Irrigation Events Detection over Intensively Irrigated Grassland Plots Using Sentinel-1 Data

Better management of water consumption and irrigation schedule in irrigated agriculture is essential in order to save water resources, especially at regional scales and under changing climatic conditions. In the context of water management, the aim of this study is to monitor irrigation activities by detecting the irrigation episodes at plot scale using the Sentinel-1 (S1) C-band SAR (synthetic-aperture radar) time series over intensively irrigated grassland plots located in the Crau plain of southeast France. The method consisted of assessing the newly developed irrigation detection model (IDM) at plot scale over the irrigated grassland plots. First, four S1-SAR time series acquired from four different S1-SAR acquisitions (different S1 orbits), each at six-day revisit time, were obtained over the study site. Next, the IDM was applied at each available SAR image from each S1-SAR series to obtain an irrigation indicator at each SAR image (no, low, medium, or high irrigation possibility). Then, the irrigation indicators obtained at each image from each S1-SAR time series (four series) were added and combined by threshold value criteria to determine the existence or absence of an irrigation event. Finally, the performance of the IDM for irrigation detection was assessed by comparing the in situ recorded irrigation events at each plot and the detected irrigation events. The results show that using only the VV polarization, 82.4% of the in situ registered irrigation events are correctly detected with an F_score value reaching 73.8%. Less accuracy is obtained using only the VH polarization, where 79.9% of the in situ irrigation events are correctly detected with an F_score of 72.2%. The combined use of the VV and VH polarization showed that 74.1% of the irrigation events are detected with a higher F_score value of 76.4%. The analysis of the undetected irrigation events revealed that, in the presence of very well-developed vegetation cover (normalized difference of vegetation index (NDVI) ≥ 0.8); higher uncertainty in irrigation detection is observed, where 80% of the undetected events correspond to an NDVI value greater than 0.8. The results also showed that small-sized plots encounter more false irrigation detections than large-sized plots certainly because the pixel spacing of S1 data (10 m × 10 m) is not adapted to small size plots. The obtained results prove the efficiency of the S1 C-band data and the IDM for detecting irrigation events at the plot scale, which would help in improving the irrigation water management at large scales especially with availability and global coverage of the S1 product.

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Irrigation Optimization Under a Limited Water Supply by the Integration of Modern Approaches into Traditional Water Management on the Cotton Fields

Remote Sensing ◽

10.3390/rs11182127 ◽

2019 ◽

Vol 11 (18) ◽

pp. 2127

Author(s):

Polinova ◽

Salinas ◽

Bonfante ◽

Brook

Keyword(s):

Water Management ◽

Water Supply ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Irrigation Schedule ◽

Irrigation Scheduling ◽

Future Climate Change ◽

Weather Forecasts ◽

Red Edge ◽

Cotton Fields

The ability to effectively develop agriculture with limited water resources is an important strategic objective to face future climate change and to achieve the Sustainable Development Goal 2 (SDG2) of the United Nations. Since new conditions increasingly point to a limited water supply, the aim of modern irrigation management is to be sure to maximize the crop yield and minimize water use. This study aims to explore the advantages of the traditional agronomic approach, agro-hydrological model and field feedback obtained by spectroscopy, to optimize irrigation water management in the example of a cotton field. The study was conducted for two summer growing seasons in 2015 and 2016 in Kibbutz Hazorea, near Haifa, Israel. The irrigation schedule was developed by farmers using weather forecasts and corrected by the results of field inspections. The Soil Water Atmosphere Plant (SWAP) model was applied to optimize seasonal water distribution based on different criteria (critical soil pressure head and allowable daily stress). A new optimization algorithm for irrigation schedules by weather forecasts and vegetation indices was developed and presented in this paper. A few indices related to physical parameters and plant health (Normalized Difference Vegetation Index, Red Edge Normalized Difference Vegetation Index, Modified Chlorophyll Absorption Ratio Index 2, and Photochemical Reflectance Index) were considered. Red Edge Normalized Difference Vegetation Index proves itself as a suitable parameter for monitoring crop state due to its clear-cut response to irrigation treatments and was introduced in the developed algorithm. The performance of the considered irrigation scheduling approaches was assessed by a simulation model application for cotton fields in 2016. The results show, that the irrigation schedule developed by farmers did not compensate for the absence of precipitation in spring, which led to long-term lack of water during crop development. The optimization developed by SWAP allows determining the minimal amount of water which ensures appropriate yield. However, this approach could not take into account the non-linear effect of the lack of water at specific phenological stages on the yield. The new algorithm uses the minimal sufficient seasonal amount of water obtained from SWAP optimization. The approach designed allows one to prevent critical stress in cotton and distribute water in conformity with agronomic practice.

