COMBINED ANALYSIS OF RADARSAT-2 SAR AND SENTINEL-2 OPTICAL DATA FOR IMPROVED MONITORING OF TUBER INITIATION STAGE OF POTATO

Abstract. Tuber initiation and tuber bulking stages are critical part of various phenological phases for potato production. Tuber initiation covers the period from the formation of spherical rhizome ends, the flowering and the start of tuber bulking. In general, the tuberization spans from 3 to 5 weeks after emergence and ends with the row closer i.e. canopies in adjacent rows touch each other across the furrow. Hence, this rapid growth seeks critical agronomic management practices such as irrigation and fertilization. It majorly influences the growth of stems, foliar area, dry weight and number of tubers particularly at the phase of tuber initiation. During these phenological stages, potato crops are susceptible to the infestation of late blight diseases caused by Phytophthora infestans and largely affects the potato production. Thus identifying the crop risk using remote sensing approaches can provide an efficient potato growth monitoring framework. In the context of monitoring crop dynamics, quad-pol Synthetic Aperture Radar (SAR) data has proven to be effective due to its sensitivity towards dielectric and geometric properties. In addition to SAR data, optical remote sensing data derived vegetation information can provide an improved insight into crop growth when combined with SAR data. In this research, quad-pol RADARSAT-2 and Sentinel-2 optical data are analyzed to monitor potato tuberization phase over Bardhaman district in the state ofWest Bengal, which is one of the major potato growing regions in India. The proposed approach uses polarimetric parameters such as backscatter intensities, ratio (HH/VV, VH/VV, linear depolarization ratio), and co-pol correlation (&rho;HH–VV) along with the vegetation indices derived from the Sentinel-2 data for understanding the spatio-temporal dynamics. The initial results show a promising accuracy in monitoring the dynamics of potato tuberization. Integration of such earth observation (EO) data, in conjunction with in-situ field measurements, might significantly enhance the current capabilities for crop monitoring.

Download Full-text

Synergistic Use of Radar Sentinel-1 and Optical Sentinel-2 Imagery for Crop Mapping: A Case Study for Belgium

Remote Sensing ◽

10.3390/rs10101642 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1642 ◽

Cited By ~ 56

Author(s):

Kristof Van Tricht ◽

Anne Gobin ◽

Sven Gilliams ◽

Isabelle Piccard

Keyword(s):

Remote Sensing ◽

Random Forest ◽

Satellite Remote Sensing ◽

Random Forest Classifier ◽

Optical Observations ◽

Optical Data ◽

Crop Monitoring ◽

Crop Types ◽

Optical Imagery ◽

Sentinel 2

A timely inventory of agricultural areas and crop types is an essential requirement for ensuring global food security and allowing early crop monitoring practices. Satellite remote sensing has proven to be an increasingly more reliable tool to identify crop types. With the Copernicus program and its Sentinel satellites, a growing source of satellite remote sensing data is publicly available at no charge. Here, we used joint Sentinel-1 radar and Sentinel-2 optical imagery to create a crop map for Belgium. To ensure homogenous radar and optical inputs across the country, Sentinel-1 12-day backscatter mosaics were created after incidence angle normalization, and Sentinel-2 normalized difference vegetation index (NDVI) images were smoothed to yield 10-daily cloud-free mosaics. An optimized random forest classifier predicted the eight crop types with a maximum accuracy of 82% and a kappa coefficient of 0.77. We found that a combination of radar and optical imagery always outperformed a classification based on single-sensor inputs, and that classification performance increased throughout the season until July, when differences between crop types were largest. Furthermore, we showed that the concept of classification confidence derived from the random forest classifier provided insight into the reliability of the predicted class for each pixel, clearly showing that parcel borders have a lower classification confidence. We concluded that the synergistic use of radar and optical data for crop classification led to richer information increasing classification accuracies compared to optical-only classification. Further work should focus on object-level classification and crop monitoring to exploit the rich potential of combined radar and optical observations.

