scholarly journals UP-SCALING PADDY YIELD AT SATELLITE-FOOTPRINT SCALE USING SATELLITE DATA IN CONJUNCTION WITH CCE DATA IN SAHIBGANJ DISTRICT, JHARKHAND

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 235-238 ◽  
Author(s):  
B. R. Parida ◽  
A. K. Ranjan
Keyword(s):  
Satellite Data ◽  
Vegetation Index ◽  
Monsoon Season ◽  
Survey Method ◽  
Yield Model ◽  
Yield Data ◽  
Time Saving ◽  
Aquacrop Model ◽  
Paddy Yield ◽  
Up Scaling

<p><strong>Abstract.</strong> Agriculture plays a vital role in the economy of India as almost half of the workforce dependent on agriculture and allied activities. Rice is an important staple food and provides nutritious need for the billions of population. Mapping the spatial distribution of paddy and predicting yields at district level aggregation are crucial for food security measures. This study has utilized the time-series MODIS-based Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) data in conjunction with CCE data to derive a statistical model for up-scaling paddy yield at satellite-footprint scale over Sahibganj district in Kharif (monsoon) season 2017. The CCE data were collected from ten random paddy plots. In addition, Area, Production, and Yield (APY) data were collected during harvesting period by interacting with eighty farmers belong to eight villages. The AquaCrop model was also used to simulate the paddy yield for Kharif season. The key results showed that based on the farmers-based yield data, paddy yield was observed as ~3200&amp;thinsp;kg/hectare, whereas, NDVI and EVI-based yield models based on satellite data showed about 2,960 and 3,530 kg/hectare, respectively. Moreover, multi-regression-based yield model showed the mean yield of 3,070&amp;thinsp;kg/hectare. With respect to farmers-level yield data, the relative deviation (RD) of yield based on NDVI data was &amp;minus;7.5% (underestimation), while EVI was 10.31% (overestimation). The multi-regression-based yield model and AquaCrop model were underestimated by &amp;minus;4.06 and &amp;minus;10.16%, respectively. Thus, it can be inferred that the multi-regression-based yield was close to farmers-based survey yields. It can be concluded that the satellite databased yield prediction can be reliable with &amp;plusmn;&amp;thinsp;10% of RD. Nevertheless, remote sensing technology can be beneficial over traditional survey method as the satellite-based methods are cost-effective, robust, reliable, and time-saving than the traditional methods.</p>

Synergy of Satellite-Derived Drought Indices for Agricultural Drought Quantification and Yield Prediction

2020 ◽  
pp. 122-148
Author(s):  
Dipti Ladli ◽  
Kanhaiya Lal ◽  
Kiran Jalem ◽  
Avinash Kumar Ranjan
Keyword(s):  
Condition Index ◽  
District Level ◽  
Drought Indices ◽  
Yield Prediction ◽  
Drought Condition ◽  
Yield Model ◽  
Yield Data ◽  
Moderate Drought ◽  
Vegetation Health Index ◽  
Paddy Yield

The present study was conducted over Jharkhand state (India) for assessing the drought condition and corresponding yield of paddy (district-level) during Kharif 2018. Vegetation drought indices, namely Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), and vegetation indices (VI) anomaly, were derived from different VI (i.e., NDVI, EVI) to assess the paddy health condition during drought year (2018) and non-drought year (2017). Later, the correlation between the DES-based yield data and derived drought indices (for the year 2017) were made to develop the district-level paddy yield model for the drought year 2018. The key results of the study shown that VCI derived from EVI data was found to be more reasonable to depict the drought condition, wherein ~21% area was under severe drought condition, 43% area under moderate drought condition, and 36% area under no drought condition. In addition, the yield prediction model derived from VCI (EVI-based) was found to be promising for predicting the paddy yield for Kharif 2018 with fair R2 of 0.53.

scholarly journals Modeling of Durum Wheat Yield Based on Sentinel-2 Imagery

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1486
Author(s):  
Chris Cavalaris ◽  
Sofia Megoudi ◽  
Maria Maxouri ◽  
Konstantinos Anatolitis ◽  
Marios Sifakis ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Model Development ◽  
Wheat Yield ◽  
Growth Period ◽  
Yield Data ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Sentinel 2

