1,721,040 research outputs found
Sentinel-2 estimation of CNC and LAI in rice cropping system through hybrid approach modelling
Full text linkEarth observation techniques represent a reliable and faster alternative to in-situ measurements by providing spatio-temporal information on crop status. In this framework, a study was conducted to assess the performance of hybrid approaches, either standard (HYB) or exploiting an active learning optimisation strategy (HYB-AL), to estimate leaf area index (LAI) and canopy nitrogen content (CNC) from Sentinel–2 (S2) data, in rice crops. To achieve this, the PROSAILPRO Radiative Transfer Model (RTM) was tested. Results demonstrate that a wide range of rice spectra, simulated according to realistic crop parameters, are reliable when appropriate field background conditions are considered. Simulations were used to train a Gaussian Process Regression (GPR) algorithm. Both cross-validation and validation results showed that HYB-AL approach resulted the best performing retrieval schema. LAI estimation achieved good performance (R2=0.86; RMSE=0.54) and resulted very promising for model application in operational monitoring systems. CNC estimations showed moderate performance (R2=0.63; RMSE=0.89) due to a saturation behaviour limiting the retrieval accuracy for moderate/high CNC values, approximately above 4 [g m−2]. S2 maps of LAI and CNC provided spatio-temporal information in agreement with crop growth, nutritional status and agro-practices applied to the study area, resulting in an important contribution to precision farming applications
Impact of deforestation on local precipitation patterns over the Da River basin, Vietnam
No full textChange in land cover, e.g. from forest to bare soil, might severely impact the hydrological cycle at the river basin scale by altering the balance between rainfall and evaporation, ultimately affecting streamflow dynamics. These changes generally occur over decades, but they might be much more rapid in developing countries, where economic growth and growing population may cause abrupt changes in landscape and ecosystem. Detecting, analysing and modelling these changes is an essential step to design mitigation strategies and adaptation plans, balancing economic development and ecosystem protection. In this work we investigate the impact of land cover changes on the water cycle in the Da River basin, Vietnam. More precisely, the objective is to evaluate the interlink between deforestation and precipitation. The case study is particularly interesting because Vietnam is one of the world fastest growing economies and natural resources have been considerably exploited to support after-war development. Vietnam has the second highest rate of deforestation of primary forests in the world, second to only Nigeria (FAO 2005), with associated problems like abrupt change in run-off, erosion, sediment transport and flash floods. We performed land cover evaluation by combining literature information and Remote Sensing techniques, using Landsat images. We then analysed time series of precipitation observed on the period 1960-2011 in several stations located in the catchment area. We used multiple trend detection techniques, both state-of-the-art (e.g., Linear regression and Mann-Kendall) and novel trend detection techniques (Moving Average on Shifting Horizon), to investigate trends in seasonal pattern of precipitation. Results suggest that deforestation may induce a negative trend in the precipitation volume. The effect is mainly recognizable at the beginning and at the end of the monsoon season, when the local mechanisms of precipitation formation prevail over the large scale ones
Self-Supervised Convolutional Neural Network Learning in a Hybrid Approach Framework to Estimate Chlorophyll and Nitrogen Content of Maize from Hyperspectral Images
No full textThe new generation of available (i.e., PRISMA, ENMAP, DESIS) and future (i.e., ESA-CHIME, NASA-SBG) spaceborne hyperspectral missions provide unprecedented data for environmental and agricultural monitoring, such as crop trait assessment. This paper focuses on retrieving two crop traits, specifically Chlorophyll and Nitrogen content at the canopy level (CCC and CNC), starting from hyperspectral images acquired during the CHIME-RCS project, exploiting a self-supervised learning (SSL) technique. SSL is a machine learning paradigm that leverages unlabeled data to generate valuable representations for downstream tasks, bridging the gap between unsupervised and supervised learning. The proposed method comprises pre-training and fine-tuning procedures: in the first stage, a de-noising Convolutional Autoencoder is trained using pairs of noisy and clean CHIME-like images; the pre-trained Encoder network is utilized as-is or fine-tuned in the second stage. The paper demonstrates the applicability of this technique in hybrid approach methods that combine Radiative Transfer Modelling (RTM) and Machine Learning Regression Algorithm (MLRA) to set up a retrieval schema able to estimate crop traits from new generation space-born hyperspectral data. The results showcase excellent prediction accuracy for estimating CCC (R2 = 0.8318; RMSE = 0.2490) and CNC (R2 = 0.9186; RMSE = 0.7908) for maize crops from CHIME-like images without requiring further ground data calibration
In-season and dynamic crop mapping using 3D convolution neural networks and sentinel-2 time series
