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 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 Effect of tillage, slope, and rainfall on soil surface microtopography quantified by geostatistical and fractal indices during sheet erosion

2020 ◽  
Vol 12 (1) ◽  
pp. 232-241
Author(s):  
Na Ta ◽  
Chutian Zhang ◽  
Hongru Ding ◽  
Qingfeng Zhang
Keyword(s):  
Surface Roughness ◽  
Fractal Dimension ◽  
Soil Surface ◽  
Rainfall Events ◽  
Tillage Practices ◽  
Surface Microtopography ◽  
Artificial Rainfall ◽  
Soil Surface Roughness ◽  
The Difference ◽  
Sheet Erosion

AbstractTillage and slope will influence soil surface roughness that changes during rainfall events. This study tests this effect under controlled conditions quantified by geostatistical and fractal indices. When four commonly adopted tillage practices, namely, artificial backhoe (AB), artificial digging (AD), contour tillage (CT), and linear slope (CK), were prepared on soil surfaces at 2 × 1 × 0.5 m soil pans at 5°, 10°, or 20° slope gradients, artificial rainfall with an intensity of 60 or 90 mm h−1 was applied to it. Measurements of the difference in elevation points of the surface profiles were taken before rainfall and after rainfall events for sheet erosion. Tillage practices had a relationship with fractal indices that the surface treated with CT exhibited the biggest fractal dimension D value, followed by the surfaces AD, AB, and CK. Surfaces under a stronger rainfall tended to have a greater D value. Tillage treatments affected anisotropy differently and the surface CT had the strongest effect on anisotropy, followed by the surfaces AD, AB, and CK. A steeper surface would have less effect on anisotropy. Since the surface CT had the strongest effect on spatial variability or the weakest spatial autocorrelation, it had the smallest effect on runoff and sediment yield. Therefore, tillage CT could make a better tillage practice of conserving water and soil. Simultaneously, changes in semivariogram and fractal parameters for surface roughness were examined and evaluated. Fractal parameter – crossover length l – is more sensitive than fractal dimension D to rainfall action to describe vertical differences in soil surface roughness evolution.

scholarly journals Erosion Process and Temporal Variations in the Soil Surface Roughness of Spoil Heaps under Multi-Day Rainfall Simulation

2020 ◽  
Vol 12 (14) ◽  
pp. 2192
Author(s):  
Jiaorong Lv ◽  
Yongsheng Xie ◽  
Han Luo
Keyword(s):  
Surface Roughness ◽  
Erosion Rate ◽  
Soil Surface ◽  
Rainfall Event ◽  
Rainfall Simulation ◽  
Rainfall Events ◽  
Erosion Processes ◽  
Spoil Heap ◽  
Soil Surface Roughness ◽  
Spoil Heaps

The extensive artificially accelerated erosion of spoil heaps on newly engineered landforms is a key ecological management point requiring better understanding. Soil surface roughness is a crucial factor influencing erosion processes; however, study on spoil heap erosion with a view of surface roughness is lacking. This study investigated the erosion processes and the spatiotemporal variation of surface roughness on spoil heaps, and then, analyzed how the roughness affected the hydrological and sediment yield characteristics. Sequences of four artificial rainstorms with constant rainfall intensity (90 mm/h) were applied to cone-shaped spoil heaps (ground radius 3.5 m, height 2.3 m) of a loess soil containing 30 mass percent rock fragments. The surface elevation was sampled by a laser scanner. For the surface roughness indicators, the root mean square height (rmsh) and the correlation length (cl) increased sharply during the first rainfall event, and in the last three rainfall events, rmsh increased slightly and cl showed a relative decrease. The initial rmsh/cl of the whole slope surface ranged from 0.063 to 0.135, and increased with the rainfall sequence, thus, indicating that the spoil heap surface became rougher. Increasing soil roughness in the rainfall sequence delayed the initial runoff time and increased the runoff yield. The average runoff coefficient of the spoil heaps was 0.658. The average erosion rate of each rainfall event can be simulated by a regression equation of the corresponding average runoff rate and median cl (R-square of 0.816). Soil slumping with an average volume of 0.014 m3 occurred in the first two rainfall events, thus, significantly changing the roughness and peak instant erosion rate. Together, the results revealed the effects of surface roughness on the erosion of spoil heaps and would provide a useful reference for soil loss prediction and control.

scholarly journals Estimation of Soil Surface Roughness Using Stereo Vision Approach

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4386
Author(s):  
Afshin Azizi ◽  
Yousef Abbaspour-Gilandeh ◽  
Tarahom Mesri-Gundoshmian ◽  
Aitazaz A. Farooque ◽  
Hassan Afzaal
Keyword(s):  
Surface Roughness ◽  
Stereo Vision ◽  
Agricultural Soils ◽  
Depth Map ◽  
Soil Surface ◽  
Regression Equations ◽  
Height Profile ◽  
Peak Fitting ◽  
Soil Surface Roughness ◽  
Tillage Operations

