Yield Prediction Model for Potato Using Landsat Time Series Images Driven Vegetation Indices

2018 ◽  
Vol 1 (1-2) ◽  
pp. 29-38 ◽  
Author(s):  
Imran Hossain Newton ◽  
A. F. M Tariqul Islam ◽  
A. K. M. Saiful Islam ◽  
G. M. Tarekul Islam ◽  
Anika Tahsin ◽  
...  
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Vegetation Indices ◽  
Yield Prediction ◽  
Time Series Images

scholarly journals Estimation of rice phenology date using integrated HJ-1 CCD and Landsat-8 OLI vegetation indices time-series images

2015 ◽  
Vol 16 (10) ◽  
pp. 832-844 ◽  
Author(s):  
Jing Wang ◽  
Jing-feng Huang ◽  
Xiu-zhen Wang ◽  
Meng-ting Jin ◽  
Zhen Zhou ◽  
...  
Keyword(s):  
Time Series ◽  
Vegetation Indices ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Time Series Images ◽  
Rice Phenology

scholarly journals Wheat Yield Forecasting for the Tisza River Catchment Using Landsat 8 NDVI and SAVI Time Series and Reported Crop Statistics

Agronomy ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 652
Author(s):  
Attila Nagy ◽  
Andrea Szabó ◽  
Odunayo David Adeniyi ◽  
János Tamás
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Crop Production ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Wheat Yield ◽  
Landsat 8 ◽  
Tisza River ◽  
Yield Forecasting

Due to the increasing global demand of food grain, early and reliable information on crop production is important in decision making in agricultural production. Remote sensing (RS)-based forecast models developed from vegetation indices have the potential to give quantitative and timely information on crops for larger regions or even at farm scale. Different vegetation indices are being used for this purpose, however, their efficiency in estimating crop yield certainly needs to be tested. In this study, wheat yield was derived by linear regressing reported yield values against a time series of six different peak-seasons (2013–2018) using the Landsat 8-derived Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI). NDVI- and SAVI-based forecasting models were validated based on 2018–2019 datasets and compared to evaluate the most appropriate index that performs better in forecasting wheat production in the Tisza river basin. Nash-Sutcliffe efficiency index was positive with E1 = 0.716 for the model from NDVI and for SAVI E1 = 0.909, which means that the forecasting method developed and performed good forecast efficiency. The best time for wheat yield prediction with Landsat 8-SAVI and NDVI was found to be the beginning of full biomass period from the 138th to 167th day of the year (18 May to 16 June; BBCH scale: 41–71) with high regression coefficients between the vegetation indices and the wheat yield. The RMSE of the NDVI-based prediction model was 0.357 t/ha (NRMSE: 7.33%). The RMSE of the SAVI-based prediction model was 0.191 t/ha (NRMSE 3.86%). The validation of the results revealed that the SAVI-based model provided more accurate forecasts compared to NDVI. Overall, probable yield amount is possible to predict far before harvest (six weeks earlier) based on Landsat 8 NDVI and SAVI and generating simple thresholds for yield forecasting, and a potential loss of wheat yield can be mapped.

Cross-sensor comparisons between Landsat 5 TM and IRS-P6 AWiFS and disturbance detection using integrated Landsat and AWiFS time-series images

2012 ◽  
Vol 34 (7) ◽  
pp. 2432-2453 ◽  
Author(s):  
Xuexia Chen ◽  
James E. Vogelmann ◽  
Gyanesh Chander ◽  
Lei Ji ◽  
Brian Tolk ◽  
...  
Keyword(s):  
Time Series ◽  
Irs P6 ◽  
Landsat 5 Tm ◽  
Time Series Images ◽  
Disturbance Detection

LamaH: Large-sample Data for Hydrology in Central Europe

2021 ◽  
Author(s):  
Christoph Klingler ◽  
Mathew Herrnegger ◽  
Frederik Kratzert ◽  
Karsten Schulz
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Vegetation Indices ◽  
River Network ◽  
Series Data ◽  
High Temporal Resolution ◽  
Stream Length ◽  
Discharge Data ◽  
Large Sample ◽  
Access To Data

<p>Open large-sample datasets are important for various reasons: i) they enable large-sample analyses, ii) they democratize access to data, iii) they enable large-sample comparative studies and foster reproducibility, and iv) they are a key driver for recent developments of machine-learning based modelling approaches.</p><p>Recently, various large-sample datasets have been released (e.g. different country-specific CAMELS datasets), however, all of them contain only data of individual catchments distributed across entire countries and not connected river networks.</p><p>Here, we present LamaH, a new dataset covering all of Austria and the foreign upstream areas of the Danube, spanning a total of 170.000 km² in 9 different countries with discharge observations for 882 gauges. The dataset also includes 15 different meteorological time series, derived from ERA5-Land, for two different basin delineations: First, corresponding to the entire upstream area of a particular gauge, and second, corresponding only to the area between a particular gauge and its upstream gauges. The time series data for both, meteorological and discharge data, is included in hourly and daily resolution and covers a period of over 35 years (with some exceptions in discharge data for a couple of gauges).</p><p>Sticking closely to the CAMELS datasets, LamaH also contains more than 60 catchment attributes, derived for both types of basin delineations. The attributes include climatic, hydrological and vegetation indices, land cover information, as well as soil, geological and topographical properties. Additionally, the runoff gauges are classified by over 20 different attributes, including information about human impact and indicators for data quality and completeness. Lastly, LamaH also contains attributes for the river network itself, like gauge topology, stream length and the slope between two sequential gauges.</p><p>Given the scope of LamaH, we hope that this dataset will serve as a solid database for further investigations in various tasks of hydrology. The extent of data combined with the interconnected river network and the high temporal resolution of the time series might reveal deeper insights into water transfer and storage with appropriate methods of modelling.</p>

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