In this study, a modelling approach for the estimation/prediction of wheat yield based on Sentinel-2 data is presented. Model development was accomplished through a two-step process: firstly, the capacity of Sentinel-2 vegetation indices (VIs) to follow plant ecophysiological parameters was established through measurements in a pilot field and secondly, the results of the first step were extended/evaluated in 31 fields, during two growing periods, to increase the applicability range and robustness of the models. Modelling results were examined against yield data collected by a combine harvester equipped with a yield-monitoring system. Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were examined as plant signals and combined with Normalized Difference Water Index (NDWI) and/or Normalized Multiband Drought Index (NMDI) during the growth period or before sowing, as water and soil signals, respectively. The best performing model involved the EVI integral for the 20 April–31 May period as a plant signal and NMDI on 29 April and before sowing as water and soil signals, respectively (R2 = 0.629, RMSE = 538). However, model versions with a single date and maximum seasonal VIs values as a plant signal, performed almost equally well. Since the maximum seasonal VIs values occurred during the last ten days of April, these model versions are suitable for yield prediction.

scholarly journals Prediction of Maize Yield at the City Level in China Using Multi-Source Data

2021 ◽  
Vol 13 (1) ◽  
pp. 146
Author(s):  
Xinxin Chen ◽  
Lan Feng ◽  
Rui Yao ◽  
Xiaojun Wu ◽  
Jia Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Satellite Data ◽  
Vegetation Index ◽  
Maize Yield ◽  
Machine Learning Algorithms ◽  
Shortwave Radiation ◽  
Growth Stages ◽  
Climate Data ◽  
Early Peak ◽  
The City

Maize is a widely grown crop in China, and the relationships between agroclimatic parameters and maize yield are complicated, hence, accurate and timely yield prediction is challenging. Here, climate, satellite data, and meteorological indices were integrated to predict maize yield at the city-level in China from 2000 to 2015 using four machine learning approaches, e.g., cubist, random forest (RF), extreme gradient boosting (Xgboost), and support vector machine (SVM). The climate variables included the diffuse flux of photosynthetic active radiation (PDf), the diffuse flux of shortwave radiation (SDf), the direct flux of shortwave radiation (SDr), minimum temperature (Tmn), potential evapotranspiration (Pet), vapor pressure deficit (Vpd), vapor pressure (Vap), and wet day frequency (Wet). Satellite data, including the enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and adjusted vegetation index (SAVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), were used. Meteorological indices, including growing degree day (GDD), extreme degree day (EDD), and the Standardized Precipitation Evapotranspiration Index (SPEI), were used. The results showed that integrating all climate, satellite data, and meteorological indices could achieve the highest accuracy. The highest estimated correlation coefficient (R) values for the cubist, RF, SVM, and Xgboost methods were 0.828, 0.806, 0.742, and 0.758, respectively. The climate, satellite data, or meteorological indices inputs from all growth stages were essential for maize yield prediction, especially in late growth stages. R improved by about 0.126, 0.117, and 0.143 by adding climate data from the early, peak, and late-period to satellite data and meteorological indices from all stages via the four machine learning algorithms, respectively. R increased by 0.016, 0.016, and 0.017 when adding satellite data from the early, peak, and late stages to climate data and meteorological indices from all stages, respectively. R increased by 0.003, 0.032, and 0.042 when adding meteorological indices from the early, peak, and late stages to climate and satellite data from all stages, respectively. The analysis found that the spatial divergences were large and the R value in Northwest region reached 0.942, 0.904, 0.934, and 0.850 for the Cubist, RF, SVM, and Xgboost, respectively. This study highlights the advantages of using climate, satellite data, and meteorological indices for large-scale maize yield estimation with machine learning algorithms.

scholarly journals A GNSS-IR Method for Retrieving Soil Moisture Content from Integrated Multi-Satellite Data That Accounts for the Impact of Vegetation Moisture Content

