scholarly journals Application of GIS and RS in real time crop monitoring and yield forecasting: a case study of cotton fields in low and high productive farmlands

2021 ◽  
Vol 227 ◽  
pp. 03001
Author(s):  
Zokhid Mamatkulov ◽  
Eshkobil Safarov ◽  
Rustam Oymatov ◽  
Ilhom Abdurahmanov ◽  
Maksud Rajapbaev
Keyword(s):  
Real Time ◽  
Vegetation Index ◽  
Agricultural Sector ◽  
Vegetation Development ◽  
Ground Truth Data ◽  
Crop Monitoring ◽  
Multi Temporal ◽  
Gis And Rs ◽  
Yield Forecasting ◽  
Cotton Fields

Badland reclamation and low productive farmlands always have been one of the most detrimental effects on the national economy, typically in agricultural sector of Uzbekistan. Nonetheless, such kind of lands has been used extensively for major crops like cotton and winter wheat. However, it is difficult to assessing real productivity of them. Advanced technologies as GIS and RS are vital tool for geospatially analysing and making decisions on this type of fields. This research was carried out for real-time crop monitoring and yield forecasting in case of low productive (3.5 ha) and high productive (8.3 ha) cotton areas of Jarkurgan district (Surkhandarya region, Uzbekistan) based on geospatial analyses of multi-temporal satellite images, condition of groundwater, soil salinity, and ground truth data. For monitoring vegetation phenology of cotton and forecasting its harvest, False Colour, NDVI (Normalized Difference Vegetation Index) and SI (Salinity Index) analyses of areas were carried out by using 6 temporal windows of multi-temporal Sentinel 2 from April through August 2019. Besides, groundwater condition data which was obtained from observation wells these located in massives consists of both cotton fields was analysed by IDW (Inverse Distance Weighting) interpolation algorithm method to determine groundwater’s effect to vegetation development and yield.

scholarly journals Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data

2020 ◽  
Vol 12 (9) ◽  
pp. 1456 ◽  
Author(s):  
Hassan Bazzi ◽  
Nicolas Baghdadi ◽  
Ibrahim Fayad ◽  
Mehrez Zribi ◽  
Hatem Belhouchette ◽  
...  
Keyword(s):  
Real Time ◽  
Vegetation Index ◽  
Agricultural Sector ◽  
Soil Surface ◽  
Growth Cycle ◽  
Vegetation Development ◽  
Rainfall Events ◽  
Optical Images ◽  
Soil Surface Roughness ◽  
Study Sites

In the context of monitoring and assessment of water consumption in the agricultural sector, the objective of this study is to build an operational approach capable of detecting irrigation events at plot scale in a near real-time scenario using Sentinel-1 (S1) data. The proposed approach is a decision tree-based method relying on the change detection in the S1 backscattering coefficients at plot scale. First, the behavior of the S1 backscattering coefficients following irrigation events has been analyzed at plot scale over three study sites located in Montpellier (southeast France), Tarbes (southwest France), and Catalonia (northeast Spain). To eliminate the uncertainty between rainfall and irrigation, the S1 synthetic aperture radar (SAR) signal and the soil moisture estimations at grid scale (10 km × 10 km) have been used. Then, a tree-like approach has been constructed to detect irrigation events at each S1 date considering additional filters to reduce ambiguities due to vegetation development linked to the growth cycle of different crops types as well as the soil surface roughness. To enhance the detection of irrigation events, a filter using the normalized differential vegetation index (NDVI) obtained from Sentinel-2 optical images has been proposed. Over the three study sites, the proposed method was applied on all possible S1 acquisitions in ascending and descending modes. The results show that 84.8% of the irrigation events occurring over agricultural plots in Montpellier have been correctly detected using the proposed method. Over the Catalonian site, the use of the ascending and descending SAR acquisition modes shows that 90.2% of the non-irrigated plots encountered no detected irrigation events whereas 72.4% of the irrigated plots had one and more detected irrigation events. Results over Catalonia also show that the proposed method allows the discrimination between irrigated and non-irrigated plots with an overall accuracy of 85.9%. In Tarbes, the analysis shows that irrigation events could still be detected even in the presence of abundant rainfall events during the summer season where two and more irrigation events have been detected for 90% of the irrigated plots. The novelty of the proposed method resides in building an effective unsupervised tool for near real-time detection of irrigation events at plot scale independent of the studied geographical context.