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Estimación de la evapotranspiración de maíz forrajero apoyada con sensores remotos y mediciones in situ.

REVISTA TERRA LATINOAMERICANA ◽

10.28940/terra.v37i3.485 ◽

2019 ◽

Vol 37 (3) ◽

pp. 279 ◽

Cited By ~ 1

Author(s):

Arturo Reyes González ◽

David Guadalupe Reta Sánchez ◽

Juan Isidro Sánchez Duarte ◽

Esmeralda Ochoa Martínez ◽

Karla Rodríguez Hernández ◽

...

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Irrigation System ◽

Vegetation Indices ◽

Irrigation Scheduling ◽

Irrigated Agriculture ◽

Crop Development ◽

Subsurface Drip

Irrigated agriculture requires better estimates of crop water demand. The aim of this study was to estimate the evapotranspiration (ETc) in forage corn through vegetation indices obtained in situ and estimated with remote sensing in the Comarca Lagunera, Mexico. The research was carried out in 2011 and 2012 in four 900 m2 plots irrigated with a subsurface drip irrigation system. Normalized Difference Vegetation Index (NDVI) and crop coeff icient (Kc) during crop development were determined. The initial, maximum and f inal NDVI values were 0.13, 0.79 and 0.63 for both methods and in both cycles. The maximum Kc values were obtained 54 and 48 days after sowing (DDS) with GreenSeeker, and at 61 and 59 DDS with satellite images in 2011 and 2012, respectively. The results showed a good relationship between ETc estimated in situ and ETc estimated with remote sensing (r = 0.98) for both years. Although the variation of ETc using both methods was 1.2 mm day‑1, early in the cycle and 7.4 mm day-1 to flowering start-milky grains. Water needs of forage corn were estimated with similar precision using remote sensing and in situ measurements. Therefore, both methods can be used to improve irrigation scheduling and preserve water resources in agriculture.

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Seasonal Crop Water Balance Using Harmonized Landsat-8 and Sentinel-2 Time Series Data

Water ◽

10.3390/w11112236 ◽

2019 ◽

Vol 11 (11) ◽

pp. 2236 ◽

Cited By ~ 8

Author(s):

Viviana Gavilán ◽

Mario Lillo-Saavedra ◽

Eduardo Holzapfel ◽

Diego Rivera ◽

Angel García-Pedrero

Keyword(s):

Water Management ◽

Time Series ◽

Water Balance ◽

Distribution System ◽

Vegetation Index ◽

Time Series Data ◽

Series Data ◽

Landsat 8 ◽

Electromagnetic Spectrum ◽

Sentinel 2

Efficient water management in agriculture requires a precise estimate of evapotranspiration ( E T ). Although local measurements can be used to estimate surface energy balance components, these values cannot be extrapolated to large areas due to the heterogeneity and complexity of agriculture environment. This extrapolation can be done using satellite images that provide information in visible and thermal infrared region of the electromagnetic spectrum; however, most current satellite sensors do not provide this end, but they do include a set of spectral bands that allow the radiometric behavior of vegetation that is highly correlated with the E T . In this context, our working hypothesis states that it is possible to generate a strategy of integration and harmonization of the Normalized Difference Vegetation Index ( N D V I ) obtained from Landsat-8 ( L 8 ) and Sentinel-2 ( S 2 ) sensors in order to obtain an N D V I time series used to estimate E T through fit equations specific to each crop type during an agricultural season (December 2017–March 2018). Based on the obtained results it was concluded that it is possible to estimate E T using an N D V I time series by integrating data from both sensors L 8 and S 2 , which allowed to carry out an updated seasonal water balance over study site, improving the irrigation water management both at plot and water distribution system scale.

