Field scale spatial wheat yield forecasting system under limited field data availability by integrating crop simulation model with weather forecast and satellite remote sensing

2022 ◽  
Vol 195 ◽  
pp. 103299
Author(s):  
Rajkumar Dhakar ◽  
Vinay Kumar Sehgal ◽  
Debasish Chakraborty ◽  
Rabi Narayan Sahoo ◽  
Joydeep Mukherjee ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Simulation Model ◽  
Satellite Remote Sensing ◽  
Field Data ◽  
Wheat Yield ◽  
Weather Forecast ◽  
Data Availability ◽  
Field Scale ◽  
Crop Simulation ◽  
Forecasting System

scholarly journals An Improved CASA Model for Estimating Winter Wheat Yield from Remote Sensing Images

2019 ◽  
Vol 11 (9) ◽  
pp. 1088 ◽  
Author(s):  
Yulong Wang ◽  
Xingang Xu ◽  
Linsheng Huang ◽  
Guijun Yang ◽  
Lingling Fan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Winter Wheat ◽  
Crop Yield ◽  
Satellite Remote Sensing ◽  
Wheat Yield ◽  
Growing Season ◽  
Remote Sensing Images ◽  
Casa Model ◽  
Winter Wheat Yield

The accurate and timely monitoring and evaluation of the regional grain crop yield is more significant for formulating import and export plans of agricultural products, regulating grain markets and adjusting the planting structure. In this study, an improved Carnegie–Ames–Stanford approach (CASA) model was coupled with time-series satellite remote sensing images to estimate winter wheat yield. Firstly, in 2009 the entire growing season of winter wheat in the two districts of Tongzhou and Shunyi of Beijing was divided into 54 stages at five-day intervals. Net Primary Production (NPP) of winter wheat was estimated by the improved CASA model with HJ-1A/B satellite images from 39 transits. For the 15 stages without HJ-1A/B transit, MOD17A2H data products were interpolated to obtain the spatial distribution of winter wheat NPP at 5-day intervals over the entire growing season of winter wheat. Then, an NPP-yield conversion model was utilized to estimate winter wheat yield in the study area. Finally, the accuracy of the method to estimate winter wheat yield with remote sensing images was verified by comparing its results to the ground-measured yield. The results showed that the estimated yield of winter wheat based on remote sensing images is consistent with the ground-measured yield, with R2 of 0.56, RMSE of 1.22 t ha−1, and an average relative error of −6.01%. Based on time-series satellite remote sensing images, the improved CASA model can be used to estimate the NPP and thereby the yield of regional winter wheat. This approach satisfies the accuracy requirements for estimating regional winter wheat yield and thus may be used in actual applications. It also provides a technical reference for estimating large-scale crop yield.

scholarly journals Satellite-Based Drought Impact Assessment on Rice Yield in Thailand with SIMRIW−RS

2020 ◽  
Vol 12 (13) ◽  
pp. 2099
Author(s):  
Mongkol Raksapatcharawong ◽  
Watcharee Veerakachen ◽  
Koki Homma ◽  
Masayasu Maki ◽  
Kazuo Oki
Keyword(s):  
Remote Sensing ◽  
Simulation Model ◽  
Rice Yield ◽  
Drought Monitoring ◽  
Weather Data ◽  
Area Index ◽  
Drought Impact ◽  
Crop Simulation Model ◽  
Crop Simulation ◽  
Rice Yields

Advances in remote sensing technologies have enabled effective drought monitoring globally, even in data-limited areas. However, the negative impact of drought on crop yields still necessitates stakeholders to make informed decisions according to its severity. This research proposes an algorithm to combine a drought monitoring model, based on rainfall, land surface temperature (LST), and normalized difference vegetation index/leaf area index (NDVI/LAI) satellite products, with a crop simulation model to assess drought impact on rice yields in Thailand. Typical crop simulation models can provide yield information, but the requirement for a complicated set of inputs prohibits their potential due to insufficient data. This work utilizes a rice crop simulation model called the Simulation Model for Use with Remote Sensing (SIMRIW–RS), whose inputs can mostly be satisfied by such satellite products. Based on experimental data collected during the 2018/19 crop seasons, this approach can successfully provide a drought monitoring function as well as effectively estimate the rice yield with mean absolute percentage error (MAPE) around 5%. In addition, we show that SIMRIW–RS can reasonably predict the rice yield when historical weather data is available. In effect, this research contributes a methodology to assess the drought impact on rice yields on a farm to regional scale, relevant to crop insurance and adaptation schemes to mitigate climate change.

scholarly journals CLIMATIC VARIABILITY PREDICTION WITH SATELLITE REMOTE SENSING AND METEOROLOGICAL DATA IN THE SOUTH WESTERN NIGERIA

