scholarly journals Automatic Mapping of Rice Growth Stages Using the Integration of SENTINEL-2, MOD13Q1, and SENTINEL-1

2020 ◽  
Vol 12 (21) ◽  
pp. 3613 ◽  
Author(s):  
Fadhlullah Ramadhani ◽  
Reddy Pullanagari ◽  
Gabor Kereszturi ◽  
Jonathan Procter
Keyword(s):  
Remote Sensing ◽  
Food Security ◽  
Prediction Models ◽  
Spatial Information ◽  
Rice Production ◽  
Growth Stages ◽  
Time Lags ◽  
Cropping Pattern ◽  
Rice Growth ◽  
Sentinel 2

Rice (Oryza sativa L.) is a staple food crop for more than half of the world’s population. Rice production is facing a myriad of problems, including water shortage, climate, and land-use change. Accurate maps of rice growth stages are critical for monitoring rice production and assessing its impacts on national and global food security. Rice growth stages are typically monitored by coarse-resolution satellite imagery. However, it is difficult to accurately map due to the occurrence of mixed pixels in fragmented and patchy rice fields, as well as cloud cover, particularly in tropical countries. To solve these problems, we developed an automated mapping workflow to produce near real-time multi-temporal maps of rice growth stages at a 10-m spatial resolution using multisource remote sensing data (Sentinel-2, MOD13Q1, and Sentinel-1). This study was investigated between 1 June and 29 September 2018 in two (wet and dry) areas of Java Island in Indonesia. First, we built prediction models based on Sentinel-2, and fusion of MOD13Q1/Sentinel-1 using the ground truth information. Second, we applied the prediction models on all images in area and time and separation between the non-rice planting class and rice planting class over the cropping pattern. Moreover, the model’s consistency on the multitemporal map with a 5–30-day lag was investigated. The result indicates that the Sentinel-2 based model classification gives a high overall accuracy of 90.6% and the fusion model MOD13Q1/Sentinel-1 shows 78.3%. The performance of multitemporal maps was consistent between time lags with an accuracy of 83.27–90.39% for Sentinel-2 and 84.15% for the integration of Sentinel-2/MOD13Q1/Sentinel-1. The results from this study show that it is possible to integrate multisource remote sensing for regular monitoring of rice phenology, thereby generating spatial information to support local-, national-, and regional-scale food security applications.

scholarly journals Mapping a Cloud-Free Rice Growth Stages Using the Integration of PROBA-V and Sentinel-1 and Its Temporal Correlation with Sub-District Statistics

2021 ◽  
Vol 13 (8) ◽  
pp. 1498
Author(s):  
Fadhlullah Ramadhani ◽  
Reddy Pullanagari ◽  
Gabor Kereszturi ◽  
Jonathan Procter
Keyword(s):  
Remote Sensing ◽  
Growth Stage ◽  
Temporal Correlation ◽  
Lag Time ◽  
Rice Production ◽  
Support Vector ◽  
Growth Stages ◽  
Accuracy Measurement ◽  
Local Statistics ◽  
Rice Growth

Monitoring rice production is essential for securing food security against climate change threats, such as drought and flood events becoming more intense and frequent. The current practice to survey an area of rice production manually and in near real-time is expensive and involves a high workload for local statisticians. Remote sensing technology with satellite-based sensors has grown in popularity in recent decades as an alternative approach, reducing the cost and time required for spatial analysis over a wide area. However, cloud-free pixels of optical imagery are required to produce accurate outputs for agriculture applications. Thus, in this study, we propose an integration of optical (PROBA-V) and radar (Sentinel-1) imagery for temporal mapping of rice growth stages, including bare land, vegetative, reproductive, and ripening stages. We have built classification models for both sensors and combined them into 12-day periodical rice growth-stage maps from January 2017 to September 2018 at the sub-district level over Java Island, the top rice production area in Indonesia. The accuracy measurement was based on the test dataset and the predicted cross-correlated with monthly local statistics. The overall accuracy of the rice growth-stage model of PROBA-V was 83.87%, and the Sentinel-1 model was 71.74% with the Support Vector Machine classifier. The temporal maps were comparable with local statistics, with an average correlation between the vegetative area (remote sensing) and harvested area (local statistics) is 0.50, and lag time 89.5 days (n = 91). This result was similar to local statistics data, which correlate planting and the harvested area at 0.61, and the lag time as 90.4 days, respectively. Moreover, the cross-correlation between the predicted rice growth stage was also consistent with rice development in the area (r > 0.52, p < 0.01). This novel method is straightforward, easy to replicate and apply to other areas, and can be scaled up to the national and regional level to be used by stakeholders to support improved agricultural policies for sustainable rice production.

