Retrieval of regional LAI over agricultural land from an Indian geostationary satellite and its application for crop yield estimation

Nigam, Rahul; Vyas, Swapnil; Bhattacharya, Bimal K.; Oza, M. P.; Manjunath, K. R.

doi:10.1080/14498596.2016.1220872

Cited by 6 publications

(6 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Further to this, the apparent ability of RF to detect underlying relationships can also reduce the number of explanatory variables required to provide an accurate estimation. Previous studies have commonly utilised a variety of VIs to estimate yield by inferring relationships between VIs and yield (Liaqat et al, 2017;Lopresti et al, 2015;Ren et al, 2008), or to derive relationships with surface parameters such as LAI and fAPAR, which can be used to estimate yield (Boschetti et al, 2014;Nigam et al, 2017). In this study, using VIs and the original Sentinel-2 data together provided no improvement in accuracy.…”

Section: Benefits Of Random Forestmentioning

confidence: 82%

High resolution wheat yield mapping using Sentinel-2

Hunt

Blackburn

Carrasco

et al. 2019

Remote Sensing of Environment

168

View full text Add to dashboard Cite

Accurate crop yield estimates are important for governments, farmers, scientists and agribusiness. This paper provides a novel demonstration of the use of freely available Sentinel-2 data to estimate withinfield wheat yield variability in a single year. The impact of data resolution and availability on yield estimation is explored using different combinations of input data. This was achieved by combining Sentinel-2 with environmental data (e.g. meteorological, topographical, soil moisture) for different periods throughout the growing season. Yield was estimated using Random Forest (RF) regression models. They were trained and validated using a dataset containing over 8000 points collected by combine harvester yield monitors from 39 wheat fields in the UK. The results demonstrate that it is possible to produce accurate maps of within-field yield variation at 10m resolution using Sentinel-2 data (RMSE 0.66 tonnes/ha). When combined with environmental data further improvements in accuracy can be obtained (RMSE 0.61 tonnes/ha). We demonstrate that with knowledge of crop-type distribution it is possible to use these models, trained with data from a few fields, to estimate within-field yield variability on a landscape scale. Applying this method gives us a range of crop yield across the landscape of 4.09 to 12.22 tonnes/ha, with a total crop production of approx. 289000 tonnes.

show abstract

Section: Benefits Of Random Forestmentioning

confidence: 82%

High resolution wheat yield mapping using Sentinel-2

Hunt

Blackburn

Carrasco

et al. 2019

Remote Sensing of Environment

168

View full text Add to dashboard Cite

show abstract

“…Geostationary orbit satellite sensors are primarily designed for observation and forecasting of weather conditions. These have a higher temporal resolution than Earth orbit satellite sensors such as MODIS and AVHRR due to their continuous observation characteristic in specified regions [15,16]. This characteristic can increase the chances for obtaining clear satellite imagery by acquiring many images in a single day than polar orbit satellites [14,17].…”

Section: Introductionmentioning

confidence: 99%

Nationwide Projection of Rice Yield Using a Crop Model Integrated with Geostationary Satellite Imagery: A Case Study in South Korea

Jeong

Yeom

2018

Remote Sensing

View full text Add to dashboard Cite

The Geostationary Ocean Color Imager (GOCI) of the Communication, Ocean, and Meteorological Satellite (COMS) increases the chance of acquiring images with greater clarity eight times a day and is equipped with spectral bands suitable for monitoring crop yield in the national scale with a spatial resolution of 500 m. The objectives of this study were to classify nationwide paddy fields and to project rice (Oryza sativa) yield and production using the grid-based GRAMI-rice model and GOCI satellite products over South Korea from 2011 to 2014. Solar insolation and temperatures were obtained from COMS and the Korea local analysis and prediction systems for model inputs, respectively. The paddy fields and transplanting dates were estimated by using Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and land cover products. The crop model was calibrated using observed yield data in 11 counties and was applied to 62 counties in South Korea. The overall accuracies of the estimated paddy fields using MODIS data ranged from 89.5% to 90.2%. The simulated rice yields statistically agreed with the observed yields with mean errors of −0.07 to +0.10 ton ha−1, root-mean-square errors of 0.219 to 0.451 ton ha−1, and Nash–Sutcliffe efficiencies of 0.241 to 0.733 in four years, respectively. According to paired t-tests (α = 0.05), the simulated and observed rice yields were not significantly different. These results demonstrate the possible development of a crop information delivery system that can classify land cover, simulate crop yield, and monitor regional crop production on a national scale.

show abstract

“…At present, few studies have used remote sensing to monitor soil heat stress and have mainly focused on drought stress [20], waterlogging stress [21], high-temperature stress [22], disease stress, and heavy metal stress [23]. Plant environmental stress has been estimated directly or indirectly based on spectral features (such as frequency-domain transformation features [24], vegetation indexes [25]), physiological and biochemical parameters (such as plant water [26], the leaf area index [27], pigment content [28], and chlorophyll fluorescence parameters [29]).…”

Section: Introductionmentioning

confidence: 99%

Simulating Heat Stress of Coal Gangue Spontaneous Combustion on Vegetation Using Alfalfa Leaf Water Content Spectral Features as Indicators

et al. 2021

View full text Add to dashboard Cite

Vegetation heat-stress assessment in the reclamation areas of coal gangue dumps is of great significance in controlling spontaneous combustion; through a temperature gradient experiment, we collected leaf spectra and water content data on alfalfa. We then obtained the optimal spectral features of appropriate leaf water content indicators through time series analysis, correlation analysis, and Lasso regression analysis. A spectral feature-based long short-term memory (SF-LSTM) model is proposed to estimate alfalfa’s heat stress level; the live fuel moisture content (LFMC) varies significantly with time and has high regularity. Correlation analysis of the raw spectrum, first-derivative spectrum, spectral reflectance indices, and leaf water content data shows that LFMC and spectral data were the most strongly correlated. Combined with Lasso regression analysis, the optimal spectral features were the first-derivative spectral value at 1661 nm (abbreviated as FDS (1661)), RVI (1525,1771), DVI (1412,740), and NDVI (1447,1803). When the classification strategies were divided into three categories and the time sequence length of the spectral features was set to five consecutive monitoring dates, the SF-LSTM model had the highest accuracy in estimating the heat stress level in alfalfa; the results provide an important theoretical basis and technical support for vegetation heat-stress assessment in coal gangue dump reclamation areas.

show abstract

Retrieval of regional LAI over agricultural land from an Indian geostationary satellite and its application for crop yield estimation

Cited by 6 publications

References 50 publications

High resolution wheat yield mapping using Sentinel-2

High resolution wheat yield mapping using Sentinel-2

Nationwide Projection of Rice Yield Using a Crop Model Integrated with Geostationary Satellite Imagery: A Case Study in South Korea

Simulating Heat Stress of Coal Gangue Spontaneous Combustion on Vegetation Using Alfalfa Leaf Water Content Spectral Features as Indicators

Contact Info

Product

Resources

About