to the Sentinel-2 MSI and Landsat 8 OLI spectral configurations simulated using proximally sensed data. Healthy, intermediate, and severe categories of maize crop infections by the Gray Leaf Spot disease were discriminated based on partial least squares–discriminant analysis (PLS-DA) algorithm. Comparatively, the results show that the HyspIRI’s simulated spectral settings slightly performed better than those of Sentinel-2 MSI, VENµS, and Landsat 8 OLI sensor. HyspIRI exhibited an overall accuracy of 0.98 compared to 0.95, 0.93, and 0.89, which were exhibited by Sentinel-2 MSI, VENµS, and Landsat 8 OLI sensor sensors, respectively. Furthermore, the results showed that the visible section, red-edge, and NIR covered by all the four sensors were the most influential spectral regions for discriminating different Maize Gray Leaf Spot infections. These findings underscore the potential value of the upcoming hyperspectral HyspIRI sensor in precision agriculture and forecasting of crop-disease epidemics, which are necessary to ensure food security. Full article
(This article belongs to the Special Issue Remote Sensing of Agricultural Monitoring)
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22 pages, 2339 KiB  
Article
Retrieval of Evapotranspiration from Sentinel-2: Comparison of Vegetation Indices, Semi-Empirical Models and SNAP Biophysical Processor Approach
by Nieves Pasqualotto, Guido D’Urso, Salvatore Falanga Bolognesi, Oscar Rosario Belfiore, Shari Van Wittenberghe, Jesús Delegido, Alejandro Pezzola, Cristina Winschel and José Moreno
Agronomy 2019, 9(10), 663; https://doi.org/10.3390/agronomy9100663 - 22 Oct 2019
Cited by 42 | Viewed by 7063
Abstract
Remote sensing evapotranspiration estimation over agricultural areas is increasingly used for irrigation management during the crop growing cycle. Different methodologies based on remote sensing have emerged for the leaf area index (LAI) and the canopy chlorophyll content (CCC) estimation, essential biophysical parameters for [...] Read more.
Remote sensing evapotranspiration estimation over agricultural areas is increasingly used for irrigation management during the crop growing cycle. Different methodologies based on remote sensing have emerged for the leaf area index (LAI) and the canopy chlorophyll content (CCC) estimation, essential biophysical parameters for crop evapotranspiration monitoring. Using Sentinel-2 (S2) spectral information, this study performed a comparative analysis of empirical (vegetation indices), semi-empirical (CLAIR model with fixed and calibrated extinction coefficient) and artificial neural network S2 products derived from the Sentinel Application Platform Software (SNAP) biophysical processor (ANN S2 products) approaches for the estimation of LAI and CCC. Four independent in situ collected datasets of LAI and CCC, obtained with standard instruments (LAI-2000, SPAD) and a smartphone application (PocketLAI), were used. The ANN S2 products present good statistics for LAI (R2 > 0.70, root mean square error (RMSE) < 0.86) and CCC (R2 > 0.75, RMSE < 0.68 g/m2) retrievals. The normalized Sentinel-2 LAI index (SeLI) is the index that presents good statistics in each dataset (R2 > 0.71, RMSE < 0.78) and for the CCC, the ratio red-edge chlorophyll index (CIred-edge) (R2 > 0.67, RMSE < 0.62 g/m2). Both indices use bands located in the red-edge zone, highlighting the importance of this region. The LAI CLAIR model with a fixed extinction coefficient value produces a R2 > 0.63 and a RMSE < 1.47 and calibrating this coefficient for each study area only improves the statistics in two areas (RMSE ≈ 0.70). Finally, this study analyzed the influence of the LAI parameter estimated with the different methodologies in the calculation of crop potential evapotranspiration (ETc) with the adapted Penman–Monteith (FAO-56 PM), using a multi-temporal dataset. The results were compared with ETc estimated as the product of the reference evapotranspiration (ETo) and on the crop coefficient (Kc) derived from FAO table values. In the absence of independent reference ET data, the estimated ETc with the LAI in situ values were considered as the proxy of the ground-truth. ETc estimated with the ANN S2 LAI product is the closest to the ETc values calculated with the LAI in situ (R2 > 0.90, RMSE < 0.41 mm/d). Our findings indicate the good validation of ANN S2 LAI and CCC products and their further suitability for the implementation in evapotranspiration retrieval of agricultural areas. Full article
(This article belongs to the Special Issue Remote Sensing of Agricultural Monitoring)
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20 pages, 29546 KiB  
Article
A Copernicus Sentinel-1 and Sentinel-2 Classification Framework for the 2020+ European Common Agricultural Policy: A Case Study in València (Spain)
by Manuel Campos-Taberner, Francisco Javier García-Haro, Beatriz Martínez, Sergio Sánchez-Ruíz and María Amparo Gilabert
Agronomy 2019, 9(9), 556; https://doi.org/10.3390/agronomy9090556 - 16 Sep 2019
Cited by 40 | Viewed by 5060
Abstract
This paper proposes a methodology for deriving an agreement map between the Spanish Land Parcel Information System (LPIS), also known as SIGPAC, and a classification map obtained from multitemporal Sentinel-1 and Sentinel-2 data. The study area comprises the province of València (Spain). The [...] Read more.
This paper proposes a methodology for deriving an agreement map between the Spanish Land Parcel Information System (LPIS), also known as SIGPAC, and a classification map obtained from multitemporal Sentinel-1 and Sentinel-2 data. The study area comprises the province of València (Spain). The approach exploits predictions and class probabilities obtained from an ensemble method of decision trees (boosting trees). The overall accuracy reaches 91.18% when using only Sentinel-2 data and increases up to 93.96% when Sentinel-1 data are added in the training process. Blending both Setninel-1 and Sentinel-2 data causes a remarkable classification improvement ranging from 3.6 to 8.7 percentage points over shrubs, forest, and pasture with trees, which are the most confusing classes in the optical domain as demonstrated by a spectral separability analysis. The derived agreement map is built upon combining per pixel classifications, their probabilities, and the Spanish LPIS. This map can be exploited into the decision-making chain for subsidies payment to cope with the 2020+ European Common Agricultural Policy (CAP). Full article
(This article belongs to the Special Issue Remote Sensing of Agricultural Monitoring)
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