2013 ◽  
Vol 39 (2) ◽  
pp. 59-63
Author(s):  
Ebenezer Yemi Ogunbadewa
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Spatial Scales ◽  
Climatic Variability ◽  
Data Availability ◽  
The South ◽  
Suitable Alternative ◽  
Temporal And Spatial ◽  
Satellite Sensor

Climatic variability affects both seasonal phenological cycles of vegetation and monthly distribution of rainfall in the south western Nigeria. Variations in vegetation biophysical parameters have been known to be a good indicator of climate variability; hence they are used as key inputs into climate change models. However, understanding the response of vegetation to the influence of climate at both temporal and spatial scales have been a major challenge. This is because most climatic data available are derived from ground-based instruments, which are mainly point measurements and are characterized by sparse network of meteorological stations that lacks the spatial coverage required for climate change investigation. Satellite remote sensing instruments can provide a suitable alternative of time-reliable datasets in a more consistent manner at both temporal and spatial scales. The aim of this study is to test the suitability of one year time series datasets obtained from satellite sensor and meteorological stations as a starting point for the development of a climate change model that can be exploited in planning adaptation strategies. Taking into consideration that rainfall is the most variable element of climate in the study area, rainfall data acquired from five meteorological stations for the year 2006 were correlated with changes in Normalized Difference Vegetation Index (NDVI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra satellite sensor for the same period using a linear regression equation. The results shows that rainfall–NDVI relationship was stronger along the seasonal track with R2 ranging from 0.74 to 0.94, indicating that NDVI seasonal variations can be used as a surrogate data source for monitoring climate change for short and long term scales ranging from regional to global magnitude especially in areas where data availability from ground-based measurements are unreliable.

Forecasting wheat yield in Punjab state of India by combining crop simulation model WOFOST and remotely sensed inputs

2013 ◽  
Vol 4 (1) ◽  
pp. 19-28 ◽  
Author(s):  
Rojalin Tripathy ◽  
Karshan N. Chaudhari ◽  
Joydeep Mukherjee ◽  
Shibendu S. Ray ◽  
N. K. Patel ◽  
...  
Keyword(s):  
Simulation Model ◽  
Wheat Yield ◽  
Remotely Sensed ◽  
Crop Simulation Model ◽  
Crop Simulation

Forecasting Wheat Yield Using Remote Sensing: The ARYA Forecasting System

2021 ◽  
Author(s):  
B. Franch ◽  
E. Vermote ◽  
S. Skakun ◽  
A. Santamaria-Artigas ◽  
N. Kalecinski ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Wheat Yield ◽  
Forecasting System

AN UNSUPERVISED SOIL MOISTURE ESTIMATION MODELLING APPROACH USING C-BAND DUAL PolSAR DATA

2020 ◽  
Author(s):  
Reet Kamal Tiwari ◽  
Akshar Tripathi
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Field Data ◽  
Microwave Remote Sensing ◽  
Data Availability ◽  
Series Data ◽  
Soil Conditions ◽  
Decomposition Models ◽  
Sar Data ◽  
Moisture Estimation

<p>With the advent of remote sensing and its widescale implementation in the field of agriculture and soil studies, today remote sensing has become an integral non-evasive analysis and research tool. After decades of research with conventional optical remote sensing, both airborne and spaceborne, a need was felt to have an all-weather remote sensing data availability. Spaceborne SAR (Synthetic Aperture RADAR) or microwave remote sensing with its all-weather availability and high temporal resolution, owing to its penetration capabilities has been found highly suitable for the soil and crop health studies. Since, SAR remote sensing is highly sensitive to surface roughness and dielectrics in dry and moist soil conditions respectively, it becomes highly important to study and observe the variations of these properties in various polarisation channels. PolSAR (Polarimetric SAR) data with its different decomposition models has an advantage over conventional SAR data since it uses more than one polarisation channels and polarimetric decomposition models which consider several soil and crop parameters. This helps to study the RADAR wave interaction with the target easier. This helps in the proper and better study and understanding of retrieval of soil moisture and analysis of its variation over time. This study makes use of C-band Sentinel 1A satellite dual PolSAR, time series data of VV and VH polarisations. The datasets used are that of pre-monsoon and monsoon period of 2019, February to May respectively for Rupnagar area. In this study it has been aimed to model for retrieval of soil moisture based on RADAR backscatter values and Normalised Differential Moisture Indices values from Sentinel-1A and Sentinel 2 satellite imageries respectively. The process has been performed on both VV and VH polarisations and the results are analysed for both the time periods. Theoretically, it has been observed that VH polarisation yields better and nearer to ground truth results with least Root Mean Squared Error (RMSE) of 0.05 and high R<sup>2</sup>-Squared statistics of 0.72 (72%) in training and testing. This study aims at unsupervised modelling using satellite datasets for model development, training and validation and without the input of field data. The results though not very good yet give an idea of soil moisture estimation and is highly beneficial for areas and conditions when field validations and data collection is difficult or not possible. This study also aims at reducing field validation dependence. Once integrated with field data, accuracy is expected to increase.</p>

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