scholarly journals Deep learning para la clasificación de usos de suelo agrícola con Sentinel-2

2020 ◽  
pp. 35
Author(s):  
M. Campos-Taberner ◽  
F.J. García-Haro ◽  
B. Martínez ◽  
M.A. Gilabert
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Near Infrared ◽  
Spatial Information ◽  
Learning Algorithm ◽  
Support Vector ◽  
Sensing Applications ◽  
Learning Techniques ◽  
Remote Sensing Applications ◽  
Sentinel 2

<p class="p1">The use of deep learning techniques for remote sensing applications has recently increased. These algorithms have proven to be successful in estimation of parameters and classification of images. However, little effort has been made to make them understandable, leading to their implementation as “black boxes”. This work aims to evaluate the performance and clarify the operation of a deep learning algorithm, based on a bi-directional recurrent network of long short-term memory (2-BiLSTM). The land use classification in the Valencian Community based on Sentinel-2 image time series in the framework of the common agricultural policy (CAP) is used as an example. It is verified that the accuracy of the deep learning techniques is superior (98.6 % overall success) to that other algorithms such as decision trees (DT), k-nearest neighbors (k-NN), neural networks (NN), support vector machines (SVM) and random forests (RF). The performance of the classifier has been studied as a function of time and of the predictors used. It is concluded that, in the study area, the most relevant information used by the network in the classification are the images corresponding to summer and the spectral and spatial information derived from the red and near infrared bands. These results open the door to new studies in the field of the explainable deep learning in remote sensing applications.</p>

scholarly journals PANSHARPENING ON THE NARROW VNIR AND SWIR SPECTRAL BANDS OF SENTINEL-2

2016 ◽  
Vol XLI-B7 ◽  
pp. 723-730 ◽  
Author(s):  
A. D. Vaiopoulos ◽  
K. Karantzalos
Keyword(s):  
Remote Sensing ◽  
Quantitative Assessment ◽  
Spatial Information ◽  
State Of The Art ◽  
Qualitative Evaluation ◽  
Evaluation Framework ◽  
Image Similarity ◽  
Spectral Bands ◽  
Quantitative Image ◽  
Sentinel 2

In this paper results from the evaluation of several state-of-the-art pansharpening techniques are presented for the VNIR and SWIR bands of Sentinel-2. A procedure for the pansharpening is also proposed which aims at respecting the closest spectral similarities between the higher and lower resolution bands. The evaluation included 21 different fusion algorithms and three evaluation frameworks based both on standard quantitative image similarity indexes and qualitative evaluation from remote sensing experts. The overall analysis of the evaluation results indicated that remote sensing experts disagreed with the outcomes and method ranking from the quantitative assessment. The employed image quality similarity indexes and quantitative evaluation framework based on both high and reduced resolution data from the literature didn’t manage to highlight/evaluate mainly the spatial information that was injected to the lower resolution images. Regarding the SWIR bands none of the methods managed to deliver significantly better results than a standard bicubic interpolation on the original low resolution bands.

scholarly journals INTEGRATION OF HUMAN PARTICIPATORY SENSING AND ARCHIVES OF REMOTE SENSING OBSERVATIONS FOR FIELD LEVEL CROP PHENOLOGY ESTIMATION

2018 ◽  
Vol XLII-4 ◽  
pp. 547-550
Author(s):  
S. A. Sawant ◽  
J. D. Mohite ◽  
S. Pappula
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Vegetation Indices ◽  
Crop Growth ◽  
Growth Stages ◽  
Growth Monitoring ◽  
Specific Information ◽  
Crop Phenology ◽  
Field Level ◽  
Sentinel 2

<p><strong>Abstract.</strong> The rise in global population has increased food and water demand thereby causing excessive pressure on existing resources. In developing countries with fragmented land holdings there exists constant pressure on available water and land resources. Obtaining field scale crop specific information is challenging task. Advent of open freely available multi-temporal remote sensing observations with improved radiometric resolution the possibilities for near real / real time applications has increased. In this study and an attempt has been made to establish operational model for field level crop growth monitoring using integrated approach of crowd sourcing and time series of remote sensing observations. The time series of Sentinel 2 (A and B) satellite has been used to estimate crop growth related components such as vegetation indices and crop growth stage and crop phenology. In initial stage high valued cereal crop Wheat has been selected. The field level information (i.e. 108 Wheat fields) collected using mobile based agro-advisory platform mKRISHI&amp;reg; has been used to extract time series of Sentinel 2 observations (44 scenes for year 2016 and 2018). The moving average has been used for filling gaps in the time series of vegetation indices. The BFAST and GreenBrown package in R were used for detecting breaks in vegetation index time series and estimating crop growth stages. Analysis shows that the estimated crop phenology parameters were in better agreement with the field observations. In future more crops from different agro-climatic conditions will be considered for providing field level crop management advisory.</p>

scholarly journals High Accuracy Geochemical Map Generation Method by a Spatial Autocorrelation-Based Mixture Interpolation Using Remote Sensing Data