Soil roughness is one of the most challenging issues in the agricultural domain and plays a crucial role in soil quality. The objective of this research was to develop a computerized method based on stereo vision technique to estimate the roughness formed on the agricultural soils. Additionally, soil till quality was investigated by analyzing the height of plow layers. An image dataset was provided in the real conditions of the field. For determining the soil surface roughness, the elevation of clods obtained from tillage operations was computed using a depth map. This map was obtained by extracting and matching corresponding keypoints as super pixels of images. Regression equations and coefficients of determination between the measured and estimated values indicate that the proposed method has a strong potential for the estimation of soil shallow roughness as an important physical parameter in tillage operations. In addition, peak fitting of tilled layers was applied to the height profile to evaluate the till quality. The results of this suggest that the peak fitting is an effective method of judging tillage quality in the fields.

scholarly journals Evaluation of the Consistency of Simultaneously Acquired Sentinel-2 and Landsat 8 Imagery on Plastic Covered Greenhouses

2020 ◽  
Vol 12 (12) ◽  
pp. 2015 ◽  
Author(s):  
Manuel Ángel Aguilar ◽  
Rafael Jiménez-Lao ◽  
Abderrahim Nemmaoui ◽  
Fernando José Aguilar ◽  
Dilek Koc-San ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Remote Sensing Techniques ◽  
Image Pairs ◽  
Study Sites ◽  
High Level ◽  
Sentinel 2

Remote sensing techniques based on medium resolution satellite imagery are being widely applied for mapping plastic covered greenhouses (PCG). This article aims at testing the spectral consistency of surface reflectance values of Sentinel-2 MSI (S2 L2A) and Landsat 8 OLI (L8 L2 and the pansharpened and atmospherically corrected product from L1T product; L8 PANSH) data in PCG areas located in Spain, Morocco, Italy and Turkey. The six corresponding bands of S2 and L8, together with the normalized difference vegetation index (NDVI), were generated through an OBIA approach for each PCG study site. The coefficient of determination (r2) and the root mean square error (RMSE) were computed in sixteen cloud-free simultaneously acquired image pairs from the four study sites to evaluate the coherence between the two sensors. It was found that the S2 and L8 correlation (r2 > 0.840, RMSE < 9.917%) was quite good in most bands and NDVI. However, the correlation of the two sensors fluctuated between study sites, showing occasional sun glint effects on PCG roofs related to the sensor orbit and sun position. Moreover, higher surface reflectance discrepancies between L8 L2 and L8 PANSH data, mainly in the visible bands, were always observed in areas with high-level aerosol values derived from the aerosol quality band included in the L8 L2 product (SR aerosol). In this way, the consistency between L8 PANSH and S2 L2A was improved mainly in high-level aerosol areas according to the SR aerosol band.

scholarly journals Assessment of Near-Real-Time Satellite Precipitation Products from GSMaP in Monitoring Rainfall Variations over Taiwan

2021 ◽  
Vol 13 (2) ◽  
pp. 202
Author(s):  
Wan-Ru Huang ◽  
Pin-Yi Liu ◽  
Jie Hsu ◽  
Xiuzhen Li ◽  
Liping Deng
Keyword(s):  
Real Time ◽  
Complex Terrain ◽  
Daily Rainfall ◽  
Rainfall Estimation ◽  
Rainfall Events ◽  
Satellite Precipitation ◽  
Different Seasons ◽  
Daily Scale ◽  
Rainfall Variations ◽  
Southwestern Taiwan

This study assessed four near-real-time satellite precipitation products (NRT SPPs) of Global Satellite Mapping of Precipitation (GSMaP)—NRT v6 (hereafter NRT6), NRT v7 (hereafter NRT7), Gauge-NRT v6 (hereafter GNRT6), and Gauge-NRT v7 (hereafter GNRT7)— in representing the daily and monthly rainfall variations over Taiwan, an island with complex terrain. The GNRT products are the gauge-adjusted version of NRT products. Evaluations for warm (May–October) and cold months (November–April) were conducted from May 2017 to April 2020. By using observations from more than 400 surface gauges in Taiwan as a reference, our evaluations showed that GNRT products had a greater error than NRT products in underestimating the monthly mean rainfall, especially during the warm months. Among SPPs, NRT7 performed best in quantitative monthly mean rainfall estimation; however, when examining the daily scale, GNRT6 and GNRT7 were superior, particularly for monitoring stronger (i.e., more intense) rainfall events during warm and cold months, respectively. Spatially, the major improvement from NRT6 to GNRT6 (from NRT7 to GNRT7) in monitoring stronger rainfall events over southwestern Taiwan was revealed during warm (cold) months. From NRT6 to NRT7, the improvement in daily rainfall estimation primarily occurred over southwestern and northwestern Taiwan during the warm and cold months, respectively. Possible explanations for the differences between the ability of SPPs are attributed to the algorithms used in SPPs. These findings highlight that different NRT SPPs of GSMaP should be used for studying or monitoring the rainfall variations over Taiwan for different purposes (e.g., warning of floods in different seasons, studying monthly or daily precipitation features in different seasons, etc.).

