Time Series Analysis of Remote Sensing Observations for Citrus  Crop Growth Stage and Evapotranspiration Estimation

Sawant, Suryakant; Chakraborty, M.; Suradhaniwar, Saurabh; Adinarayana, J.; Durbha, Surya S.

doi:10.5194/isprs-archives-xli-b8-1037-2016

Cited by 4 publications

(3 citation statements)

References 23 publications

(9 reference statements)

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“…The time-series of NDVI at the different CSs (Figures 5 and 6) have shown a typical annual pattern deriving from the sum of two contributions: (i) the fairly stable vegetation dynamic of citrus trees and (ii) the presence of typical conditions of grassed soil [64][65][66][67]. It can be assumed, given that the ground cover plays a role of constant "background" every year, that the dynamics of the seasonal NDVI parameters are mainly linked to the presence of citrus groves.…”

Section: Discussionmentioning

confidence: 99%

Suitability of the MODIS-NDVI Time-Series for a Posteriori Evaluation of the Citrus Tristeza Virus Epidemic

et al. 2020

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The technological advances of remote sensing (RS) have allowed its use in a number of fields of application including plant disease depiction. In this study, an RS approach based on an 18-year (i.e., 2001–2018) time-series analysis of Normalized Difference Vegetation Index (NDVI) data, derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and processed with TIMESAT free software, was applied in Sicily (insular Italy). The RS approach was carried out in four orchards infected by Citrus tristeza virus (CTV) at different temporal stages and characterized by heterogeneous conditions (e.g., elevation, location, plant age). The temporal analysis allowed the identification of specific metrics of the NDVI time-series at the selected sites during the study period. The most reliable parameter which was able to identify the temporal evolution of CTV syndrome and the impact of operational management practices was the “Base value” (i.e., average NDVI during the growing seasons, which reached R2 values up to 0.88), showing good relationships with “Peak value”, “Small integrated value” and “Amplitude”, with R2 values of 0.63, 0.70 and 0.75, respectively. The approach herein developed is valid to be transferred to regional agencies involved in and/or in charge of the management of plant diseases, especially if it is integrated with ground-based early detection methods or high-resolution RS approaches, in the case of quarantine plant pathogens requiring control measures at large-scale level.

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Section: Discussionmentioning

confidence: 99%

Suitability of the MODIS-NDVI Time-Series for a Posteriori Evaluation of the Citrus Tristeza Virus Epidemic

et al. 2020

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“…Tedesco, et al [37] used a combination of remote sensing derived VIs (NDVI, GNDVI and SAVI) and growing degree days (GDD) to characterize the phenology stages of sweet potato. In another study, Sawant, et al [52] used NDVI time-series data obtained from Landsat archives spanning the period 1990-2015 to study the phenological growth stages and evapotranspiration of citrus. Furthermore, satellite image time series of Sentinel-1 and Sentinel-2 derived crop phenology was used to classify 9 perennial tree crops [53,54].…”

Section: Introductionmentioning

confidence: 99%

Assessing the Potential of Sentinel-2 Derived Vegetation Indices to Retrieve Phenological Stages of Mango in Ghana

et al. 2021

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In 2020, mango (Mangifera indica) exports contributed over 40 million tons, worth around US$20 billion, to the global economy. Only 10% of this contribution was made from African countries including Ghana, largely due to lower investment in the sector and general paucity of research into the mango value chain, especially production, quality and volume. Considering the global economic importance of mango coupled with the gap in the use of the remote sensing technology in the sector, this study tested the hypothesis that phenological stages of mango can be retrieved from Sentinel-2 (S2) derived time series vegetation indices (VIs) data. The study was conducted on four mango farms in the Yilo Krobo Municipal Area of Ghana. Seasonal (temporal) growth curves using four VIs (NDVI, GNDVI, EVI and SAVI) for the period from 2017 to 2020 were derived for each of the selected orchards and then aligned with five known phenology stages: Flowering/Fruitset (F/FS), Fruit Development (FRD), Maturity/Harvesting (M/H), Flushing (FLU) and Dormancy (D). The significance of the variation “within” and “between” farms obtained from the VI metrics of the S2 data were tested using single-factor and two-factor analysis of variance (ANOVA). Furthermore, to identify which specific variable pairs (phenology stages) were significantly different, a Tukey honest significant difference (HSD) post-hoc test was conducted, following the results of the ANOVA. Whilst it was possible to differentiate the phenological stages using all the four VIs, EVI was found to be the best related with p < 0.05 for most of the studied farms. A distinct annual trend was identified with a peak in June/July and troughs in December/January. The derivation of remote sensing based ‘time series’ growth profiles for commercial mango orchards supports the ‘benchmarking’ of annual and seasonal orchard performance and therefore offers a near ‘real time’ technology for identifying significant variations resulting from pest and disease incursions and the potential impacts of seasonal weather variations.

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“…These historical and near-real time EO archives are rich source of information to understand the seasonal changes in the horticultural crops (Sawant et al, 2016). With increase in the availability of satellite observations in regular interval it's possible to monitor the crop growth conditions across the cropping cycle (Sentinel 2 and Landsat 8 with return period of 12 and 15 days respectively).…”

Section: Introductionmentioning

confidence: 99%

Integration of Human Participatory Sensing and Archives of Remote Sensing Observations for Field Level Crop Phenology Estimation

Sawant

Mohite

Pappula

2018

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

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<p><strong>Abstract.</strong> The rise in global population has increased food and water demand thereby causing excessive pressure on existing resources. In developing countries with fragmented land holdings there exists constant pressure on available water and land resources. Obtaining field scale crop specific information is challenging task. Advent of open freely available multi-temporal remote sensing observations with improved radiometric resolution the possibilities for near real / real time applications has increased. In this study and an attempt has been made to establish operational model for field level crop growth monitoring using integrated approach of crowd sourcing and time series of remote sensing observations. The time series of Sentinel 2 (A and B) satellite has been used to estimate crop growth related components such as vegetation indices and crop growth stage and crop phenology. In initial stage high valued cereal crop Wheat has been selected. The field level information (i.e. 108 Wheat fields) collected using mobile based agro-advisory platform mKRISHI&reg; has been used to extract time series of Sentinel 2 observations (44 scenes for year 2016 and 2018). The moving average has been used for filling gaps in the time series of vegetation indices. The BFAST and GreenBrown package in R were used for detecting breaks in vegetation index time series and estimating crop growth stages. Analysis shows that the estimated crop phenology parameters were in better agreement with the field observations. In future more crops from different agro-climatic conditions will be considered for providing field level crop management advisory.</p>

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Time Series Analysis of Remote Sensing Observations for Citrus Crop Growth Stage and Evapotranspiration Estimation

Cited by 4 publications

References 23 publications

Suitability of the MODIS-NDVI Time-Series for a Posteriori Evaluation of the Citrus Tristeza Virus Epidemic

Suitability of the MODIS-NDVI Time-Series for a Posteriori Evaluation of the Citrus Tristeza Virus Epidemic

Assessing the Potential of Sentinel-2 Derived Vegetation Indices to Retrieve Phenological Stages of Mango in Ghana

Integration of Human Participatory Sensing and Archives of Remote Sensing Observations for Field Level Crop Phenology Estimation

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