Monitoring leaf area index after heading stage using hyperspectral remote sensing data in rice

He, Jue; Qin, Yehui; Guo, Caili; Zhao, Liyun; Zhou, Xiang; Yao, Xia; Cheng, Tao; Tian, Yongchao

doi:10.1109/igarss.2016.7730643

Cited by 3 publications

(5 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The reflectance was higher at 67-82 days after sowing (which comprises 2 phases, the final days of the reproductive phase and the beginning of the ripening phase) compared to the other sowing dates, which coincides with the results of [48], in tests performed on the Kongyu rice variety in China. The presence of the panicle also increases reflectance in the near-infrared range, which is consistent with the results of He et al [30] for the japonica rice variety. At 116 days after sowing (last stages of cultivation), a decay of reflectance is observed, possibly due to decreased leaf water content and the loss of green leaf area.…”

Section: Discussionsupporting

confidence: 92%

See 1 more Smart Citation

Comparing Two Methods of Leaf Area Index Estimation for Rice (Oryza sativa L.) Using In-Field Spectroradiometric Measurements and Multispectral Satellite Images

et al. 2023

View full text Add to dashboard Cite

This work presents a remote sensing application to estimate the leaf area index (LAI) in two rice (Oryza sativa L.) varieties (IDIAP 52-05 and IDIAP FL 137-11), as a proxy for crop performance. In-field, homogeneous spectroradiometric measurements (350–1050 nm) were carried in two campaigns (June–November 2017 and January–March 2018), on a private farm, TESKO, located in Juan Hombrón, Coclé Province, Panama. The spectral fingerprint of IDIAP 52-05 plants was collected in four dates (47, 67, 82 and 116 days after sowing), according to known phenological stages of rice plant growth. Moreover, true LAI or green leaf area was measured from representative plants and compared to LAI calculated from normalized PlanetScope multi-spectral satellite images (selected according to dates close to the in-field collection). Two distinct estimation models were used to establish the relationships of measured LAI and two vegetational spectral indices (NDVI and MTVI2). The results show that the MTVI2 based model has a slightly higher predictive ability of true LAI (R2 = 0.92, RMSE = 2.20), than the NDVI model. Furthermore, the satellite images collected were corrected and satellite LAI was contrasted with true LAI, achieving in average 18% for Model 2 for MTVI2, with the NDVI (Model 1) corrected model having a smaller error around 13%. This work provides an important advance in precision agriculture, specifically in the monitoring of total crop growth via LAI for rice crops in the Republic of Panama.

show abstract

Section: Discussionsupporting

confidence: 92%

“…Information from these bands can be applied to inspection, control, and surveillance of extensive rice crops. For example, He et al [30] studied the effect of panicle presence on LAI estimation for monitoring rice cultivation at the harvest stage.…”

Section: Introductionmentioning

confidence: 99%

Comparing Two Methods of Leaf Area Index Estimation for Rice (Oryza sativa L.) Using In-Field Spectroradiometric Measurements and Multispectral Satellite Images

et al. 2023

View full text Add to dashboard Cite

show abstract

“…En la gráfica 2 se observa que la reflectancia en general es mayor en la fase de maduración que en las otras fases, lo cual coincide con los resultados de [14], en pruebas realizadas a la variedad de arroz Kongyu proveniente de China. La presencia de la panícula aumenta la reflectancia en el rango de infrarrojo cercano, lo cual concuerda con los resultados de [10] para la variedad de arroz japónica. Se observa un decaimiento de la reflectancia en la últimas estapas del cultivo, esta debido al disminución de pérdida de cobertura foliar verde, hojas que han perdido agua.…”

Section: Discussionunclassified

“…En la teledetección del LAI en cultivos de arroz se ha elaborado trabajos como la supervisión del cultivo en la etapa de cosecha donde se estudia el efecto la presencia de la panícula en la estimación del LAI [10].…”

Section: Introductionunclassified

Detección de índice de área foliar por firma espectral de cultivo de arroz

Reyes¹,

Jimenez²,

Fábrega³

et al. 2018

Proceedings of the 16th LACCEI International Multi-Conference for Engineering, Education, and Technology: “Innovation in Educat

View full text Add to dashboard Cite

show abstract

“…There are many direct (e.g., allometry, harvesting and littering collection) and indirect methods (based on radiation transfer through plant canopy) to measure LAI in the field (Gower et al, 1999;Bréda, 2003). Plant canopy directly linked with spectral reflectance therefore, LAI retrieval from remote sensing data has stimulated many research studies in recent years (Zheng and Moskal, 2009;He et al, 2016). Positive correlation has been reported in many studies between NDVI and LAI (Wang et al, 2005;Maki and Homma, 2014).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Different Phenological Information to Map Crop Rotation in Complex Irrigated Indus Basin

Ismaeel

Zhou

2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:Accurate information of crop rotation in large basin is essential for policy decisions on land, water and nutrient resources around the world. Crop area estimation using low spatial resolution remote sensing data is challenging in a large heterogeneous basin having more than one cropping seasons. This study aims to evaluate the accuracy of two phenological datasets individually and in combined form to map crop rotations in complex irrigated Indus basin without image segmentation. Phenology information derived from Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) of Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, having 8-day temporal and 1000 m spatial resolution, was used in the analysis. An unsupervised (temporal space clustering) to supervised (area knowledge and phenology behavior) classification approach was adopted to identify 13 crop rotations. Estimated crop area was compared with reported area collected by field census. Results reveal that combined dataset (NDVI*LAI) performs better in mapping wheat-rice, wheat-cotton and wheat-fodder rotation by attaining root mean square error (RMSE) of 34.55, 16.84, 20.58 and mean absolute percentage error (MAPE) of 24.56%, 36.82%, 30.21% for wheat, rice and cotton crop respectively. For sugarcane crop mapping, LAI produce good results by achieving RMSE of 8.60 and MAPE of 34.58%, as compared to NDVI (10.08, 40.53%) and NDVI*LAI (10.83, 39.45%). The availability of major crop rotation statistics provides insight to develop better strategies for land, water and nutrient accounting frameworks to improve agriculture productivity.

show abstract

Monitoring leaf area index after heading stage using hyperspectral remote sensing data in rice

Cited by 3 publications

References 9 publications

Comparing Two Methods of Leaf Area Index Estimation for Rice (Oryza sativa L.) Using In-Field Spectroradiometric Measurements and Multispectral Satellite Images

Comparing Two Methods of Leaf Area Index Estimation for Rice (Oryza sativa L.) Using In-Field Spectroradiometric Measurements and Multispectral Satellite Images

Detección de índice de área foliar por firma espectral de cultivo de arroz

Evaluation of Different Phenological Information to Map Crop Rotation in Complex Irrigated Indus Basin

Contact Info

Product

Resources

About