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Forest cover trend analysis using MODIS time series and its climatic responses in the Mari El Republic of Russia

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/932/1/012003 ◽

2021 ◽

Vol 932 (1) ◽

pp. 012003

Author(s):

E A Kurbanov ◽

O N Vorobev ◽

S A Lezhnin ◽

D M Dergunov ◽

Y Wang

Keyword(s):

Time Series ◽

Satellite Data ◽

Vegetation Index ◽

Forest Cover ◽

Meteorological Data ◽

Climatic Conditions ◽

Data Series ◽

Modis Ndvi ◽

Climatic Responses ◽

Phenological Metrics

Abstract This study assesses whether MODIS NDVI satellite data time series can be used to detect changes in forest phenology over the different forest types of the Mari El Republic of Russia. Due to the severe climatic conditions, coniferous and deciduous forests of this region are especially vulnerable to climate change, which can lead to stresses from droughts and increase the frequency of wild fires in the long term. Time series analysis was applied to 16-day composite MODIS (MOD13Q1) (250 m) satellite data records (2000-2020) for the investigated territory, based on understanding that the NDVI trend vectors would enable detection of phenological changes in forest cover. There was also the determination of land cover/land use change for the area and examination of meteorological data for the investigated period. For the study, we utilized four phenological metrics: start of season (SOS), end of season (EOS), length of season (LOS), and Maximum vegetation index (MVI). The NDVI MODIS data series were smoothed in the TimeSAT software using the Savitsky-Golay filter. The results of the study show that over the 20-years period variations in phenological metrics do not have a significant impact on the productivity and growth of forest ecosystems in the Mari El Republic.

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Modelling of Vegetation Dynamics from Satellite Time Series to Determine Proglacial Primary Succession in the Course of Global Warming—A Case Study in the Upper Martell Valley (Eastern Italian Alps)

Remote Sensing ◽

10.3390/rs13214450 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4450

Author(s):

Bettina Knoflach ◽

Katharina Ramskogler ◽

Matthew Talluto ◽

Florentin Hofmeister ◽

Florian Haas ◽

...

Keyword(s):

Time Series ◽

Vegetation Cover ◽

Laser Scanning ◽

Vegetation Index ◽

Ice Age ◽

Plant Colonization ◽

Italian Alps ◽

Climatic Impact ◽

In Situ Observations

Satellite-based long-term observations of vegetation cover development in combination with recent in-situ observations provide a basis to better understand the spatio-temporal changes of vegetation patterns, their sensitivity to climate drivers and thus climatic impact on proglacial landscape development. In this study we combined field investigations in the glacier forelands of Fürkele-, Zufall- and Langenferner (Ortles-Cevedale group/Eastern Italian Alps) with four different Vegetation Indices (VI) from Landsat scenes in order to test the suitability for modelling an area-wide vegetation cover map by using a Bayesian beta regression model (RStan). Since the model with the Normalized Difference Vegetation Index (NDVI) as predictor showed the best results, it was used to calculate a vegetation cover time series (1986–2019). The alteration of the proglacial areas since the end of the Little Ice Age (LIA) was analyzed from digital elevation models based on Airborne Laser Scanning (ALS) data and areal images, orthophotos, historical maps and field mapping campaigns. Our results show that a massive glacier retreat with an area loss of 8.1 km2 (56.9%; LIA–2019) resulted in a constant enlargement of the glacier forelands, which has a statistically significant impact on the degree of vegetation cover. The area covered by vegetation increased from 0.25 km2 (5.6%) in 1986 to 0.90 km2 (11.2%) in 2019 with a significant acceleration of the mean annual changing rate. As patterns of both densification processes and plant colonization at higher elevations can be reflected by the model results, we consider in-situ observations combined with NDVI time series to be powerful tools for monitoring vegetation cover changes in alpine proglacial areas.

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An Operational Framework for Mapping Irrigated Areas at Plot Scale Using Sentinel-1 and Sentinel-2 Data

Remote Sensing ◽

10.3390/rs13132584 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2584

Author(s):

Hassan Bazzi ◽

Nicolas Baghdadi ◽

Ghaith Amin ◽

Ibrahim Fayad ◽

Mehrez Zribi ◽

...