scholarly journals Sentinel 1A SAR Backscattering Signature of Maize and Cotton Crops

2017 ◽  
Vol 104 (.1-.4) ◽  
Author(s):  
Kumaraperumal R ◽  
◽  
Shama M ◽  
Balaji Kannan ◽  
Ragunath K P ◽  
...  
Keyword(s):  
Tamil Nadu ◽  
Ground Truth ◽  
Backscattering Coefficient ◽  
Ground Truth Data ◽  
Crop Monitoring ◽  
Multi Temporal ◽  
The Mean ◽  
Sar Data ◽  
Remote Sensing Techniques ◽  
Crop Discrimination

Crop discrimination is a key issue for agricultural monitoring using remote sensing techniques. Synthetic Aperture Radar (SAR) data are advantageous for crop monitoring and classification because of their all-weather imaging capabilities. The multi-temporal Sentinel 1A SAR data was acquired from 08th August, 2015 to 23rd January, 2016 at 12 days interval covering the extent of Perambalur district of Tamil Nadu. Both the Vertical - Vertical (VV) and Vertical-Horizontal (VH) polarized data are compared. The ground truth data collection was performed for cotton and maize during the vegetative, flowering and harvesting stages. The temporal backscattering coefficient (σ0 ) for cotton and maize are extracted using the training datasets. The mean backscattering values for cotton during the entire cropping period ranges from -10.58 dB to -6.28 dB and -20.59 dB to -14.53 dB for VV and VH polarized data respectively, and for maize it ranges from -11.08 dB to -7.07 dB and -19.85 dB to -14.14 dB for VV and VH polarized data respectively.

Real-Time Crop Monitoring in Agriculture

2021 ◽  
pp. 230-242
Author(s):  
Sumit Pahuja ◽  
Garima Singh ◽  
Prabhjot Singh
Keyword(s):  
Soil Moisture ◽  
Air Quality ◽  
Plant Growth ◽  
Real Time ◽  
Agricultural Sector ◽  
Weather Forecasts ◽  
Crop Monitoring ◽  
Farmer Suicides ◽  
Increasing Demand ◽  
Indigenous Methods

In many countries like India, farming is done using indigenous methods. Because of lack of proper knowledge in our farmers, the state of the agricultural sector becomes even more critical. Since the farming methodologies rely mostly on weather forecasts and predictions which might not be foolproof, most often the farmers incur huge losses leading to debts and mass farmer suicides. Adequate soil moisture, soil quality, air quality, and proper irrigation play a major role in the yield of crops, and hence, such factors cannot be overlooked. A major concern now is the exploding population due to which the agricultural supplies are not meeting the ever-increasing demand. The world's population is expected to cross nine billion marks by 2050 due to which the agricultural supply should increase at least by 70% to meet the requirement. To achieve this, it's necessary to monitor the plant growth at all stages starting from sowing until cultivation.

scholarly journals Monitoring of Spectral Signatures of Maize Crop using Temporal SAR and Optical Remote Sensing data

2021 ◽  
Vol 12 (6) ◽  
pp. 745-750
Author(s):  
D. Anil Kumar ◽  
◽  
P. Srikanth ◽  
T. L. Neelima ◽  
M. Uma Devi ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Plant Density ◽  
Optical Data ◽  
Maturity Stage ◽  
Optical Remote Sensing ◽  
Maize Crop ◽  
Ground Truth Data ◽  
Backscatter Intensity ◽  
Multi Temporal ◽  
Sentinel 2A

