scholarly journals Crop modelling and remote sensing for yield prediction

1995 ◽  
Vol 43 (2) ◽  
pp. 143-161 ◽  
Author(s):  
B.A.M. Bouman
Keyword(s):  
Remote Sensing ◽  
Growth Models ◽  
Remote Sensing Data ◽  
Ground Cover ◽  
Crop Growth ◽  
Model Parameters ◽  
Optical Remote Sensing ◽  
Sensing Data ◽  
Forcing Function ◽  
Crop Growth Models

Methods for the application of crop growth models, remote sensing and their integrative use for yield forecasting and prediction are presented. First, the general principles of crop growth models are explained. When crop simulation models are used on regional scales, uncertainty and spatial variation in model parameters can result in broad bands of simulated yield. Remote sensing can be used to reduce some of this uncertainty. With optical remote sensing, standard relations between the Weighted Difference Vegetation Index and fraction ground cover and LAI were established for a number of crops. The radar backscatter of agricultural crops was found to be largely affected by canopy structure, and, for most crops, no consistent relationships with crop growth indicators were established. Two approaches are described to integrate remote sensing data with crop growth models. In the first one, measures of light interception (ground cover, LAI) estimated from optical remote sensing are used as forcing function in the models. In the second method, crop growth models are extended with remote sensing sub-models to simulate time-series of optical and radar remote sensing signals. These simulated signals are compared to measured signals, and the crop growth model is re-calibrated to match simulated with measured remote sensing data. The developed methods resulted in increased accuracy in the simulation of crop growth and yield of wheat and sugar beet in a number of case-studies.

scholarly journals Integrating Crop Growth Models with Remote Sensing for Predicting Biomass Yield of Sorghum

2021 ◽  
Author(s):  
Kai-Wei Yang ◽  
Scott Chapman ◽  
Neal Carpenter ◽  
Graeme Hammer ◽  
Greg McLean ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Plant Breeding ◽  
Growth Models ◽  
Remote Sensing Data ◽  
Crop Growth ◽  
Weather Data ◽  
Crop Models ◽  
Geographical Regions ◽  
Crop Growth Models ◽  
Photoperiod Sensitive

Abstract Plant phenotypes are often descriptive, rather than predictive of crop performance. As a result, extensive testing is required in plant breeding programs to develop varieties aimed at performance in the target environments. Crop models can improve this testing regime by providing a predictive framework to (1) augment field phenotyping data and derive hard-to-measure phenotypes and (2) estimate performance across geographical regions using historical weather data. The goal of this study was to parameterize the Agricultural Production Systems sIMulator (APSIM) crop growth models with remote sensing and ground reference data to predict variation in phenology and yield-related traits in 18 commercial grain and biomass sorghum hybrids. Genotype parameters for each hybrid were estimated using remote sensing measurements combined with manual phenotyping in West Lafayette, Indiana in 2018. The models were validated in hybrid performance trials in two additional seasons at that site and against yield trials conducted in Bushland, Texas between 2001 and 2018. These trials demonstrated that (1) maximum plant height, final dry biomass, and radiation use efficiency (RUE) of photoperiod sensitive and insensitive forage sorghum hybrids tended to be higher than observed in grain sorghum, (2) photoperiod sensitive sorghum hybrids exhibited greater biomass production in longer growing environments, and (3) the parameterized and validated models perform well in above ground biomass simulations across years and locations. Crop growth models that integrate remote sensing data offer an efficient approach to parameterise larger plant breeding populations.

scholarly journals Rapid Evaluation and Validation Method of Above Ground Forest Biomass Estimation Using Optical Remote Sensing in Tundi Reserved Forest Area, India

2021 ◽  
Vol 10 (1) ◽  
pp. 29
Author(s):  
Praveen Kumar ◽  
Akhouri P. Krishna ◽  
Thorkild M. Rasmussen ◽  
Mahendra K. Pal
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Remote Sensing Data ◽  
Test Site ◽  
Forest Area ◽  
Relational Model ◽  
Biomass Estimation ◽  
Optical Remote Sensing ◽  
Ground Sample ◽  
Sensing Data

Optical remote sensing data are freely available on a global scale. However, the satellite image processing and analysis for quick, accurate, and precise forest above ground biomass (AGB) evaluation are still challenging and difficult. This paper is aimed to develop a novel method for precise, accurate, and quick evaluation of the forest AGB from optical remote sensing data. Typically, the ground forest AGB was calculated using an empirical model from ground data for biophysical parameters such as tree density, height, and diameter at breast height (DBH) collected from the field at different elevation strata. The ground fraction of vegetation cover (FVC) in each ground sample location was calculated. Then, the fraction of vegetation cover (FVC) from optical remote sensing imagery was calculated. In the first stage of method implementation, the relation model between the ground FVC and ground forest AGB was developed. In the second stage, the relational model was established between image FVC and ground FVC. Finally, both models were fused to derive the relational model between image FVC and forest AGB. The validation of the developed method was demonstrated utilizing Sentinel-2 imagery as test data and the Tundi reserved forest area located in the Dhanbad district of Jharkhand state in eastern India was used as the test site. The result from the developed model was ground validated and also compared with the result from a previously developed crown projected area (CPA)-based forest AGB estimation approach. The results from the developed approach demonstrated superior capabilities in precision compared to the CPA-based method. The average forest AGB estimation of the test site obtained by this approach revealed 463 tons per hectare, which matches the previous estimate from this test site.

