USING THE NORMALIZED DIFFERENCE VEGETATION INDEX AND A CROP SIMULATION MODEL TO PREDICT SOIL SPATIAL VARIABILITY

2002 ◽  
Vol 45 (4) ◽  
Author(s):  
J. I. Lizaso ◽  
W. D. Batchelor ◽  
M. E. Westgate
Keyword(s):  
Spatial Variability ◽  
Simulation Model ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Crop Simulation Model ◽  
Crop Simulation ◽  
Soil Spatial Variability

Optimizing Sugarcane Planting Windows Using a Crop Simulation Model at the State Level

2021 ◽  
Author(s):  
Jéssica Sousa Paixão ◽  
Derblai Casaroli ◽  
João Carlos Rocha dos Anjos ◽  
José Alves Júnior ◽  
Adão Wagner Pêgo Evangelista ◽  
...  
Keyword(s):  
Simulation Model ◽  
State Level ◽  
The State ◽  
Crop Simulation Model ◽  
Crop Simulation

Regional yield estimation using a crop simulation model: Concepts, methods, and validation

1994 ◽  
Vol 46 (1) ◽  
pp. 79-92 ◽  
Author(s):  
Thomas N. Moen ◽  
Harry M. Kaiser ◽  
Susan J. Riha
Keyword(s):  
Simulation Model ◽  
Yield Estimation ◽  
Crop Simulation Model ◽  
Crop Simulation

scholarly journals Integration of Soil Electrical Conductivity and Indices Obtained through Satellite Imagery for Differential Management of Pasture Fertilization

2019 ◽  
Vol 1 (4) ◽  
pp. 567-585 ◽  
Author(s):  
João Serrano ◽  
Shakib Shahidian ◽  
José Marques da Silva ◽  
Luís Paixão ◽  
José Calado ◽  
...  
Keyword(s):  
Electrical Conductivity ◽  
Spatial Variability ◽  
Satellite Imagery ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Temporal Stability ◽  
Soil Characteristics ◽  
Low Fertility ◽  
Soil Electrical Conductivity ◽  
Potential Productivity

Dryland pastures in the Alentejo region, located in the south of Portugal, normally occupy soils that have low fertility but, simultaneously, important spatial variability. Rational application of fertilizers requires knowledge of spatial variability of soil characteristics and crop response, which reinforces the interest of technologies that facilitates the identification of homogeneous management zones (HMZ). In this work, a pasture field of about 25 ha, integrated in the Montado mixed ecosystem (agro-silvo-pastoral), was monitored. Surveys of apparent soil electrical conductivity (ECa) were carried out in November 2017 and October 2018 with a Veris 2000 XA contact sensor. A total of 24 sampling points (30 × 30 m) were established in tree-free zones to allow readings of normalized difference vegetation index (NDVI) and normalized difference water index (NDWI). Historical time series of these indices were obtained from satellite imagery (Sentinel-2) in winter and spring 2017 and 2018. Three zones with different potential productivity were defined based on the results obtained in terms of spatial variability and temporal stability of the measured parameters. These are the basis for the elaboration of differentiated prescription maps of fertilizers with variable application rate technology, taking into account the variability of soil characteristics and pasture development, contributing to the sustainability of this ecosystem.

scholarly journals Spatial variability of vegetation index and soil properties in an integrated crop-livestock system

2017 ◽  
Vol 21 (8) ◽  
pp. 513-518 ◽  
Author(s):  
Alberto C. de C. Bernardi ◽  
Célia R. Grego ◽  
Ricardo G. Andrade ◽  
Ladislau M. Rabello ◽  
Ricardo Y. Inamasu
Keyword(s):  
Spatial Variability ◽  
Soil Properties ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Management Strategies ◽  
Exchange Capacity ◽  
Soil Parameters ◽  
Forage Production ◽  
Soil P ◽  
Livestock System

ABSTRACT The knowledge of soil property spatial variability is useful for determining the rational use of inputs, such as the site-specific application of lime and fertilizer. The objective of this study was to evaluate the vegetation index and spatial variability of physical and chemical soil properties in an integrated crop-livestock system (ICLS). Soil samples were taken from a 6.9 ha area in a regular hexagon grid at 0-0.20 m depths. Soil P, K, Ca, Mg, and cation exchange capacity - CEC; base saturation; clay and sand were analyzed. Soil electrical conductivity (ECa) was measured with a contact sensor. The site was evaluated at the end of the corn season (April) and during forage production (October) using Landsat 5 images, remote sensing techniques and a geographic information system (GIS). Results showed that the normalized difference vegetation index (NDVI) was associated with ECa and soil parameters, indicating crop and pasture variations in the ICLS. Geostatistics and GIS were effective tools for collecting data regarding the spatial variability of soil and crop indicators, identifying variation trends in the data, and assisting data interpretation to determine adequate management strategies.

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.

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