Enhancing Agricultural Productivity: Integrating Remote Sensing
Techniques for Cotton Yield Monitoring and Assessment

J 2024

Enhancing Agricultural Productivity: Integrating Remote Sensing Techniques for Cotton Yield Monitoring and Assessment

AGHAYEV, Amil; Tomáš ŘEZNÍK a Milan KONEČNÝ

Základní údaje

Originální název

Enhancing Agricultural Productivity: Integrating Remote Sensing Techniques for Cotton Yield Monitoring and Assessment

Autoři

AGHAYEV, Amil; Tomáš ŘEZNÍK a Milan KONEČNÝ

Vydání

ISPRS International Journal of Geo-Information, MDPI, 2024, 2220-9964

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10500 1.5. Earth and related environmental sciences

Stát vydavatele

Švýcarsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 2.800

Označené pro přenos do RIV

Ano

Kód RIV

RIV/00216224:14310/24:00137980

Organizační jednotka

Přírodovědecká fakulta

Klíčová slova anglicky

remote sensing; geographic ınformation systems; soil; cotton; vegetation indices; correlation

Štítky

14315030, rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 19. 3. 2025 10:31, Mgr. Marie Novosadová Šípková, DiS.

Anotace

V originále

This study assesses soil productivity in a 15-hectare cotton field using an integrated approach combining field data, laboratory analysis, and remote sensing techniques. Soil samples were collected and analyzed for key parameters including nitrogen (N), humus, phosphorus (P2O5), potassium (K2O), carbonates, pH, and electrical conductivity (EC). In addition to low salinity, these analyses showed low results for humus and nutrient parameters. A Pearson correlation analysis showed that low organic matter and high salinity had a strong negative correlation with crop productivity, explaining 37% of the variation in NDVI values. Remote sensing indices (NDVI, SAVI, NDMI, and NDSI) confirmed these findings by highlighting the relationship between soil properties and spectral reflectance. This research demonstrates the effectiveness of remote sensing in soil assessment, emphasizing its critical role in sustainable agricultural planning. By integrating traditional methods with advanced remote sensing technologies, this study provides actionable insights for policymakers and practitioners to improve soil productivity and ensure food security.

Citovat

AGHAYEV, Amil; Tomáš ŘEZNÍK a Milan KONEČNÝ. Enhancing Agricultural Productivity: Integrating Remote Sensing Techniques for Cotton Yield Monitoring and Assessment. ISPRS International Journal of Geo-Information. MDPI, 2024, roč. 13, č. 10, s. 1-19. ISSN 2220-9964. Dostupné z: https://doi.org/10.3390/ijgi13100340.

@article{2459461,
   author = {Aghayev, Amil and Řezník, Tomáš and Konečný, Milan},
   article_number = {10},
   doi = {https://doi.org/10.3390/ijgi13100340},
   keywords = {remote sensing; geographic ınformation systems; soil; cotton; vegetation indices; correlation},
   language = {eng},
   issn = {2220-9964},
   journal = {ISPRS International Journal of Geo-Information},
   title = {Enhancing Agricultural Productivity: Integrating Remote Sensing Techniques for Cotton Yield Monitoring and Assessment},
   url = {https://www.mdpi.com/2220-9964/13/10/340},
   volume = {13},
   year = {2024}
}

TY  - JOUR
ID  - 2459461
AU  - Aghayev, Amil - Řezník, Tomáš - Konečný, Milan
PY  - 2024
TI  - Enhancing Agricultural Productivity: Integrating Remote Sensing Techniques for Cotton Yield Monitoring and Assessment
JF  - ISPRS International Journal of Geo-Information
VL  - 13
IS  - 10
SP  - 1-19
EP  - 1-19
PB  - MDPI
SN  - 22209964
KW  - remote sensing
KW  - geographic ınformation systems
KW  - soil
KW  - cotton
KW  - vegetation indices
KW  - correlation
UR  - https://www.mdpi.com/2220-9964/13/10/340
N2  - This study assesses soil productivity in a 15-hectare cotton field using an integrated approach combining field data, laboratory analysis, and remote sensing techniques. Soil samples were collected and analyzed for key parameters including nitrogen (N), humus, phosphorus (P2O5), potassium (K2O), carbonates, pH, and electrical conductivity (EC). In addition to low salinity, these analyses showed low results for humus and nutrient parameters. A Pearson correlation analysis showed that low organic matter and high salinity had a strong negative correlation with crop productivity, explaining 37% of the variation in NDVI values. Remote sensing indices (NDVI, SAVI, NDMI, and NDSI) confirmed these findings by highlighting the relationship between soil properties and spectral reflectance. This research demonstrates the effectiveness of remote sensing in soil assessment, emphasizing its critical role in sustainable agricultural planning. By integrating traditional methods with advanced remote sensing technologies, this study provides actionable insights for policymakers and practitioners to improve soil productivity and ensure food security.
ER  -

AGHAYEV, Amil; Tomáš ŘEZNÍK a Milan KONEČNÝ. Enhancing Agricultural Productivity: Integrating Remote Sensing Techniques for Cotton Yield Monitoring and Assessment. \textit{ISPRS International Journal of Geo-Information}. MDPI, 2024, roč.~13, č.~10, s.~1-19. ISSN~2220-9964. Dostupné z: https://doi.org/10.3390/ijgi13100340.

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