scholarly journals The Application of Modified Normalized Difference Water Index by Leaf Area Index in the Retrieval of Regional Drought Monitoring

2017 ◽  
Author(s):  
Hong-Wei ZHANG ◽  
Huai-Liang CHEN
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Drought Monitoring ◽  
Area Index ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Regional Drought

scholarly journals The Application of Modified Normalized Difference Water Index (MNDWI) by Leaf Area Index in the Retrieval of Regional Drought Monitoring

2015 ◽  
Vol XL-7/W3 ◽  
pp. 141-147
Author(s):  
H.-w. Zhang ◽  
H.-l. Chen
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Coverage ◽  
Area Index ◽  
Water Index ◽  
Retrieval Accuracy ◽  
Normalized Difference Water Index ◽  
Green Stage

The vegetation coverage is one of the important factors that restrict the accuracy of remote sensing retrieval of soil moisture. In order to effectively improve the accuracy of the remote sensing retrieval of soil moisture and to reduce the impact of vegetation coverage variation on the retrieval accuracy, the Leaf Area Index (LAI) is introduced to the Normalized Difference Water Index (NDWI) to greatly improve the accuracy of the soil moisture retrieval. In its application on the regional drought monitoring, the paper uses the relative LAI from two places which locate in the north and south of Henan Province respectively (Xin Xiang and Zhu Ma Dian) as indicators. It uses the days after turned-green stage to conduct difference value correction on the Relative Leaf Area Index (RLAL) of the entire province, so as to acquire the distribution of RLAI of the province’s wheat producing area. After this, the local remote sensing NDWI will be Modified （MNDWI = NDWI ×RLAI ） to acquire the soil moisture distribution status of the entire province’s wheat producing area. The result shows that, the Modified Normalized Difference Water Index of LAI which based on the days after turned-green stage can improve the real time retrieval accuracy of soil moisture under different vegetation coverage.

scholarly journals Assimilation of Leaf Area Index and Soil Water Index from Satellite Observations in a Land Surface Model in Hungary

Atmosphere ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 944
Author(s):  
Helga Tóth ◽  
Balázs Szintai
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Leaf Area Index ◽  
Soil Water ◽  
Leaf Area ◽  
Weather Prediction ◽  
Regional Scale ◽  
Surface Model ◽  
Area Index ◽  
Water Index

In this study, a Land Data Assimilation System (LDAS) is applied over the Carpathian Basin at the Hungarian Meteorological Service to monitor the above-ground biomass, surface fluxes (carbon and water), and the associated root-zone soil moisture at the regional scale (spatial resolution of 8 km × 8 km) in quasi-real-time. In this system the SURFEX model is used, which applies the vegetation growth version of the Interactions between Soil, Biosphere and Atmosphere (ISBA-A-gs) photosynthesis scheme to describe the evolution of vegetation. SURFEX is forced using the outputs of the ALADIN numerical weather prediction model run operationally at the Hungarian Meteorological Service. First, SURFEX is run in an open-loop (i.e., no assimilation) mode for the period 2008–2015. Secondly, the Extended Kalman Filter (EKF) method is used to assimilate Leaf Area Index (LAI) Spot/Vegetation (until May 2014) and PROBA-V (from June 2014) and Soil Water Index (SWI) ASCAT/Metop satellite measurements. The benefit of LDAS is proved over the whole country and to a selected site in West Hungary (Hegyhátsál). It is demonstrated that the EKF can provide useful information both in wet and dry seasons as well. It is shown that the data assimilation is efficient to describe the inter-annual variability of biomass and soil moisture values. The vegetation development and the water and carbon fluxes vary from season to season and LDAS is a capable tool to monitor the variability of these parameters.

scholarly journals Leaf Area Index, Water Index, and Red: Far Red Ratio Calculated by Spectral Reflectance and its Relation to Plant Architecture and Cut Rose Production

2006 ◽  
Vol 131 (3) ◽  
pp. 313-319 ◽  
Author(s):  
Libertad Mascarini ◽  
Gabriel A. Lorenzo ◽  
Fernando Vilella
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Spectral Reflectance ◽  
Limiting Factor ◽  
Simple Method ◽  
Area Index ◽  
Water Index ◽  
Significant Difference ◽  
Shoot System

In roses (Rosa ×hybrida L.), the bending of branches is a technique that modifies the canopy of the plant and could affect such parameters as the leaf area index (LAI), the quality of reflected light, and the water index (WI) of the plant. The measurement of spectral reflectance with remote sensors is a nondestructive, quick, and simple method to study these parameters. The aim of this paper is to quantify the modification of reflected radiation quality, the LAI and the water index of the plant with different canopies, and its impact on flowering and the number and quality of flowers produced. In R. ×hybrida `Terracotta', using the spectral crop reflectance, the red: far red ratio [red (R) = 680 nm; far red (FR) = 730 nm], percentage of blue light of reflected radiation, and vegetation indices [normalized difference vegetation index (NDVI), simple ratio index (SRI), water index (WI)] were calculated in two architectural managements: traditional (upright hedge) and bent shoot. NDVI had a greater correlation with LAI than SRI (r2 = 0.98 and 0.85, respectively), but SRI was more reliable for LAI values of 1 to 3.5. The bent shoot system compared to the traditional one decreased the R:FR ratio of reflected radiation and increased LAI and plant water content. These changes were related to a higher commercial quality of the flowers (longer flowering shoots with a larger stem diameter and fresh weight), although there was no significant difference in the number of flowers harvested. The period that showed the largest difference in the quality of the flower using the bent shoot system had a LAI of 2.8 vs. 1.8 with traditional management and a marked reduction in the R:FR of the light reflected by bent plants. The bent shoot system advanced the peak production by 1 month at the end of winter and improved the flowers at a time when sun radiation is limiting factor for production.

Comparing site quality indices and productivity in ponderosa pine stands of western Montana

1988 ◽  
Vol 18 (3) ◽  
pp. 346-352 ◽  
Author(s):  
Scott D. McLeod ◽  
Steven W. Running
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Ponderosa Pine ◽  
Volume Growth ◽  
Stand Density ◽  
Site Index ◽  
Site Quality ◽  
Area Index ◽  
Water Index ◽  
Available Water

Four indices of site quality were compared with volume growth of pure, ideal ponderosa pine (Pinusponderosa Laws.) stands in western Montana. Indices based on quantifying the biophysical factors or physiological processes that control productivity (available water index and a relative index of seasonal photosynthesis from computer simulations) worked as well as those based on tree or stand measurements (site index and leaf area index). The following correlations of mean annual stem volume increment were found: with leaf area index, R2 = 0.93; with available water index, R2 = 0.95; with site index, R2 = 0.98; with gross photosynthesis R2 = 0.96. The available water and photosynthesis indices were also highly correlated to site index (R2 > 0.95). However, the tree-dependent site quality indices varied by stand density. Leaf area index and volume growth increased with stand density while site index decreased. Simulations indicated that depletion of soil water effectively halted transpiration and photosynthesis by midsummer and illustrated that even with adequate water, cold spring and fall temperatures ultimately defined the length of the growing season and hence site quality. We conclude that an ecosystem process model can provide an index to site quality independent of tree or stand measurements.

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.

Sign in / Sign up

Export Citation Format

Share Document