A study was carried out using the temporal Sentinel-1B microwave data (June to November at 12 days interval) and Sentinel-2A/2B optical data (June to November) to discriminate the maize crop from other competing crops rice and cotton in Siddipet district, Telangana state, India during kharif, 2019 (June to November). The study utilized the data from multiple sources such as Multi-temporal VH backscatter intensity from Sentinel-1B SAR and NDVI values from Sentinel-2A/2B in combination with field data to discriminate the maize crop. Synchronous to satellite pass, ground truth data on crop parameters viz., crop stage, crop vigour, biomass, plant height, plant density, soil moisture, LAI and chlorophyll content were collected. Multi-temporal VH backscatter intensity and Normalized Difference Vegetation Index (NDVI) data were used to characterize backscatter and greenness behaviour of the maize crop. The backscatter intensity (dB) for maize crop ranged from -21.83 (the lowest backscatter values) at planting to -12.52 (the highest backscatter values) at peak growth stage. The NDVI values during vegetative and reproductive stages (August and September) were >0.6 and during senescence to harvesting the values were less than or equal to 0.52. The increase in backscatter intensity values from initial vegetative stage to peak stage was due to increased volume scattering of the maize crop canopy and a continuous decline in backscatter intensity values of VH band at maturity stage, was due to decrease in greenness and moisture content in leaves of the maize crop helped in maize crop discrimination from other dominant kharif crops in the study area.

scholarly journals Mapping the Groundwater Level and Soil Moisture of a Montane Peat Bog Using UAV Monitoring and Machine Learning

2021 ◽  
Vol 13 (5) ◽  
pp. 907
Author(s):  
Theodora Lendzioch ◽  
Jakub Langhammer ◽  
Lukáš Vlček ◽  
Robert Minařík
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Spatial Data ◽  
Groundwater Level ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Sampling Strategy ◽  
Peat Bog ◽  
Strong Impact ◽  
Ground Truth Data

One of the best preconditions for the sufficient monitoring of peat bog ecosystems is the collection, processing, and analysis of unique spatial data to understand peat bog dynamics. Over two seasons, we sampled groundwater level (GWL) and soil moisture (SM) ground truth data at two diverse locations at the Rokytka Peat bog within the Sumava Mountains, Czechia. These data served as reference data and were modeled with a suite of potential variables derived from digital surface models (DSMs) and RGB, multispectral, and thermal orthoimages reflecting topomorphometry, vegetation, and surface temperature information generated from drone mapping. We used 34 predictors to feed the random forest (RF) algorithm. The predictor selection, hyperparameter tuning, and performance assessment were performed with the target-oriented leave-location-out (LLO) spatial cross-validation (CV) strategy combined with forward feature selection (FFS) to avoid overfitting and to predict on unknown locations. The spatial CV performance statistics showed low (R2 = 0.12) to high (R2 = 0.78) model predictions. The predictor importance was used for model interpretation, where temperature had strong impact on GWL and SM, and we found significant contributions of other predictors, such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Index (NDI), Enhanced Red-Green-Blue Vegetation Index (ERGBVE), Shape Index (SHP), Green Leaf Index (GLI), Brightness Index (BI), Coloration Index (CI), Redness Index (RI), Primary Colours Hue Index (HI), Overall Hue Index (HUE), SAGA Wetness Index (TWI), Plan Curvature (PlnCurv), Topographic Position Index (TPI), and Vector Ruggedness Measure (VRM). Additionally, we estimated the area of applicability (AOA) by presenting maps where the prediction model yielded high-quality results and where predictions were highly uncertain because machine learning (ML) models make predictions far beyond sampling locations without sampling data with no knowledge about these environments. The AOA method is well suited and unique for planning and decision-making about the best sampling strategy, most notably with limited data.

scholarly journals Annually Urban Fractional Vegetation Cover Dynamic Mapping in Hefei, China (1999–2018)

2021 ◽  
Vol 13 (11) ◽  
pp. 2126
Author(s):  
Yuliang Wang ◽  
Mingshi Li
Keyword(s):  
Standard Deviation ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Dynamic Range ◽  
Dynamic Change ◽  
Estimation Model ◽  
Sparse Vegetation ◽  
Fractional Vegetation Cover ◽  
Dynamic Mapping ◽  
Multi Temporal

