scholarly journals A Simplified Sub-Surface Soil Salinity estimation using Synergy of Sentinel-1 SAR and Sentinel-2 multispectral satellite data, for early stages of wheat crop growth in Rupnagar, Punjab, India

2020 ◽  
Author(s):  
Akshar Tripathi ◽  
Reet Tiwari
Keyword(s):  
Satellite Data ◽  
Soil Salinity ◽  
Surface Soil ◽  
Crop Growth ◽  
Wheat Crop ◽  
Early Stages ◽  
Sentinel 2

scholarly journals Assimilation of Sentinel-2 Leaf Area Index Data into a Physically-Based Crop Growth Model for Yield Estimation

Agronomy ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 255 ◽  
Author(s):  
Francesco Novelli ◽  
Heide Spiegel ◽  
Taru Sandén ◽  
Francesco Vuolo
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Crop Yield ◽  
Field Data ◽  
Crop Growth ◽  
Wheat Crop ◽  
Calibration Data ◽  
Yield Estimation ◽  
Area Index ◽  
Sentinel 2

Remote sensing data, crop growth models, and optimization routines constitute a toolset that can be used together to map crop yield over large areas when access to field data is limited. In this study, Leaf Area Index (LAI) data from the Copernicus Sentinel-2 satellite were combined with the Environmental Policy Integrated Climate (EPIC) model to estimate crop yield using a re-calibration data assimilation approach. The experiment was implemented for a winter wheat crop during two growing seasons (2016 and 2017) under four different fertilization management strategies. A number of field measurements were conducted spanning from LAI to biomass and crop yields. LAI showed a good correlation between the Sentinel-2 estimates and the ground measurements using non-destructive method. A correlating fit between satellite LAI curves and EPIC modelled LAI curves was also observed. The assimilation of LAI in EPIC provided an improvement in yield estimation in both years even though in 2017 strong underestimations were observed. The diverging results obtained in the two years indicated that the assimilation framework has to be tested under different environmental conditions before being applied on a larger scale with limited field data.

scholarly journals MONITORING WHEAT CROP GROWTH PARAMETERS USING TIME SERIES SENTINEL-1 AND SENTINEL-2 DATA FOR AGRICULTURAL APPLICATION IN MONGOLIA

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 989-994
Author(s):  
B. Gansukh ◽  
B. Batsaikhan ◽  
A. Dorjsuren ◽  
C. Jamsran ◽  
N. Batsaikhan
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Growth Parameters ◽  
Remote Sensing Data ◽  
Crop Growth ◽  
Wheat Crop ◽  
Sensing Data ◽  
Application Method ◽  
Agricultural Application ◽  
Sentinel 2

Abstract. Wheat is the most important food crop in Mongolia, most of the croplands are utilizing for wheat cultivating area in the central northern region of Mongolia. The Mongolian government has several policies on the agricultural sector with wheat production in the study region has been intensified to meet people’s food demands and economic development. Monitoring wheat-growing areas is thus important to developing strategies for food security in the region. In the present study, we aimed to develop an agricultural application method using remote sensing data. Sentinel-1 SAR and Sentinel-2 MSI analysis of time series data were carried out to monitor the wheat crop growth parameters. Time-series images were acquired during May 2019–September 2019 at different growth stages in Bornuur soum, Tuv province of Mongolia. The wheat crop parameters, i.e. normalized difference vegetation index, vegetation water content, backscatter value of VV, VH channels were estimated using remote sensing data with reference data as cadastre polygons of current cropland area. The results showed that provide timely and valuable information for agricultural production, management and policy-making. The agricultural application method will help to agriculture management and monitoring include crop identification and cropland mapping, crop growth monitoring, inversion of key biophysical, biochemical and environmental parameters, crop damage/disaster monitoring, precision agriculture, etc.

scholarly journals SENTINEL 2 AND LANDSAT-8 BANDS SENSITIVITY ANALYSIS FOR MAPPING OF ALKALINE SOIL IN NORTHERN DRY ZONE OF KARNATAKA, INDIA

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 307-313 ◽  
Author(s):  
S. Meti ◽  
P. D. Lakshmi ◽  
M. S. Nagaraja ◽  
V. Shreepad ◽  
Keyword(s):  
High Resolution ◽  
Soil Ph ◽  
Satellite Data ◽  
Soil Salinity ◽  
Degradation Process ◽  
Landsat 8 ◽  
Alkaline Soil ◽  
Policy Makers ◽  
Dry Zone ◽  
Sentinel 2

<p><strong>Abstract.</strong> Soil salinization is most common land degradation process occurring in deep vertisol of northern dry zone of Karnataka, India. Accurate and high resolution spatial information on salinization can assist policy makers to better target areas for interventions to avoid aggravation of soil degradation process. Digital soil mapping using satellite data has been identified as a potential means of obtaining soil information. This paper focuses on exploring possibility of using new generation medium resolution Landsat-8 and Sentinel-2 satellite data to map alkaline soils of Ramthal irrigation project area in north Karnataka. Surface soil salinity parameters of zone 20 were correlated with reflectance values of different band and band combination and traditional salinity indices and result has indicated that SWIR bands of both satellite showed significant negative correlation with soil pH, EC (r&amp;thinsp;=&amp;thinsp;&amp;minus;0.39 to &amp;minus;0.45) whereas visible and NIR bands did not show significant relation. However rationing of SWIR bands with visible blue band has significantly improved the correlation with soil pH and EC (r&amp;thinsp;=&amp;thinsp;+0.60 to +0.70). Traditional salinity index based on visible bands failed to show significant correlation with soil parameters. It is interesting to note that SWIR bands alone did not show significant correlation with soil sodicity parameters like exchangeable Na, SAR, RSC but band rationing with blue bands has significantly improved the correlation (r&amp;thinsp;=&amp;thinsp;0.45). High resolution soil salinity map was prepared using simple linear regression model and using this map will serve as base map for the policy makers.</p>