Biology and natural history of Caladenia

2009 ◽  
Vol 57 (4) ◽  
pp. 247 ◽  
Author(s):  
Kingsley Dixon ◽  
Raymond L. Tremblay
Keyword(s):  
Natural History ◽  
Arid Zone ◽  
Secondary Infection ◽  
Soil Surface ◽  
Growth Cycle ◽  
Cortical Cells ◽  
Herbaceous Perennials ◽  
Terrestrial Orchids ◽  
History Of ◽  
Soil Wetting

The genus Caladenia comprises species that exhibit remarkable consistency in terms of growth form and phenological patterns. All taxa are herbaceous perennials, with the shoot arising annually from a single, usually spheroid, tuber and producing a single, hairy leaf. The tuber is annually replaced either side-by-side with the parent tuber or terminating a descending structure known as a dropper. The dropper is a depth-seeking mechanism that enables placement of the tuber at depth in the soil as a means to avoid predation by surface-foraging native mammals or away from the high temperatures and desiccating conditions during summer dormancy. The 3--5 attenuated roots produced at the junction between the tuber and shoot and produced late in the growing cycle and devoid of mycorrhiza suggest their functional significance may relate to water uptake. Mycorrhizal endophytes are confined to a hypertrophic stem region at the soil surface (collar) subtending the leaf that positions the collar directly in the organically rich zone at the soil surface. This morphology is a unique characteristic of several Australasian orchids in the tribe Diuridae. Mycorrhizal infection occurs rapidly, with maximum colonisation in concert with the onset of breaking rains. Pelotons are restricted to cortical cells, with fully developed pelotons throughout infected tissues within a week or so of soil wetting. Infection occurs as a ‘once-off’ event, with little evidence of secondary infection later in the growth cycle and no evidence of peloton digestion. Some taxa utilise vegetative propagation, often leading to localised clustering as for taxa in the ‘filamentosa’ complex or, extensive clonal mats as found in Caladenia flava and C. latifolia where daughter tubers are produced at the end of extending horizontal outgrowths. For the majority of taxa, plants remain dry-season (summer) dormant from a few months up to 7 months for arid-zone taxa, with shoot emergence from the tuber of temperate species thought to occur in response to a drop in the mean minimum temperature. Pollination biology of Caladenia is apparently through a process of deception, either as food or sexual mimics, with some taxa engaging in self-pollination. Here we review the natural history of Caladenia and acknowledge that much of our understanding is based on assumptions of the biology of terrestrial orchids in general and emphasise areas of research and biological enquiry that will be critical in the development of an effective conservation program for the genus.

scholarly journals Potential Applications of GNSS-R Observations over Agricultural Areas: Results from the GLORI Airborne Campaign

2018 ◽  
Vol 10 (8) ◽  
pp. 1245 ◽  
Author(s):  
Mehrez Zribi ◽  
Erwan Motte ◽  
Nicolas Baghdadi ◽  
Frédéric Baup ◽  
Sylvia Dayau ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Signal To Noise Ratio ◽  
Soil Surface ◽  
Satellite System ◽  
Area Index ◽  
Vegetation Height ◽  
Study Sites ◽  
Global Navigation Satellite ◽  
Vegetation Characteristic ◽  
Agricultural Areas

The aim of this study is to analyze the sensitivity of airborne Global Navigation Satellite System Reflectometry (GNSS-R) on soil surface and vegetation cover characteristics in agricultural areas. Airborne polarimetric GNSS-R data were acquired in the context of the GLORI’2015 campaign over two study sites in Southwest France in June and July of 2015. Ground measurements of soil surface parameters (moisture content) and vegetation characteristics (leaf area index (LAI), and vegetation height) were recorded for different types of crops (corn, sunflower, wheat, soybean, vegetable) simultaneously with the airborne GNSS-R measurements. Three GNSS-R observables (apparent reflectivity, the reflected signal-to-noise-ratio (SNR), and the polarimetric ratio (PR)) were found to be well correlated with soil moisture and a major vegetation characteristic (LAI). A tau-omega model was used to explain the dependence of the GNSS-R reflectivity on both the soil moisture and vegetation parameters.

Sign in / Sign up

Export Citation Format

Share Document