Vegetation measures are crucial for assessing changes in the ecological environment. Fractional vegetation cover (FVC) provides information on the growth status, distribution characteristics, and structural changes of vegetation. An in-depth understanding of the dynamic changes in urban FVC contributes to the sustainable development of ecological civilization in the urbanization process. However, dynamic change detection of urban FVC using multi-temporal remote sensing images is a complex process and challenge. This paper proposed an improved FVC estimation model by fusing the optimized dynamic range vegetation index (ODRVI) model. The ODRVI model improved sensitivity to the water content, roughness degree, and soil type by minimizing the influence of bare soil in areas of sparse vegetation cover. The ODRVI model enhanced the stability of FVC estimation in the near-infrared (NIR) band in areas of dense and sparse vegetation cover through introducing the vegetation canopy vertical porosity (VCVP) model. The verification results confirmed that the proposed model had better performance than typical vegetation index (VI) models for multi-temporal Landsat images. The coefficient of determination (R2) between the ODRVI model and the FVC was 0.9572, which was 7.4% higher than the average R2 of other typical VI models. Moreover, the annual urban FVC dynamics were mapped using the proposed improved FVC estimation model in Hefei, China (1999–2018). The total area of all grades FVC decreased by 33.08% during the past 20 years in Hefei, China. The areas of the extremely low, low, and medium grades FVC exhibited apparent inter-annual fluctuations. The maximum standard deviation of the area change of the medium grade FVC was 13.35%. For other grades of FVC, the order of standard deviation of the change ratio was extremely low FVC > low FVC > medium-high FVC > high FVC. The dynamic mapping of FVC revealed the influence intensity and direction of the urban sprawl on vegetation coverage, which contributes to the strategic development of sustainable urban management plans.

scholarly journals Multi-Temporal Arable Land Monitoring in Arid Region of Northwest China Using a New Extraction Index

2021 ◽  
Vol 13 (9) ◽  
pp. 5274
Author(s):  
Xinyang Yu ◽  
Younggu Her ◽  
Xicun Zhu ◽  
Changhe Lu ◽  
Xuefei Li
Keyword(s):  
Arable Land ◽  
Ground Truth ◽  
Northwest China ◽  
Hexi Corridor ◽  
Ground Truth Data ◽  
Land Protection ◽  
Promising Tool ◽  
Study Results ◽  
Multi Temporal ◽  
The Mean

Development of a high-accuracy method to extract arable land using effective data sources is crucial to detect and monitor arable land dynamics, servicing land protection and sustainable development. In this study, a new arable land extraction index (ALEI) based on spectral analysis was proposed, examined by ground truth data, and then applied to the Hexi Corridor in northwest China. The arable land and its change patterns during 1990–2020 were extracted and identified using 40 Landsat TM/OLI images acquired in 1990, 2000, 2010, and 2020. The results demonstrated that the proposed method can distinguish arable land areas accurately, with the User’s (Producer’s) accuracy and overall accuracy (kappa coefficient) exceeding 0.90 (0.88) and 0.89 (0.87), respectively. The mean relative error calculated using field survey data obtained in 2012 and 2020 was 0.169 and 0.191, respectively, indicating the feasibility of the ALEI method in arable land extracting. The study found that arable land area in the Hexi Corridor was 13217.58 km2 in 2020, significantly increased by 25.33% compared to that in 1990. At 10-year intervals, the arable land experienced different change patterns. The study results indicate that ALEI index is a promising tool used to effectively extract arable land in the arid area.