2020 ◽  
Vol 12 (12) ◽  
pp. 1991
Author(s):  
Chenhui Huang ◽  
Akinobu Shibuya
Keyword(s):  
Remote Sensing ◽  
Spatial Autocorrelation ◽  
Data Augmentation ◽  
Prediction Models ◽  
Spatial Information ◽  
Thermal Emission ◽  
Remote Sensing Data ◽  
Regression Problem ◽  
Sensing Data ◽  
Multivariate Prediction

Generating a high-resolution whole-pixel geochemical contents map from a map with sparse distribution is a regression problem. Currently, multivariate prediction models like machine learning (ML) are constructed to raise the geoscience mapping resolution. Methods coupling the spatial autocorrelation into the ML model have been proposed for raising ML prediction accuracy. Previously proposed methods are needed for complicated modification in ML models. In this research, we propose a new algorithm called spatial autocorrelation-based mixture interpolation (SABAMIN), with which it is easier to merge spatial autocorrelation into a ML model only using a data augmentation strategy. To test the feasibility of this concept, remote sensing data including those from the advanced spaceborne thermal emission and reflection radiometer (ASTER), digital elevation model (DEM), and geophysics (geomagnetic) data were used for the feasibility study, along with copper geochemical and copper mine data from Arizona, USA. We explained why spatial information can be coupled into an ML model only by data augmentation, and introduced how to operate data augmentation in our case. Four tests—(i) cross-validation of measured data, (ii) the blind test, (iii) the temporal stability test, and (iv) the predictor importance test—were conducted to evaluate the model. As the results, the model’s accuracy was improved compared with a traditional ML model, and the reliability of the algorithm was confirmed. In summary, combining the univariate interpolation method with multivariate prediction with data augmentation proved effective for geological studies.

scholarly journals Evaluation of Grass Quality under Different Soil Management Scenarios Using Remote Sensing Techniques

2019 ◽  
Vol 11 (15) ◽  
pp. 1835 ◽  
Author(s):  
Mohammad Sadegh Askari ◽  
Timothy McCarthy ◽  
Aidan Magee ◽  
Darren J. Murphy
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Quality Indicators ◽  
Prediction Models ◽  
Unmanned Aircraft ◽  
Multispectral Imagery ◽  
European Climate ◽  
Grass Biomass ◽  
Remote Sensing Techniques ◽  
Sentinel 2

Hyperspectral and multispectral imagery have been demonstrated to have a considerable potential for near real-time monitoring and mapping of grass quality indicators. The objective of this study was to evaluate the efficiency of remote sensing techniques for quantification of aboveground grass biomass (BM) and crude protein (CP) in a temperate European climate such as Ireland. The experiment was conducted on 64 plots and 53 paddocks with varying quantities of nitrogen applied. Hyperspectral imagery (HSI) and multispectral imagery (MSI) were analyzed to develop the prediction models. The MSI data used in this study were captured using an unmanned aircraft vehicle (UAV) and the satellite Sentinel-2, while the HSI data were obtained using a handheld hyperspectral camera. The prediction models were developed using partial least squares regression (PLSR) and stepwise multi-linear regression (MLR). Eventually, the spatial distribution of grass biomass over plots and paddocks was mapped to assess the within-field variability of grass quality metrics. An excellent accuracy was achieved for the prediction of BM and CP using HSI (RPD > 2.5 and R2 > 0.8), and a good accuracy was obtained via MSI-UAV (2 < RPD < 2.5 and R2 > 0.7) for the grass quality indicators. The accuracy of the models calculated using MSI-Sentinel-2 was reasonable for BM prediction and insufficient for CP estimation. The red-edge range of the wavelengths showed the maximum impact on the predictability of grass BM, and the NIR range had the greatest influence on the estimation of grass CP. Both the PLSR and MLR techniques were found to be sufficiently robust for spectral modelling of aboveground BM and CP. The PLSR yielded a slightly better model than MLR. This study suggested that remote sensing techniques can be used as a rapid and reliable approach for near real-time quantitative assessment of fresh grass quality under a temperate European climate.

scholarly journals Exploring the Suitability of UAS-Based Multispectral Images for Estimating Soil Organic Carbon: Comparison with Proximal Soil Sensing and Spaceborne Imagery