Download Full-text

High-Resolution Urban Land Mapping in China from Sentinel 1A/2 Imagery Based on Google Earth Engine

Remote Sensing ◽

10.3390/rs11070752 ◽

2019 ◽

Vol 11 (7) ◽

pp. 752 ◽

Cited By ~ 21

Author(s):

Zhongchang Sun ◽

Ru Xu ◽

Wenjie Du ◽

Lei Wang ◽

Dengsheng Lu

Keyword(s):

High Resolution ◽

Vegetation Index ◽

Urban Land ◽

Google Earth ◽

Economic Research ◽

Optical Data ◽

Large Area ◽

Google Earth Engine ◽

Sar Data ◽

Sentinel 2

Accurate and timely urban land mapping is fundamental to supporting large area environmental and socio-economic research. Most of the available large-area urban land products are limited to a spatial resolution of 30 m. The fusion of optical and synthetic aperture radar (SAR) data for large-area high-resolution urban land mapping has not yet been widely explored. In this study, we propose a fast and effective urban land extraction method using ascending/descending orbits of Sentinel-1A SAR data and Sentinel-2 MSI (MultiSpectral Instrument, Level 1C) optical data acquired from 1 January 2015 to 30 June 2016. Potential urban land (PUL) was identified first through logical operations on yearly mean and standard deviation composites from a time series of ascending/descending orbits of SAR data. A Yearly Normalized Difference Vegetation Index (NDVI) maximum and modified Normalized Difference Water Index (MNDWI) mean composite were generated from Sentinel-2 imagery. The slope image derived from SRTM DEM data was used to mask mountain pixels and reduce the false positives in SAR data over these regions. We applied a region-specific threshold on PUL to extract the target urban land (TUL) and a global threshold on the MNDWI mean, and slope image to extract water bodies and high-slope regions. A majority filter with a three by three window was applied on previously extracted results and the main processing was carried out on the Google Earth Engine (GEE) platform. China was chosen as the testing region to validate the accuracy and robustness of our proposed method through 224,000 validation points randomly selected from high-resolution Google Earth imagery. Additionally, a total of 735 blocks with a size of 900 × 900 m were randomly selected and used to compare our product’s accuracy with the global human settlement layer (GHSL, 2014), GlobeLand30 (2010), and Liu (2015) products. Our method demonstrated the effectiveness of using a fusion of optical and SAR data for large area urban land extraction especially in areas where optical data fail to distinguish urban land from spectrally similar objects. Results show that the average overall, producer’s and user’s accuracies are 88.03%, 94.50% and 82.22%, respectively.

Download Full-text

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

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-547-2018 ◽

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

Abstract. 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&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.

Download Full-text

Discriminating Wet Snow and Firn for Alpine Glaciers Using Sentinel-1 Data: A Case Study at Rofental, Austria

Geosciences ◽

10.3390/geosciences9020069 ◽

2019 ◽

Vol 9 (2) ◽

pp. 69 ◽

Cited By ~ 2

Author(s):

Achim Heilig ◽

Anna Wendleder ◽

Andreas Schmitt ◽

Christoph Mayer

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Optical Data ◽

Optical Remote Sensing ◽

Mountain Ranges ◽

Snow Covered Area ◽

Covered Area ◽

Wet Snow ◽

Sar Data ◽

Sar Analysis

Continuous monitoring of glacier changes supports our understanding of climate related glacier behavior. Remote sensing data offer the unique opportunity to observe individual glaciers as well as entire mountain ranges. In this study, we used synthetic aperture radar (SAR) data to monitor the recession of wet snow area extent per season for three different glacier areas of the Rofental, Austria. For four glaciological years (GYs, 2014/2015–2017/2018), Sentinel-1 (S1) SAR data were acquired and processed. For all four GYs, the seasonal snow retreated above the elevation range of perennial firn. The described processing routine is capable of discriminating wet snow from firn areas for all GYs with sufficient accuracy. For a short in situ transect of the snow—firn boundary, SAR derived wet snow extent agreed within an accuracy of three to four pixels or 30–40 m. For entire glaciers, we used optical remote sensing imagery and field data to assess reliability of derived wet snow covered area extent. Differences in determination of snow covered area between optical data and SAR analysis did not exceed 10% on average. Offsets of SAR data to results of annual field assessments are below 10% as well. The introduced workflow for S1 data will contribute to monitoring accumulation area extent for remote and hazardous glacier areas and thus improve the data basis for such locations.

