Variational assimilation to retrieve leaf area index from MODIS time series data

2009 ◽  
Author(s):  
Zhiqiang Xiao
Keyword(s):  
Time Series ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Time Series Data ◽  
Series Data ◽  
Area Index ◽  
Variational Assimilation

Estimation of the seasonal leaf area index in urban oak forests using Sentinel-2 time series data

2019 ◽  
Author(s):  
A. Kozlova ◽  
I. Piestova ◽  
L. Patrusheva ◽  
M. Lubsky ◽  
A. Nikulina ◽  
...  
Keyword(s):  
Time Series ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Time Series Data ◽  
Series Data ◽  
Oak Forests ◽  
Area Index ◽  
Sentinel 2

Variational retrieval of leaf area index from MODIS time series data: examples from the Heihe river basin, north-west China

2011 ◽  
Vol 33 (3) ◽  
pp. 730-745 ◽  
Author(s):  
Zhiqiang Xiao ◽  
Jindi Wang ◽  
Shunlin Liang ◽  
Hongmin Zhou ◽  
Xijia Li ◽  
...  
Keyword(s):  
Time Series ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Time Series Data ◽  
Series Data ◽  
Heihe River ◽  
Area Index ◽  
West China ◽  
North West ◽  
The Heihe River Basin

Real-time retrieval of Leaf Area Index from MODIS time series data

2011 ◽  
Vol 115 (1) ◽  
pp. 97-106 ◽  
Author(s):  
Zhiqiang Xiao ◽  
Shunlin Liang ◽  
Jindi Wang ◽  
Bo Jiang ◽  
Xijia Li
Keyword(s):  
Time Series ◽  
Leaf Area Index ◽  
Real Time ◽  
Leaf Area ◽  
Time Series Data ◽  
Series Data ◽  
Area Index

scholarly journals Assimilating Multiresolution Leaf Area Index of Moso Bamboo Forest from MODIS Time Series Data Based on a Hierarchical Bayesian Network Algorithm

2018 ◽  
Vol 11 (1) ◽  
pp. 56 ◽  
Author(s):  
Luqi Xing ◽  
Xuejian Li ◽  
Huaqiang Du ◽  
Guomo Zhou ◽  
Fangjie Mao ◽  
...  
Keyword(s):  
Time Series ◽  
Carbon Cycle ◽  
Bayesian Network ◽  
Leaf Area Index ◽  
Time Series Data ◽  
Series Data ◽  
Bamboo Forest ◽  
Area Index ◽  
Moso Bamboo Forest ◽  
Modis Lai

The highly accurate multiresolution leaf area index (LAI) is an important parameter for carbon cycle simulation for bamboo forests at different scales. However, current LAI products have discontinuous resolution with 1 km mostly, that makes it difficult to accurately quantify the spatiotemporal evolution of carbon cycle at different resolutions. Thus, this study used MODIS LAI product (MOD15A2) and MODIS reflectance data (MOD09Q1) of Moso bamboo forest (MBF) from 2015, and it adopted a hierarchical Bayesian network (HBN) algorithm coupled with a dynamic LAI model and the PROSAIL model to obtain high-precision LAI data at multiresolution (i.e., 1000, 500, and 250 m). The results showed the LAIs assimilated using the HBN at the three resolutions corresponded with the actual growth trend of the MBF and correlated significantly with the observed LAI with a determination coefficient (R2) value of > 0.80. The highest-precision assimilated LAI was obtained at 1000-m resolution with R2 values of 0.91. The LAI assimilated using the HBN algorithm achieved better accuracy than the MODIS LAI with increases in the R2 value of 2.7 times and decreases in the root mean square error of 87.8%. Therefore, the HBN algorithm applied in this study can effectively obtain highly accurate multiresolution LAI time series data for bamboo forest.

scholarly journals Analogy-Based Crop Yield Forecasts Based on Temporal Similarity of Leaf Area Index

2021 ◽  
Vol 13 (16) ◽  
pp. 3069
Author(s):  
Yadong Liu ◽  
Junhwan Kim ◽  
David H. Fleisher ◽  
Kwang Soo Kim
Keyword(s):  
Time Series ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Crop Yield ◽  
Satellite Data ◽  
Growing Season ◽  
Environmental Data ◽  
Area Index ◽  
Current Season ◽  
Wide Range

Seasonal forecasts of crop yield are important components for agricultural policy decisions and farmer planning. A wide range of input data are often needed to forecast crop yield in a region where sophisticated approaches such as machine learning and process-based models are used. This requires considerable effort for data preparation in addition to identifying data sources. Here, we propose a simpler approach called the Analogy Based Crop-yield (ABC) forecast scheme to make timely and accurate prediction of regional crop yield using a minimum set of inputs. In the ABC method, a growing season from a prior long-term period, e.g., 10 years, is first identified as analogous to the current season by the use of a similarity index based on the time series leaf area index (LAI) patterns. Crop yield in the given growing season is then forecasted using the weighted yield average reported in the analogous seasons for the area of interest. The ABC approach was used to predict corn and soybean yields in the Midwestern U.S. at the county level for the period of 2017–2019. The MOD15A2H, which is a satellite data product for LAI, was used to compile inputs. The mean absolute percentage error (MAPE) of crop yield forecasts was <10% for corn and soybean in each growing season when the time series of LAI from the day of year 89 to 209 was used as inputs to the ABC approach. The prediction error for the ABC approach was comparable to results from a deep neural network model that relied on soil and weather data as well as satellite data in a previous study. These results indicate that the ABC approach allowed for crop yield forecast with a lead-time of at least two months before harvest. In particular, the ABC scheme would be useful for regions where crop yield forecasts are limited by availability of reliable environmental data.

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