2021 ◽  
Vol 13 (13) ◽  
pp. 2442
Author(s):  
Jichao Lv ◽  
Rui Zhang ◽  
Jinsheng Tu ◽  
Mingjie Liao ◽  
Jiatai Pang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Satellite Data ◽  
Vegetation Index ◽  
Soil Moisture Content ◽  
Support Vector ◽  
Observation Data ◽  
Multipath Effect ◽  
Mars Model ◽  
The Impact

There are two problems with using global navigation satellite system-interferometric reflectometry (GNSS-IR) to retrieve the soil moisture content (SMC) from single-satellite data: the difference between the reflection regions, and the difficulty in circumventing the impact of seasonal vegetation growth on reflected microwave signals. This study presents a multivariate adaptive regression spline (MARS) SMC retrieval model based on integrated multi-satellite data on the impact of the vegetation moisture content (VMC). The normalized microwave reflection index (NMRI) calculated with the multipath effect is mapped to the normalized difference vegetation index (NDVI) to estimate and eliminate the impact of VMC. A MARS model for retrieving the SMC from multi-satellite data is established based on the phase shift. To examine its reliability, the MARS model was compared with a multiple linear regression (MLR) model, a backpropagation neural network (BPNN) model, and a support vector regression (SVR) model in terms of the retrieval accuracy with time-series observation data collected at a typical station. The MARS model proposed in this study effectively retrieved the SMC, with a correlation coefficient (R2) of 0.916 and a root-mean-square error (RMSE) of 0.021 cm3/cm3. The elimination of the vegetation impact led to 3.7%, 13.9%, 11.7%, and 16.6% increases in R2 and 31.3%, 79.7%, 49.0%, and 90.5% decreases in the RMSE for the SMC retrieved by the MLR, BPNN, SVR, and MARS model, respectively. The results demonstrated the feasibility of correcting the vegetation changes based on the multipath effect and the reliability of the MARS model in retrieving the SMC.

scholarly journals Estimación de la cosecha de trigo en Andalucía usando series temporales de MERIS Terrestrial Chlorophyll Index (MTCI)

2018 ◽  
pp. 99 ◽  
Author(s):  
V. Egea-Cobrero ◽  
V. Rodriguez-Galiano ◽  
E. Sánchez-Rodríguez ◽  
M.A. García-Pérez
Keyword(s):  
Vegetation Index ◽  
Net Primary Production ◽  
Vegetation Indices ◽  
Yield Data ◽  
Imaging Spectrometer ◽  
Wheat Production ◽  
Production Studies ◽  
Normalised Difference Vegetation Index ◽  
Chlorophyll Index ◽  
The One

<p>There is a relationship between net primary production of wheat and vegetation indices obtained from satellite imaging. Most wheat production studies use the Normalised Difference Vegetation Index (NDVI) to estimate the production and yield of wheat and other crops. On the one hand, few studies use the MERIS Terrestrial Chlorophyll Index (MTCI) to determine crop yield and production on a regional level. This is possibly due to a lack of continuity of MERIS. On the other hand, the emergence of Sentinel 2 open new possibilities for the research and application of MTCI. This study has built two empirical models to estimate wheat production and yield in Andalusia. To this end, the study used the complete times series (weekly images from 2006–2011) of the MTCI vegetation index from the Medium Resolution Imaging Spectrometer (MERIS) sensor associated with the Andalusian yearbook for agricultural and fishing statistics (AEAP—Anuario de estadísticas agrarias y pesqueras de Andalucía). In order to build these models, the optimal development period for the plant needed to be identified, as did the time-based aggregation of MTCI values using said optimal period as a reference, and relation with the index, with direct observations of production and yield through spatial aggregation using coverage from the Geographic Information System for Agricultural Parcels (SIGPAC—Sistema de información geográfica de parcelas agrícolas) and requests for common agricultural policy (CAP) assistance. The obtained results indicate a significant association between the MTCI index and the production and yield data collected by AEAP at the 95% confidence level (R<sup>2</sup> =0.81 and R<sup>2</sup> =0.57, respectively).</p>

scholarly journals Dragonflies and damselflies (Insecta: Odonata) of the Kole Wetlands, central Kerala, India