Download Full-text

crops planting area identification and analysis based on multi-source high resolution remote sensing data

10.5194/egusphere-egu2020-13227 ◽

2020 ◽

Author(s):

Lei Wang ◽

Haoran Sun ◽

Wenjun Li ◽

Liang Zhou

Keyword(s):

Remote Sensing ◽

Random Forest ◽

Classification Accuracy ◽

Kappa Coefficient ◽

Optical Data ◽

Accurate Estimation ◽

Support Vector ◽

Random Forest Method ◽

Multi Temporal ◽

Sar Data

Crop planting structure is of great significance to the quantitative management of agricultural water and the accurate estimation of crop yield. With the increasing spatial and temporal resolution of remote sensing optical and SAR(Synthetic Aperture Radar) images, &#160;efficient crop mapping in large area becomes possible and the accuracy is improved. In this study, Qingyijiang Irrigation District in southwest of China is selected for crop identification methods comparison, which has heterogeneous terrain and complex crop structure . Multi-temporal optical (Sentinel-2) and SAR (Sentinel-1) data were used to calculate NDVI and backscattering coefficient as the main classification indexes. The multi-spectral and SAR data showed significant change in different stages of the whole crop growth period and varied with different crop types. Spatial distribution and texture analysis was also made. Classification using different combinations of indexes were performed using neural network, support vector machine and random forest method. The results showed that, the use of multi-temporal optical data and SAR data in the key growing periods of main crops can both provide satisfactory classification accuracy. The overall classification accuracy was greater than 82% and Kappa coefficient was greater than 0.8. SAR data has high accuracy and much potential in rice identification. However optical data had more accuracy in upland crops classification. In addition, the classification accuracy can be effectively improved by combination of classification indexes from optical and SAR data, the overall accuracy was up to 91.47%. The random forest method was superior to the other two methods in terms of the overall accuracy and the kappa coefficient.

Download Full-text

Investigating Sentinel 2 Multispectral Imagery Efficiency in Describing Spectral Response of Vineyards Covered with Plastic Sheets

Agronomy ◽

10.3390/agronomy10121909 ◽

2020 ◽

Vol 10 (12) ◽

pp. 1909

Author(s):

Enrico Borgogno-Mondino ◽

Laura de Palma ◽

Vittorino Novello

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Spectral Response ◽

Table Grape ◽

Optical Data ◽

Strong Impact ◽

Spectral Indices ◽

Intrinsic Nature ◽

Sentinel 2

The protection of vineyards with overhead plastic covers is a technique largely applied in table grape growing. As with other crops, remote sensing of vegetation spectral reflectance is a useful tool for improving management even for table grape viticulture. The remote sensing of the spectral signals emitted by vegetation of covered vineyards is currently an open field of investigation, given the intrinsic nature of plastic sheets that can have a strong impact on the reflection from the underlying vegetation. Baring these premises in mind, the aim of the present work was to run preliminary tests on table grape vineyards covered with polyethylene sheets, using Copernicus Sentinel 2 (Level 2A product) free optical data, and compare their spectral response with that of similar uncovered vineyards to assess if a reliable spectral signal is detectable through the plastic cover. Vine phenology, air temperature and shoot growth, were monitored during the 2016 growing cycle. Twenty-four Copernicus Sentinel 2 (S2, Level 2A product) images were used to investigate if, in spite of plastic sheets, vine phenology can be similarly described with and without plastic covers. For this purpose, time series of S2 at-the-ground reflectance calibrated bands and correspondent normalized difference vegetation index (NDVI), modified soil-adjusted vegetation index, version two (MSAVI2) and normalized difference water index (NDWI) spectral indices were obtained and analyzed, comparing the responses of two covered vineyards with different plastic sheets in respect of two uncovered ones. Results demonstrated that no significant limitation (for both bands and spectral indices) was introduced by plastic sheets while monitoring spectral behavior of covered vineyards.

Download Full-text

Improving the Accuracy of Multiple Algorithms for Crop Classification by Integrating Sentinel-1 Observations with Sentinel-2 Data

Remote Sensing ◽

10.3390/rs13020243 ◽

2021 ◽

Vol 13 (2) ◽

pp. 243

Author(s):

Amal Chakhar ◽

David Hernández-López ◽

Rocío Ballesteros ◽

Miguel A. Moreno

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Temporal Dynamics ◽

Remote Sensing Data ◽

Radar Data ◽

Optical Data ◽

Support Vector ◽

Sensing Data ◽

Crop Classification

The availability of an unprecedented amount of open remote sensing data, such as Sentinel-1 and -2 data within the Copernicus program, has boosted the idea of combining the use of optical and radar data to improve the accuracy of agricultural applications such as crop classification. Sentinel-1’s Synthetic Aperture Radar (SAR) provides co- and cross-polarized backscatter, which offers the opportunity to monitor agricultural crops using radar at high spatial and temporal resolution. In this study, we assessed the potential of integrating Sentinel-1 information (VV and VH backscatter and their ratio VH/VV with Sentinel-2A data (NDVI) to perform crop classification and to define which are the most important input data that provide the most accurate classification results. Further, we examined the temporal dynamics of remote sensing data for cereal, horticultural, and industrial crops, perennials, deciduous trees, and legumes. To select the best SAR input feature, we tried two approaches, one based on classification with only SAR features and one based on integrating SAR with optical data. In total, nine scenarios were tested. Furthermore, we evaluated the performance of 22 nonparametric classifiers on which most of these algorithms had not been tested before with SAR data. The results revealed that the best performing scenario was the one integrating VH and VV with normalized difference vegetation index (NDVI) and cubic support vector machine (SVM) (the kernel function of the classifier is cubic) as the classifier with the highest accuracy among all those tested.