A Node and Network Level Self-Recovering Distributed Wireless Sensor Architecture for Real-Time Crop Monitoring in Greenhouses

2011 ◽  
Vol 54 (4) ◽  
pp. 1521-1527
Author(s):  
G. M. Striemer ◽  
D. L. Story ◽  
A. Akoglu ◽  
M. Kacira
Keyword(s):  
Real Time ◽  
Wireless Sensor ◽  
Crop Monitoring

scholarly journals Usability in 4D AR: Visualising Multi-temporal Real-time Geo-data in Augmented Reality Environments

2015 ◽  
Vol 9 (4) ◽  
pp. 23 ◽  
Author(s):  
Bernd Resch ◽  
Andreas Wichmann ◽  
Nicolas Göll
Keyword(s):  
Augmented Reality ◽  
Real Time ◽  
Temporal Variations ◽  
Time Base ◽  
Future Research ◽  
Time Data ◽  
Orientation Data ◽  
Information Density ◽  
Real Time Data ◽  
Multi Temporal

Even though advantages of 3D visualisation of multi-temporal geo-data versus 2D approaches have been widely proven, the particular pertaining challenge of real-time visualisation of geo-data in mobile Digital Earth applications has not been thoroughly tackled so far. In the emerging field of Augmented Reality (AR), research needs comprise finding the optimal information density, the interplay between orientation data in the background and other information layers, using the appropriate graphical variables for display, or selecting real-time base data with adequate quality and suitable spatial accuracy. In this paper we present a concept for integrating real-time data into 4D (three spatial dimensions plus time) AR environments, i.e., data with “high” spatial and temporal variations. We focus on three research challenges: 1.) high-performance integration of real-time data into AR; 2.) usability design in terms of displaying spatio-temporal developments and the interaction with the application; and 3.) design considerations regarding reality vs. virtuality, visualisation complexity and information density. We validated our approach in a prototypical application and extracted several limitations and future research areas including natural feature recognition, the cross-connection of (oftentimes monolithic) AR interface developments and well-established cartographic principles, or fostering the understanding of the temporal context in dynamic 4D Augmented Reality environments.

scholarly journals Change Detection in Hyperspectral Images Using Recurrent 3D Fully Convolutional Networks

2018 ◽  
Vol 10 (11) ◽  
pp. 1827 ◽  
Author(s):  
Ahram Song ◽  
Jaewan Choi ◽  
Youkyung Han ◽  
Yongil Kim
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Spatial Information ◽  
Short Term Memory ◽  
Hyperspectral Images ◽  
Convolutional Network ◽  
Ground Truth Data ◽  
Fully Convolutional Network ◽  
Training Samples ◽  
Multi Temporal

Hyperspectral change detection (CD) can be effectively performed using deep-learning networks. Although these approaches require qualified training samples, it is difficult to obtain ground-truth data in the real world. Preserving spatial information during training is difficult due to structural limitations. To solve such problems, our study proposed a novel CD method for hyperspectral images (HSIs), including sample generation and a deep-learning network, called the recurrent three-dimensional (3D) fully convolutional network (Re3FCN), which merged the advantages of a 3D fully convolutional network (FCN) and a convolutional long short-term memory (ConvLSTM). Principal component analysis (PCA) and the spectral correlation angle (SCA) were used to generate training samples with high probabilities of being changed or unchanged. The strategy assisted in training fewer samples of representative feature expression. The Re3FCN was mainly comprised of spectral–spatial and temporal modules. Particularly, a spectral–spatial module with a 3D convolutional layer extracts the spectral–spatial features from the HSIs simultaneously, whilst a temporal module with ConvLSTM records and analyzes the multi-temporal HSI change information. The study first proposed a simple and effective method to generate samples for network training. This method can be applied effectively to cases with no training samples. Re3FCN can perform end-to-end detection for binary and multiple changes. Moreover, Re3FCN can receive multi-temporal HSIs directly as input without learning the characteristics of multiple changes. Finally, the network could extract joint spectral–spatial–temporal features and it preserved the spatial structure during the learning process through the fully convolutional structure. This study was the first to use a 3D FCN and a ConvLSTM for the remote-sensing CD. To demonstrate the effectiveness of the proposed CD method, we performed binary and multi-class CD experiments. Results revealed that the Re3FCN outperformed the other conventional methods, such as change vector analysis, iteratively reweighted multivariate alteration detection, PCA-SCA, FCN, and the combination of 2D convolutional layers-fully connected LSTM.

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