2021 ◽  
Vol 13 (2) ◽  
pp. 308
Author(s):  
James Kobina Mensah Biney ◽  
Mohammadmehdi Saberioon ◽  
Luboš Borůvka ◽  
Jakub Houška ◽  
Radim Vašát ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Prediction Models ◽  
Unmanned Aircraft ◽  
Support Vector ◽  
Spectral Indices ◽  
Entire Study ◽  
Proximal Soil Sensing ◽  
Sentinel 2

Soil organic carbon (SOC) is a variable of vital environmental significance in terms of soil quality and function, global food security, and climate change mitigation. Estimation of its content and prediction accuracy on a broader scale remain crucial. Although, spectroscopy under proximal sensing remains one of the best approaches to accurately predict SOC, however, spectroscopy limitation to estimate SOC on a larger spatial scale remains a concern. Therefore, for an efficient quantification of SOC content, faster and less costly techniques are needed, recent studies have suggested the use of remote sensing approaches. The primary aim of this research was to evaluate and compare the capabilities of small Unmanned Aircraft Systems (UAS) for monitoring and estimation of SOC with those obtained from spaceborne (Sentinel-2) and proximal soil sensing (field spectroscopy measurements) on an agricultural field low in SOC content. Nine calculated spectral indices were added to the remote sensing approaches (UAS and Sentinel-2) to enhance their predictive accuracy. Modeling was carried out using various bands/wavelength (UAS (6), Sentinel-2 (9)) and the calculated spectral indices were used as independent variables to generate soil prediction models using five-fold cross-validation built using random forest (RF) and support vector machine regression (SVMR). The correlation regarding SOC and the selected indices and bands/wavelengths was determined prior to the prediction. Our results revealed that the selected spectral indices slightly influenced the output of UAS compared to Sentinel-2 dataset as the latter had only one index correlated with SOC. For prediction, the models built on UAS data had a better accuracy with RF than the two other data used. However, using SVMR, the field spectral prediction models achieved a better overall result for the entire study (log(1/R), RPD = 1.40; R2CV = 0.48; RPIQ = 1.65; RMSEPCV = 0.24), followed by UAS and then Sentinel-2, respectively. This study has shown that UAS imagery can be exploited efficiently using spectral indices.

scholarly journals Soil Organic Carbon Mapping from Remote Sensing: The Effect of Crop Residues

2020 ◽  
Vol 12 (12) ◽  
pp. 1913 ◽  
Author(s):  
Klara Dvorakova ◽  
Pu Shi ◽  
Quentin Limbourg ◽  
Bas van Wesemael
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Cross Validation ◽  
Crop Residues ◽  
Prediction Models ◽  
Partial Least Square ◽  
Least Square ◽  
Local Soil ◽  
Sentinel 2

Since the onset of agriculture, soils have lost their organic carbon to such an extent that the soil functions of many croplands are threatened. Hence, there is a strong demand for mapping and monitoring critical soil properties and in particular soil organic carbon (SOC). Pilot studies have demonstrated the potential for remote sensing techniques for SOC mapping in croplands. It has, however, been shown that the assessment of SOC may be hampered by the condition of the soil surface. While growing vegetation can be readily detected by means of the well-known Normalized Difference Vegetation Index (NDVI), the distinction between bare soil and crop residues is expressed in the shortwave infrared region (SWIR), which is only covered by two broad bands in Landsat or Sentinel-2 imagery. Here we tested the effect of thresholds for the Cellulose Absorption Index (CAI), on the performance of SOC prediction models for cropland soils. Airborne Prism Experiment (APEX) hyperspectral images covering an area of 240 km2 in the Belgian Loam Belt were used together with a local soil dataset. We used the partial least square regression (PLSR) model to estimate the SOC content based on 104 georeferenced calibration samples (NDVI < 0.26), firstly without setting a CAI threshold, and obtained a satisfactory result (coefficient of determination (R2) = 0.49, Ratio of Performance to Deviation (RPD) = 1.4 and Root Mean Square Error (RMSE) = 2.13 g kgC−1 for cross-validation). However, a cross comparison of the estimated SOC values to grid-based measurements of SOC content within three fields revealed a systematic overestimation for fields with high residue cover. We then tested different CAI thresholds in order to mask pixels with high residue cover. The best model was obtained for a CAI threshold of 0.75 (R2 = 0.59, RPD = 1.5 and RMSE = 1.75 g kgC−1 for cross-validation). These results reveal that the purity of the pixels needs to be assessed aforehand in order to produce reliable SOC maps. The Normalized Burn Ratio (NBR2) index based on the SWIR bands of the MSI Sentinel 2 sensor extracted from images collected nine days before the APEX flight campaign correlates well with the CAI index of the APEX imagery. However, the NBR2 index calculated from Sentinel 2 images under moist conditions is poorly correlated with residue cover. This can be explained by the sensitivity of the NBR2 index to both soil moisture and residues.

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