Download Full-text

FLOOD MONITORING USING SENTINEL-1 SAR DATA: A CASE STUDY BASED ON AN EVENT OF 2018 AND 2019 SOUTHERN PART OF KERALA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliv-m-3-2021-37-2021 ◽

2021 ◽

Vol XLIV-M-3-2021 ◽

pp. 37-41

Author(s):

S. P. Dhanabalan ◽

S. Abdul Rahaman ◽

R. Jegankumar

Keyword(s):

Remote Sensing ◽

Natural Hazards ◽

Satellite Imagery ◽

Heavy Rainfall ◽

Microwave Remote Sensing ◽

Optical Data ◽

Flood Hazards ◽

Night Time ◽

South West Monsoon ◽

Sar Data

Abstract. Flood is a natural hazard influenced by rainfall and dam collapse, which propels release of enormous amount of water. In the last two decades flood is the second largest natural hazards occurred worldwide, which caused serious damage to life properties, settlements and economic activities. Flood mapping is a process that is useful for assessment and reduces the risk factor during the flood. An effective monitoring of flood prone area is necessary to handle GIS techniques and without remote sensing data it is difficult to identify the flooded area in this study Microwave remote sensing plays a lead role in natural hazards, here Synthetic Aperture Radar (SAR) data is the best way for monitoring flood hazards. In this study Southern part of the Kerala is chosen as the study area, In August 2018, during the south west monsoon due to heavy rainfall a severe flood affected the southern part of Kerala which saw a 37% increase in the rate of normal rainfall. The objective of the study is to find the flood zone area using SAR data and estimate the flood occurrence over a period of time. However a satellite imagery of optical data is used to analyse the pre and post event of flood, but during a heavy rainfall, cloud may interrupt the data acquisition. SAR satellite imagery fromSentinel-1A is a cloud penetrating data available in all kind of weather conditions during day and night time, which provides a good source of high resolution data sets. To identify the flood affected area an adapted technology of threshold methodology developed by using SAR data and change detection for the year 2018 and 2019, will illustrate flood extended part in southern part of Kerala. The result shows the estimation of flood extended part of the study area and the damages occurred during a flooded time period of post and pre event, vulnerability assessing of crop and agriculture is to obtain an intensity of the damaged areas which is closely associated with the river channel, the Polarization displays a similar sequence for amount of flooding. The study helps to find the reason of flood extent and to equip with better planning for risk reduction and management during a flooding period.

Download Full-text

An attempt to understand corn and winter wheat temporal behavior by means of Sentinel-1 and Sentinel-2 data and its relation with phonological stages.

10.5194/egusphere-egu21-12307 ◽

2021 ◽

Author(s):

Marta Pasternak ◽

Kamila Pawluszek-Filipiak

Keyword(s):