2021 ◽  
Vol 13 (3) ◽  
pp. 17963-17971
Author(s):  
A. Vivek Chandran ◽  
Subin K. Jose ◽  
Sujith V. Gopalan
Keyword(s):  
Species Richness ◽  
Monsoon Season ◽  
Survey Method ◽  
Ramsar Site ◽  
Post Monsoon ◽  
Post Monsoon Season

A year-long study was conducted at the Kole Wetlands, a Ramsar site in central Kerala to document the diversity of dragonflies and damselflies and understand their seasonality.  Checklist survey method was used to sample adult odonates in 30 randomly chosen locations.  A total of 44 species (30 dragonflies and 14 damselflies) belonging to 33 genera and eight families were recorded in the study area.  Species richness showed a peak in the post-monsoon season and a dip in the summer.  The observations support the value of the Kole Wetlands in providing valuable resources for Odonata.

Techniques to Predict Agricultural Droughts

2005 ◽  
Author(s):  
Zekai Şen
Keyword(s):  
Crop Yield ◽  
Satellite Data ◽  
Wheat Yield ◽  
Weather Variables ◽  
Short Term ◽  
Yield Model ◽  
Regression Techniques ◽  
The Difference ◽  
Effects Of Drought

In general, the techniques to predict drought include statistical regression, time series, stochastic (or probabilistic), and, lately, pattern recognition techniques. All of these techniques require that a quantitative variable be identified to define drought, with which to begin the process of prediction. In the case of agricultural drought, such a variable can be the yield (production per unit area) of the major crop in a region (Kumar, 1998; Boken, 2000). The crop yield in a year can be compared with its long-term average, and drought intensity can be classified as nil, mild, moderate, severe, or disastrous, based on the difference between the current yield and the average yield. Regression techniques estimate crop yields using yield-affecting variables. A comprehensive list of possible variables that affect yield is provided in chapter 1. Usually, the weather variables routinely available for a historical period that significantly affect the yield are included in a regression analysis. Regression techniques using weather data during a growing season produce short-term estimates (e.g., Sakamoto, 1978; Idso et al., 1979; Slabbers and Dunin, 1981; Diaz et al., 1983; Cordery and Graham, 1989; Walker, 1989; Toure et al., 1995; Kumar, 1998). Various researchers in different parts of the world (see other chapters) have developed drought indices that can also be included along with the weather variables to estimate crop yield. For example, Boken and Shaykewich (2002) modifed the Western Canada Wheat Yield Model (Walker, 1989) drought index using daily temperature and precipitation data and advanced very high resolution radiometer (AVHRR) satellite data. The modified model improved the predictive power of the wheat yield model significantly. Some satellite data-based variables that can be used to predict crop yield are described in chapters 5, 6, 9, 13, 19, and 28. The short-term estimates are available just before or around harvest time. But many times long-term estimates are required to predict drought for next year, so that long-term planning for dealing with the effects of drought can be initiated in time.

scholarly journals Curriculum Evaluation in Online Education: The case of Teacher Candidates Preparing Online for Public Personnel Selection Examination

2018 ◽  
Vol 7 (2) ◽  
pp. 107
Author(s):  
Ömer Cem Karacaoğlu
Keyword(s):  
Online Education ◽  
Personnel Selection ◽  
Teacher Candidates ◽  
Qualitative Data ◽  
Curriculum Evaluation ◽  
Reliability And Validity ◽  
The Other ◽  
Survey Method ◽  
Time Saving ◽  
Online Curriculum

The aim of the present study is to evaluate the efficiency of an online curriculum based on the views of lecturers and students enrolled in the program. The study is mainly based on survey method. In order to collect qualitative data, interviews forms developed by the researcher were used. The reliability and validity of the interview forms were checked by experts of the field. The qualitative data was analyzed through content analysis. In the first place, data was coded, the themes emerged, the codes and themes were arranged by the researcher. As a final step, the findings were coded and interpreted. Based on the findings of the study, the strengths and weaknesses of the online curriculum were identified and a number of suggestions were offered based on the findings. The results of the study indicate that both learners and lecturers believe that online education is beneficial as well as productive and they are satisfied with the process. Online education was found to be preferable because it is time-saving, more economical, and flexible. On the other hand, limited interaction, unsustainable motivation, and problems caused by insufficient infrastructure were found to be negative aspects of online curriculum.

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