Keyword(s):

Time Series ◽

Selection Criteria ◽

Vegetation Index ◽

Data Availability ◽

Training Data ◽

Classification Model ◽

In Situ Data ◽

Ndvi Time Series ◽

Sentinel 2

In this study, we present an operational methodology for mapping irrigated areas at plot scale, which overcomes the limitation of terrain data availability, using Sentinel-1 (S1) C-band SAR (synthetic-aperture radar) and Sentinel-2 (S2) optical time series. The method was performed over a study site located near Orléans city of north-central France for four years (2017 until 2020). First, training data of irrigated and non-irrigated plots were selected using predefined selection criteria to obtain sufficient samples of irrigated and non-irrigated plots each year. The training data selection criteria is based on two irrigation metrics; the first one is a SAR-based metric derived from the S1 time series and the second is an optical-based metric derived from the NDVI (normalized difference vegetation index) time series of the S2 data. Using the newly developed irrigation event detection model (IEDM) applied for all S1 time series in VV (Vertical-Vertical) and VH (Vertical-Horizontal) polarizations, an irrigation weight metric was calculated for each plot. Using the NDVI time series, the maximum NDVI value achieved in the crop cycle was considered as a second selection metric. By fixing threshold values for both metrics, a dataset of irrigated and non-irrigated samples was constructed each year. Later, a random forest classifier (RF) was built for each year in order to map the summer agricultural plots into irrigated/non-irrigated. The irrigation classification model uses the S1 and NDVI time series calculated over the selected training plots. Finally, the proposed irrigation classifier was validated using real in situ data collected each year. The results show that, using the proposed classification procedure, the overall accuracy for the irrigation classification reaches 84.3%, 93.0%, 81.8%, and 72.8% for the years 2020, 2019, 2018, and 2017, respectively. The comparison between our proposed classification approach and the RF classifier built directly from in situ data showed that our approach reaches an accuracy nearly similar to that obtained using in situ RF classifiers with a difference in overall accuracy not exceeding 6.2%. The analysis of the obtained classification accuracies of the proposed method with precipitation data revealed that years with higher rainfall amounts during the summer crop-growing season (irrigation period) had lower overall accuracy (72.8% for 2017) whereas years encountering a drier summer had very good accuracy (93.0% for 2019).

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Assessment of Crop Changes in Southern Uzbekistan for Better Crop Allocation and Water Resource Management

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d4318.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 11513-11516

Keyword(s):

Water Management ◽

Food Security ◽

Water Resource Management ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Irrigated Agriculture ◽

Crop Water ◽

Water Shortages ◽

Lower Altitude ◽

Over Time

Economy of Central Asian countries heavily rely on irrigated agriculture. The region is facing challenges with regard to water shortages to meet currently cultivated crop water requirement. Talking about water generally gives a view of food security, climate change and energy efficiency. Scarcity of water leads to better understand the role of water to the environment and its effect to the nature. As water is indispensable part of agriculture, there is always need for positive developments of sustainable water management system in agricultural fields. Improper crop allocation can influence for degradation of land, groundwater level change, as well as food security. Therefore, this study aims to learn crop pattern changes over time which can serve as a basis for better water management in the region. In addition to this, this study covered what is the major changes in irrigated agriculture in terms of crop allocation to understand the crop rotations over time. Freely available satellite data was used for mapping crop classification and their changes for this region. Widely used Normalized Difference Vegetation Index (NDVI) technique was applied to extract information from Landsat raw data. The study was carried out in Karshi Steppe in southern part of Uzbekistan and this region is one of the area that is experiencing challenges on water availability where water resources are mainly pumped from Amudarya River which is located in the lower altitude of the region. Therefore understanding currently cultivated crop types will help to further explore actual crop water requirements which may provide an information on how much water is actually needed and can be lifted with minimum financial costs. They are then can be used in further application to find out Evapotranspiration (ET) of individual crops and help decision makers making better decisions.