Remote Sensing ◽

Winter Wheat ◽

Strong Correlation ◽

Vegetation Index ◽

Radar Data ◽

Temporal Behavior ◽

Spectral Indices ◽

Chlorophyll Index ◽

Sar Data ◽

Sentinel 2

Crops are of the fundamental food sources for humanity. Due to the population growth as well as climate change, monitoring of the crops is important to sustain agriculture and conserve natural resources. Development of the remote sensing techniques especially in terms of revisiting time opens new avenues to study crops temporal behaviors from space. Moreover, thanks to the Copernicus program, which guarantees optical as well as radar data to be freely available, there are opportunities to utilize them in an operative way. Additionally, utilization of spectral as well as radar data allows for the synergetic application of both datasets. However, to utilize this data in the operational crop monitoring, it is very important to understand the temporal variations of the remote sensing signal. Therefore, we make an attempt to understand spectral as well as radar remote sensing temporal behavior and its relation with phonological stages.For the analysis, 14 cloud-free Sentinel-2 (S-2) acquisitions as well as 34 Sentinel-1 (S-1) acquisitions are utilized. S-2 data were collected with 2A-level while S-1 data was captured in the format of Single Look Complex (SLC) in the Interferometric Wide (IW) swath mode. SLC products consist of complex SAR data preserving phase information which allows studying polarimetric indicators. All remote sensing (spectral as well as SAR) data cover the time period from 04/05/2020 to 07/11/2020. During this time, also 14 field visits were carried out to capture information about phonological stages of corn and wheat according to the BBCH scale (Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie). Additionally, to better understand the temporal behavior of S-1/S-2 signal, weather information from the Institute of Meteorology and Water Management (IMGW) was captured.Based on various spectral bands of S-2 data, 12 spectral indices were calculated e.g., GNDVI (Green Normalized Vegetation Index), IRECI (Inverted Red-Edge Chlorophyll Index), MCARI (Modified Chlorophyll Absorption in Reflectance Index), MSAVI (Modified Soil-Adjusted Vegetation Index), MTCI (MERIS Terrestrial Chlorophyll Index), NDVI (Normalized Difference Vegetation Index), PSSRa (Pigment Specific Simple Ratio) and others. After radiometric calibration and the Lee speckle filtering, backscattering coefficients (&#963;VVo ,&#963;VHo) of S-1 images were calculated as well as its backscattering ratio (&#963;VHo/ &#963;VVo).&#160; All images were then converted from linear to decibel (dB). Additionally, 2 &#215; 2 covariance matrix delivered from S-1 was extracted from the scattering matrix of each SLC image using PolSARpro version 6.0.2 software. After speckle filtration, total scattered power was derived which allows calculating the Shannon Entropy. This value measures the randomness of the scattering within a pixel.Time series of many S-2 indices reveal the strong correlation between the development of phenology stages of corn and wheat and the increase of S2 delivered values of spectral indices. However, such a strong correlation cannot be observed within many of S-1 indices. Some of them very poorly indicate the correlation between the development of phenology stages of corn and wheat and increase of S-1 indices values. Additionally, it was observed that values of S1/S2 indices for the same phenology stage very between corn and winter wheat.&#160;

Download Full-text

Evaluation of Floods and Landslides Triggered by a Meteorological Catastrophe (Ordu, Turkey, August 2018) Using Optical and Radar Data

Geofluids ◽

10.1155/2020/8830661 ◽

2020 ◽

Vol 2020 ◽

pp. 1-18

Author(s):

Sultan Kocaman ◽

Beste Tavus ◽

Hakan A. Nefeslioglu ◽

Gizem Karakas ◽

Candan Gokceoglu

Keyword(s):

Remote Sensing ◽

Landslide Susceptibility ◽

Radar Data ◽

Regional Level ◽

Disaster Mitigation ◽

Machine Learning Techniques ◽

Optical Data ◽

Economic Losses ◽

Airborne Imaging ◽

Sentinel 2

This study explores the potential of photogrammetric datasets and remote sensing methods for the assessment of a meteorological catastrophe that occurred in Ordu, Turkey in August 2018. During the event, flash floods and several landslides caused losses of lives, evacuation of people from their homes, collapses of infrastructure and large constructions, destruction of agricultural fields, and many other economic losses. The meteorological conditions before and during the flood were analyzed here and compared with long-term records. The flood extent and the landslide susceptibility were investigated by using multisensor and multitemporal data. Sentinel-1 SAR (Synthetic Aperture Radar), Sentinel-2 optical data, and aerial photogrammetric datasets were employed for the investigations using machine learning techniques. The changes were assessed both at a local and regional level and evaluated together with available damage reports. The analysis of the rainfall data recorded during the two weeks before the floods and landslides in heavily affected regions shows that the rainfall continued for consecutive hours with an amount of up to 68 mm/hour. The regional level classification results obtained from Sentinel-1 and Sentinel-2 data by using the random forest (RF) method exhibit 97% accuracy for the flood class. The landslide susceptibility prediction performance from aerial photogrammetric datasets was 92% represented by the Area Under Curve (AUC) value provided by the RF method. The results presented here show that considering the occurrence frequency and immense damages after such events, the use of novel remote sensing technologies and spatial analysis methods is unavoidable for disaster mitigation efforts and for the monitoring of environmental effects. Although the increasing number of earth observation satellites complemented with airborne imaging sensors is capable of ensuring data collection requirement with diverse spectral, spatial, and temporal resolutions, further studies are required to automate the data processing, efficient information extraction, and data fusion and also to increase the accuracy of the results.

Download Full-text