No full textAn accurate, frequently updated, automatic and reproducible mapping procedure to identify seasonal cultivated crops is a prerequisite for many crop monitoring activities. Deep learning was demonstrated to be an effective mapping approach already successfully applied to decametric resolution satellite images (like Sentinel-2 data) to produce yearly crop maps. In this framework, algorithm training is performed with ground truth typically consisting of spatially explicit information available after the end of the season (e.g. yearly crop maps and/or farmer declaration for subsidies at parcel level); however, such data (i) does not allow performing in-season prediction, and (ii) does not provide temporal details fundamental to describe a dynamic crop succession and/or to understand crop management (i.e. planting and harvesting). In this paper we present a Deep Neural Network-based approach capable of generating (i) a crop map of the current season at a specific point in time (“In season mapping” conventionally at the end of the current year), along with (ii) all intermediate maps during the season able to describe in near real-time the evolution of crop presence (“Dynamic-mapping” at the temporal granularity of satellite imagery revisiting, e.g., 5 days for Sentinel-2 data). This approach adopts a smart training procedure of a Deep Neural model by exploiting historical satellite data and ground truth. We introduce a method to automatically generate “short-term” ground truth maps (i.e. 5 days reference) starting from the “long-term” ones (i.e. available yearly static reference) and characterizing temporally the different crop presence by performing a phenological analysis of historical time series. The model was trained and validated in Lombardy (North of Italy) exploiting multi-annual authoritative crop maps from 2016 to 2019. Validation was performed both in time (same areas used for training in a different year) and space (different location) for the year 2019. The quantitative error metrics calculation and Spatio-temporal analysis clearly demonstrate that the model can predict in-season crop presence with a generalization capacity over the long-term (yearly maps: OA > 70% and Kappa > 0.64%) and that the short-term predictions (5 days maps) are coherent with the reference information from expert knowledge (local crop calendars). The model can produce dynamically along the season short-term maps with a medium-high crop-specific User Accuracy at the maximum green-up phase (UA > 53% up to 95%). These products are of extreme interest for final users providing information at the peak of plant development that dynamically changes according to the considered crop, the specific location and the investigated season. These results demonstrate that it is possible to produce a crop map early in the season and extract useful additional information such as crop intensity (e.g. double crops presence) and crop dynamics related to different sowing dates
Bioenergy and ecosystem services trade-offs and synergies in marginal agricultural lands: A remote-sensing-based assessment method
No full textGrowing non-food crops in marginal lands has been proposed as a solution to avoid land competition with food production. Mapping marginal agricultural lands is therefore fundamental for the sustainable development of rural landscapes. This study proposes a method based on remote sensing data to identify marginal agricultural lands for the production of wood biomass, and analyse potential trade-offs and synergies between the new wood crops, food production, and Ecosystem Services (ES) provided by vegetation. The province of Rovigo (northern Italy) was chosen as a representative case study. Three classes of marginal agricultural lands were mapped through the use of the Soil Adjusted Vegetation Index (SAVI): i) abandoned or unused agricultural lands, ii) potentially poorly or non-managed croplands, and iii) potentially low productivity croplands. Results showed that marginal agricultural lands cover 1.7% of the agricultural areas of the province, and approximately 13,642 MWh yr(-1) of Second-Generation (2G) bioenergy can be produced in marginal agricultural areas while enhancing ES provided by vegetation, and avoiding any trade-off with food production. Since this energy potential covers just 8.4% of the total potential authorized in the province, the enhancement of ES could provide a suitable argument to support the conversion of marginal agricultural lands and increase the multifunctionality of the agricultural landscape
Enhancing crop segmentation in satellite image time-series with transformer networks
No full textRecent studies have shown that Convolutional Neural Networks (CNNs) achieve impressive results in crop segmentation of Satellite Image Time Series (SITS). However, the emergence of transformer networks in various vision tasks raises the question of whether they can outperform CNNs in this task as well. This paper presents a revised version of the Transformer-based Swin UNETR model, specifically adapted for crop segmentation of SITS. The proposed model demonstrates significant advancements, achieving a validation accuracy of 96.14% and a test accuracy of 95.26% on the Munich dataset, surpassing the previous best results of 93.55% for validation and 92.94% for the test. Additionally, the model\u27s performance on the Lombardia dataset is comparable to UNet3D and superior to FPN and DeepLabV3. Experiments of this study indicate that the model will likely achieve comparable or superior accuracy to CNNs while requiring significantly less training time. These findings highlight the potential of transformer-based architectures for crop segmentation in SITS, opening new avenues for remote sensing applications
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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