MODIS NDVI Multi-Temporal Analysis Confirms Farmer Perceptions on Seasonality Variations Affecting Apple Orchards in Kinnaur, Himachal Pradesh

Gupta, Hansraj; Kaur, Lakhvinder; Asra, Mahbooba; Avtar, Ram; Reddy, C. Sudhakar

doi:10.3390/agriculture11080724

Cited by 2 publications

(5 citation statements)

References 29 publications

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“…Similarly, a moderate correlation was found between soil moisture and wind speed. The apple growth condition was very nicely correlated with the normalized difference vegetation index, which similarly existed between snow index and rainfall 10 …”

Section: Resultsmentioning

confidence: 80%

“…The installation of snow gauges at IMD-controlled weather stations is also critically needed to gather reliable data on the snowfall and other natural calamities information in the region and on the event-specific patterns of snowfall. This will allow policymakers and decision-makers to make informed decisions regarding weather variations 10 . Low suitability areas may be allotted the alternative horticulture crops and best agronomic practices management for sustainable agricultural planning 60 …”

Section: Discussionmentioning

confidence: 99%

“…This will allow policymakers and decision-makers to make informed decisions regarding weather variations. 10 Low suitability areas may be allotted the alternative horticulture crops and best agronomic practices management for sustainable agricultural planning. 60…”

Section: Snow Effect On Fruit Yieldmentioning

confidence: 99%

“…It can also be used to develop policies, which aimed at conserving the orchards for better economic development. Geographic information system (GIS) and remote sensing technologies have made it possible to collect and analyze multitemporal data related to the various climatological and physiological processes of the apple cultivation 10 . These tools can be used to predict the yield of the fruit and monitor the condition of apple orchards.…”

Section: Introductionmentioning

confidence: 99%

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Apple yield prediction mapping using machine learning techniques through the Google Earth Engine cloud in Kashmir Valley, India

Singha

Gulzar

Swain

et al. 2023

J. Appl. Rem. Sens.

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.Our study established a machine learning (ML) model that could predict the apple yield based on various satellite multisensor data, such as climatological, SAR backscatter, terrain distribution, and soil factors, grouped as 26 subcriteria. A total of 986 apple orchards database were collected from 2018 to 2021 in Kashmir Valley, India covering an area of 277953.7 ha farmland. The novelty of our research is the integration of Google Earth Engine cloud and ML models, namely random forest, support vector machine, extreme gradient boosting, K-nearest neighbors, and Cubist along with the geographic information system/remote sensing technology to create an accurate and comprehensive apple yield prediction model in the precision agriculture realm for highlands. The multicollinearity testing indicated that the tolerance and VIF values of all the conditioning factors were <0.1 and <6.85, respectively, indicating no multicollinearity problems among the apple yield suitability factors. Among the tested ML models, the Cubist model performed best, with R2 of 0.83, root-mean-squared error of 0.56 t / ha, and mean absolute error of 0.2 t / ha. The results showed a low mean fruit yield during 2018 of 12.36 ton / ha, whereas maximum fruit yield was reflected in 2021 of 14.05 ton / ha. The heat map revealed the highest normalized differential vegetation index along with vertical-vertical/ vertical-horizontal polarization backscatter, detected during the pre-event of severe snowfall compared to on- and postevent of snowfall for the respective years. Untimely snowing and infestation due to fungi and bacterial diseases regularly reduce fruit yield in the study area. Our study successfully used of high-resolution optical-SAR data combined with ML models as a promising tool for monitoring the yield variability over the highland areas.

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Section: Resultsmentioning

confidence: 80%

Section: Discussionmentioning

confidence: 99%

Section: Snow Effect On Fruit Yieldmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Apple yield prediction mapping using machine learning techniques through the Google Earth Engine cloud in Kashmir Valley, India

Singha

Gulzar

Swain

et al. 2023

J. Appl. Rem. Sens.

View full text Add to dashboard Cite

show abstract

“…Satellite sensors have been widely used for observing the phenological stages of plants. Gupta et al [22] showed that apple growth was highly correlated with the Normalized Difference Snow Index (NDSI). Aragones et al [23] proved that pine species could be classified using phenology-derived classes using the Normalised Difference Vegetation Index (NDVI) in Mediterranean forest.…”

Section: Introductionmentioning

confidence: 99%

A Selection of Experiments for Understanding the Strengths of Time Series SAR Data Analysis for Finding the Drivers Causing Phenological Changes in Paphos Forest, Cyprus

et al. 2022

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Observing phenological changes are important for evaluating the natural regeneration process of forests, especially in Mediterranean areas where the regeneration of coniferous forests depends on seeds and the changes in blossoming time are influenced by climate change. The high temporal resolution of Sentinel-1 data allows the time series analysis of synthetic aperture radar (SAR) data, but it is still unknown how these data could be utilised for better understanding forest phenology and climate-related alternations. This study investigates the phenological cycle of Paphos forest, Cyprus using SAR data from 1992 to 2021, acquired by ERS-1/2, Envisat and Sentinel-1. An average phenological diagram was created for each space mission and a more detailed analysis was performed from October 2014 to November 2021, using the higher temporal resolution of Sentinel-1 data. Meteorological data were used to better understand the drivers of blooming alternations. Using the interquartile range (IQR), outliers were detected and replaced using the Kalman filter imputation. Forecasting trend lines were used to estimate the amplitude of the summer peaks and the annual mean. The observation of the average phenology from each satellite mission showed that there were two main blooming peaks each year: the winter and the summer peak. We argue that the winter peak relates to increased foliage, water content and/or increased soil moisture. The winter peak was followed by a fall in February reaching the lower point around March, due to the act of pine processionary (Thaumetopoea pityocampa). The summer peak should relate to the annual regeneration of needles and the drop of the old ones. A delay in the summer peak—in August 2018—was associated with increased high temperatures in May 2018. Simultaneously, the appearance of one peak instead of two in the σVH time series during the period November 2014–October 2015 may be linked to a reduced act of the pine processionary associated with low November temperatures. Furthermore, there was an outlier in February 2015 with very low backscattering coefficients and it was associated with a drought year. Finally, predicting the amplitude of July 2020 returned high relevant Root Mean Square Error (rRMSE). Seven years of time series data are limiting for predicting using trend lines and many parameters need to be taken into consideration, including the increased rainfall between November 2018 and March 2020.

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MODIS NDVI Multi-Temporal Analysis Confirms Farmer Perceptions on Seasonality Variations Affecting Apple Orchards in Kinnaur, Himachal Pradesh

Cited by 2 publications

References 29 publications

Apple yield prediction mapping using machine learning techniques through the Google Earth Engine cloud in Kashmir Valley, India

Apple yield prediction mapping using machine learning techniques through the Google Earth Engine cloud in Kashmir Valley, India

A Selection of Experiments for Understanding the Strengths of Time Series SAR Data Analysis for Finding the Drivers Causing Phenological Changes in Paphos Forest, Cyprus

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