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In situ irradiation effects studies in medium- and high-voltage TEM

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100137549 ◽

1995 ◽

Vol 53 ◽

pp. 234-235

Author(s):

Charles W. Allen

Keyword(s):

Cross Section ◽

Particle Flux ◽

Threshold Value ◽

High Energy ◽

Irradiation Damage ◽

Defect Complexes ◽

Lattice Position ◽

Electrons And Ions ◽

The Masses

With respect to structural consequences within a material, energetic electrons, above a threshold value of energy characteristic of a particular material, produce vacancy-interstial pairs (Frenkel pairs) by displacement of individual atoms, as illustrated for several materials in Table 1. Ion projectiles produce cascades of Frenkel pairs. Such displacement cascades result from high energy primary knock-on atoms which produce many secondary defects. These defects rearrange to form a variety of defect complexes on the time scale of tens of picoseconds following the primary displacement. A convenient measure of the extent of irradiation damage, both for electrons and ions, is the number of displacements per atom (dpa). 1 dpa means, on average, each atom in the irradiated region of material has been displaced once from its original lattice position. Displacement rate (dpa/s) is proportional to particle flux (cm-2s-1), the proportionality factor being the “displacement cross-section” σD (cm2). The cross-section σD depends mainly on the masses of target and projectile and on the kinetic energy of the projectile particle.

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Combined Multi-Time Series SAR Imagery and InSAR Technology for Rice Identification in Cloudy Regions

Applied Sciences ◽

10.3390/app11156923 ◽

2021 ◽

Vol 11 (15) ◽

pp. 6923

Author(s):

Rui Zhang ◽

Zhanzhong Tang ◽

Dong Luo ◽

Hongxia Luo ◽

Shucheng You ◽

...

Keyword(s):

Remote Sensing ◽

Time Series ◽

Identification Accuracy ◽

Optical Remote Sensing ◽

Sar Image ◽

Single Time ◽

Growing Period ◽

Sensing Technology ◽

Sar Imagery ◽

Cloudy Region

The use of remote sensing technology to monitor farmland is currently the mainstream method for crop research. However, in cloudy and misty regions, the use of optical remote sensing image is limited. Synthetic aperture radar (SAR) technology has many advantages, including high resolution, multi-mode, and multi-polarization. Moreover, it can penetrate clouds and mists, can be used for all-weather and all-time Earth observation, and is sensitive to the shape of ground objects. Therefore, it is widely used in agricultural monitoring. In this study, the polarization backscattering coefficient on time-series SAR images during the rice-growing period was analyzed. The rice identification results and accuracy of InSAR technology were compared with those of three schemes (single-time-phase SAR, multi-time-phase SAR, and combination of multi-time-phase SAR and InSAR). Results show that VV and VH polarization coherence coefficients can well distinguish artificial buildings. In particular, VV polarization coherence coefficients can well distinguish rice from water and vegetation in August and September, whereas VH polarization coherence coefficients can well distinguish rice from water and vegetation in August and October. The rice identification accuracy of single-time series Sentinel-1 SAR image (78%) is lower than that of multi-time series SAR image combined with InSAR technology (81%). In this study, Guanghan City, a cloudy region, was used as the study site, and a good verification result was obtained.

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New determinations of tides on the north-western Ross Ice Shelf

Antarctic Science ◽

10.1017/s0954102020000498 ◽

2020 ◽

pp. 1-14

Author(s):

Richard D. Ray ◽

Kristine M. Larson ◽

Bruce J. Haines

Keyword(s):

Time Series ◽

Tide Gauge ◽

High Rate ◽

Ice Shelf ◽

Ross Ice Shelf ◽

The North ◽

Global Positioning ◽

North Western

Abstract New determinations of ocean tides are extracted from high-rate Global Positioning System (GPS) solutions at nine stations sitting on the Ross Ice Shelf. Five are multi-year time series. Three older time series are only 2–3 weeks long. These are not ideal, but they are still useful because they provide the only in situ tide observations in that sector of the ice shelf. The long tide-gauge observations from Scott Base and Cape Roberts are also reanalysed. They allow determination of some previously neglected tidal phenomena in this region, such as third-degree tides, and they provide context for analysis of the shorter datasets. The semidiurnal tides are small at all sites, yet M2 undergoes a clear seasonal cycle, which was first noted by Sir George Darwin while studying measurements from the Discovery expedition. Darwin saw a much larger modulation than we observe, and we consider possible explanations - instrumental or climatic - for this difference.

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