scholarly journals Some Peculiarities of Arable Soil Organic Matter Detection Using Optical Remote Sensing Data

2021 ◽  
Vol 13 (12) ◽  
pp. 2313
Author(s):  
Elena Prudnikova ◽  
Igor Savin
Keyword(s):  
Remote Sensing ◽  
Organic Matter ◽  
Soil Organic Matter ◽  
Spectral Reflectance ◽  
Remote Sensing Data ◽  
Soil Surface ◽  
Optical Remote Sensing ◽  
Test Field ◽  
Sensing Data ◽  
Sentinel 2

Optical remote sensing only provides information about the very thin surface layer of soil. Rainfall splash alters soil surface properties and its spectral reflectance. We analyzed the impact of rainfall on the success of soil organic matter (SOM) content (% by mass) detection and mapping based on optical remote sensing data. The subject of the study was the arable soils of a test field located in the Tula region (Russia), their spectral reflectance, and Sentinel-2 data. Our research demonstrated that rainfall negatively affects the accuracy of SOM predictions based on Sentinel-2 data. Depending on the average precipitation per day, the R2cv of models varied from 0.67 to 0.72, RMSEcv from 0.64 to 1.1% and RPIQ from 1.4 to 2.3. The incorporation of information on the soil surface state in the model resulted in an increase in accuracy of SOM content detection based on Sentinel-2 data: the R2cv of the models increased up to 0.78 to 0.84, the RMSEcv decreased to 0.61 to 0.71%, and the RPIQ increased to 2.1 to 2.4. Further studies are necessary to identify how the SOM content and composition of the soil surface change under the influence of rainfall for other soils, and to determine the relationships between rainfall-induced SOM changes and soil surface spectral reflectance.

scholarly journals Estimation of rice yield by SIMRIW-RS, a model that integrates remote sensing data into a crop growth model

2017 ◽  
Vol 73 (1) ◽  
pp. 2-8 ◽  
Author(s):  
Masayasu MAKI ◽  
Kosuke SEKIGUCHI ◽  
Koki HOMMA ◽  
Yoshihiro HIROOKA ◽  
Kazuo OKI
Keyword(s):  
Remote Sensing ◽  
Growth Model ◽  
Rice Yield ◽  
Remote Sensing Data ◽  
Crop Growth ◽  
Crop Growth Model ◽  
Sensing Data

scholarly journals Mapping Paleohydrologic Features in the Arid Areas of Saudi Arabia Using Remote-Sensing Data

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 417 ◽  
Author(s):  
Mohamed Abdelkareem ◽  
Fathy Abdalla ◽  
Samar Y. Mohamed ◽  
Farouk El-Baz
Keyword(s):  
Remote Sensing ◽  
Saudi Arabia ◽  
Volcanic Activity ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Alos Palsar ◽  
Sensing Data ◽  
The Past ◽  
Aeolian Deposits

At present, the Arabian Peninsula is one of the driest regions on Earth; however, this area experienced heavy rainfall in the past thousand years. During this period, catchments received substantial amounts of surface water and sustained vast networks of streams and paleolakes, which are currently inactive. The Advanced Land Observing Satellite (ALOS) Phased Array Type L-band Synthetic Aperture Radar (PALSAR) data reveal paleohydrologic features buried under shallow aeolian deposits in many areas of the ad-Dawasir, Sahba, Rimah/Batin, and as-Sirhan wadis. Optical remote-sensing data support that the middle of the trans-peninsula Wadi Rimah/Batin, which extends for ~1200 km from the Arabian Shield to Kuwait and covers ~200,000 km2, is dammed by linear sand dunes formed by changes in climate conditions. Integrating Landsat 8 Operational Land Imager (OLI), Geo-Eye, Shuttle Radar Topography Mission (SRTM) digital elevation model, and ALOS/PALSAR data allowed for the characterization of paleodrainage reversals and diversions shaped by structural and volcanic activity. Evidence of streams abruptly shifting from one catchment to another is preserved in Wadi ad-Dawasir along the fault trace. Volcanic activity in the past few thousand years in northern Saudi Arabia has also changed the slope of the land and reversed drainage systems. Relics of earlier drainage directions are well maintained as paleoslopes and wide upstream patterns. This study found that paleohydrologic activity in Saudi Arabia is impacted by changes in climate and by structural and volcanic activity, resulting in changes to stream direction and activity. Overall, the integration of radar and optical remote-sensing data is significant for deciphering past hydrologic activity and for predicting potential water resource areas.

Spatiotemporal monitoring of surface soil moisture using optical remote sensing data: a case study in a semi-arid area

2018 ◽  
Vol 65 (3) ◽  
pp. 481-499 ◽  
Author(s):  
Rida Khellouk ◽  
Ahmed Barakat ◽  
Abdelghani Boudhar ◽  
Rachid Hadria ◽  
Hayat Lionboui ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Surface Soil ◽  
Remote Sensing Data ◽  
Arid Area ◽  
Optical Remote Sensing ◽  
Surface Soil Moisture ◽  
Sensing Data ◽  
Semi Arid
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