EFFECT OF ROTATIONS, TILLAGE TREATMENTS AND FERTILIZERS ON THE AGGREGATION OF A CLAY SOIL

1971 ◽  
Vol 51 (2) ◽  
pp. 235-241 ◽  
Author(s):  
G. S. EMMOND
Keyword(s):  
Green Manure ◽  
Crop Residues ◽  
Surface Soil ◽  
Clay Soil ◽  
Soil Aggregation ◽  
Wheat Crop ◽  
Green Manure Crop

Soil aggregation was lowest in a fallow-wheat rotation and increased in other fallow-grain rotations with the second, third, and fourth crops after the fallow year. The best aggregation was under continuous wheat. Rotations containing hay crops, particularly those with grass, increased soil aggregation significantly. The influence of tillage treatments on soil aggregation declined with increased depth. Various tillage treatments affected surface soil aggregation, in the following order: green manure crop plowed under > cultivated with trash cover > crop residues plowed under > cultivated with residues burned off = crop residues disced in. Fertilizer (11–48–0) applied to the wheat crop of the various tillage treatments increased soil aggregation except where the crop residues had been removed. The application of barn manure increased soil aggregation.

scholarly journals Estimation of Wheat Area using Sentinel-1 and Sentinel-2 Datasets (A Comparative Analysis)

2019 ◽  
Author(s):  
Ayesha Behzad ◽  
Muneeb Aamir ◽  
Syed Ahmed Raza ◽  
Ansab Qaiser ◽  
Syeda Yuman Fatima ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Random Forest ◽  
Water Body ◽  
Supervised Classification ◽  
Wheat Crop ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Wheat Cultivation ◽  
Erdas Imagine ◽  
Sentinel 2

Wheat is the basic staple food, largely grown, widely used and highly demanded. It is used in multiple food products which are served as fundamental constituent to human body. Various regional economies are partially or fully dependent upon wheat production. Estimation of wheat area is essential to predict its contribution in regional economy. This study presents a comparative analysis of optical and active imagery for estimation of area under wheat cultivation. Sentinel-1 data was downloaded in Ground Range Detection (GRD) format and applied the Random Forest Classification using Sentinel Application Platform (SNAP) tools. We obtained a Sentinel-2 image for the month of March and applied supervised classification in Erdas Imagine 14. The random forest classification results of Sentinel-1 show that the total area under investigation was 1089km2 which was further subdivided in three classes including wheat (551km2), built-up (450 km2) and the water body (89 km2). Supervised classification results of Sentinel-2 data show that the area under wheat crop was 510 km2, however the built-up and waterbody were 477 km2, 102 km2 respectively. The integrated map of Sentinel-1 and Sentinel-2 show that the area under wheat was 531 km2 and the other features including water body and the built-up area were 95 km2 and 463 km2 respectively. We applied a Kappa coefficient to Sentinel-2, Sentinel-1 and Integrated Maps and found an accuracy of 71%, 78% and 85% respectively. We found that remotely sensed algorithms of classifications are reliable for future predictions.

scholarly journals Sentinel-1 and Sentinel-2 Data Fusion for Mapping and Monitoring Wetlands

2018 ◽  
Author(s):  
Gordana Kaplan ◽  
Ugur Avdan
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Classification Accuracy ◽  
Accuracy Assessment ◽  
Geographical Information ◽  
Wetland Classification ◽  
Significant Information ◽  
Data Set ◽  
Object Based ◽  
Sentinel 2

Wetlands benefits can be summarized but are not limited to their ability to store floodwaters and improve water quality, providing habitats for wildlife and supporting biodiversity, as well as aesthetic values. Over the past few decades, remote sensing and geographical information technologies has proven to be a useful and frequent applications in monitoring and mapping wetlands. Combining both optical and microwave satellite data can give significant information about the biophysical characteristics of wetlands and wetlands` vegetation. Also, fusing data from different sensors, such as radar and optical remote sensing data, can increase the wetland classification accuracy. In this paper we investigate the ability of fusion two fine spatial resolution satellite data, Sentinel-2 and the Synthetic Aperture Radar Satellite, Sentinel-1, for mapping wetlands. As a study area in this paper, Balikdami wetland located in the Anatolian part of Turkey has been selected. Both Sentinel-1 and Sentinel-2 images require pre-processing before their use. After the pre-processing, several vegetation indices calculated from the Sentinel-2 bands were included in the data set. Furthermore, an object-based classification was performed. For the accuracy assessment of the obtained results, number of random points were added over the study area. In addition, the results were compared with data from Unmanned Aerial Vehicle collected on the same data of the overpass of the Sentinel-2, and three days before the overpass of Sentinel-1 satellite. The accuracy assessment showed that the results significant and satisfying in the wetland classification using both multispectral and microwave data. The statistical results of the fusion of the optical and radar data showed high wetland mapping accuracy, with an overall classification accuracy of approximately 90% in the object-based classification. Compared with the high resolution UAV data, the classification results give promising results for mapping and monitoring not just wetlands, but also the sub-classes of the study area. For future research, multi-temporal image use and terrain data collection are recommended.

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