Assessment of soil impact on pre- and post-harvest NDVI extrema by machine learning
More details
Hide details
Zakład Gleboznawstwa Erozji i Ochrony Gruntów, Instytut Uprawy Nawożenia i Gleboznawstwa - Państwowy Instytut Badawczy, Polska
Submission date: 2023-11-22
Final revision date: 2024-03-15
Acceptance date: 2024-06-01
Online publication date: 2024-06-01
Publication date: 2024-06-01
Corresponding author
Artur Łopatka   

Zakład Gleboznawstwa Erozji i Ochrony Gruntów, Instytut Uprawy Nawożenia i Gleboznawstwa - Państwowy Instytut Badawczy, Czartoryskich 8, 24-100, Puławy, Polska
Soil Sci. Ann., 2024, 75(2)189540
It was observed that the difference in the maximum and minimum NDVI values at a time close to harvest (mxNDVI and mnNDVI, respectively), referred to as the haNDVI index (harvest amplitude of NDVI), correlates with agricultural soil quality and the share of sowings. The NDVI becomes saturated when the values of the Leaf Area Index (LAI) significantly exceed one so spatial variation in haNDVI is mainly due to the minimum post-harvest NDVI (mnNDVI). To explain the variability of mnNDVI values three hypotheses were formulated: i) impact of crop selection, ii) field size impact, and iii) impact of soil. To determine which of these hypotheses had the highest impact on the variation in the mnNDVI, the developed machine learning models of this indicator were subjected to a test removing individual explanatory variables from them. Removing a variable does not cause a significant increase in model error if a variable does not contribute useful information to the model. This test showed that the mnNDVI index depends almost exclusively on the crop indicator which was the median of mnNDVI for crops, not directly from soil variables such as the agricultural quality of soil or soil moisture. According to this, the hypothesis of direct impact of soil was rejected. The explanation for the observed correlation of haNDVI with soil quality is the agricultural practice of choosing crops with low mnNDVI (cereals, rapeseed) at better soil conditions and crops with high mnNDVI (fodder crops, grassland) for worse soil conditions.
ADMS IUNG, 2023. Agricultural Drought Monitoring System, Soil moisture monitoring.
Angström, A., 1925. The albedo of various surfaces of ground. Geografiska Annaler 7, 323-327.
Breiman L., 2001. Random Forests. Machine Learning, 45, 5–32.
Faber, A., 2007. Przegląd wskaźników rolnośrodowiskowych zalecanych do stosowania w ocenie zrównoważonego gospodarowania w rolnictwie. [In:] Harasim, A. (Eds.) Sprawdzenie przydatności wskaźników do oceny zrównoważonego gospodarowania zasobami środowiska rolniczego w wybranych gospodarstwach, gminach i województwach. „Studia i Raporty IUNG-PIB” 5, 9-24. (in Polish)
Friedman, J., Tibshirani, R., Hastie, T., 2010. Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software 33(1), 1–22.
Gao, F., Anderson, M.C., Hively, W.D., 2020. Detecting cover crop end-of-season using VENµS and Sentinel-2 Satellite imagery. Remote Sensing 12, 3524.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., Moore, R., 2017. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202, 18–27.
Harasim, A., 2013. Metoda oceny zrównoważonego rolnictwa na poziomie gospodarstwa rolnego. Studia i Raporty IUNG-PIB 32(6), 25-75. (in Polish)
Jędrejek, A., Jadczyszyn, J., Pudełko, R., 2023. Increasing accuracy of the soil-agricultural map by Sentinel-2 images analysis—Case study of maize cultivation under drought conditions. Remote Sensing 15, 1281.
Jones, H.G., Vaughan, R.A., 2010. Remote sensing of vegetation – Principles, techniques, and applications. Oxford University Press, New York, 353 pp., ISBN: 9780199207794.
Julien, Y., Sobrino, J. A., 2008. NDVI seasonal amplitude and its variability. International Journal of Remote Sensing 29(17-18), 4887-4888.
Kuhn, M., 2008. Building Predictive Models in R Using the caret Package. Journal of Statistical Software 28(5), 1–26.
Liaw, A., Wiener, M., 2002. Classification and Regression by randomForest. R News, 2(3), 18-22.
Liu, R, 2017. Compositing the Minimum NDVI for MODIS Data. [In:] IEEE Transactions on Geoscience and Remote Sensing 55(3), 1396-1406.
Liu, W., Baret, F., Gu, X.F., Tong, Q., Zheng, L., Zhang, B., 2002. Relating soil surface moisture to reflectance. Remote Sensing of Environment 81, 238-246.
Lobell, D., Asner, G., 2002. Moisture Effects on Soil Reflectance. Soil Science Society of America Journal 66, 722-727.
MChAS 2023, Monitoring Chemizmu Gleb Ornych Polski, GIOŚ.
Łopatka, A., Koza, P., 2020. Crop production intensity and haNDVI indicator – amplitude of NDVI related to harvest. Polish Journal of Agronomy 42, 24–33.
Panek, E., Gozdowski, D., 2020. Analysis of relationship between cereal yield and NDVI for selected regions of Central Europe based on MODIS satellite data. Remote Sensing Applications: Society and Environment 17, 100286.
R Core Team, 2023. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
Riley, S., Degloria, S., Elliot, S.D., 1999. A terrain ruggedness index that quantifies topographic heterogeneity. International Journal of Science 5, 23-27.
Rouse, J.W, Haas, R.H., Scheel, J.A., and Deering, D.W., 1974. Monitoring Vegetation Systems in the Great Plains with ERTS. Proceedings, 3rd Earth Resource Technology Satellite (ERTS) Symposium, vol. 1, p. 48-62.
Sicre, M.C., Inglada, J., Fieuzal, R., Baup, F., Valero, S., Cros, J., Huc, M., Demarez, V., 2016. Early detection of summer crops using high spatial resolution optical image time series. Remote Sensing 8, 591.
Schauberger, B., Archontoulis, S., Arneth, A. et al., 2017. Consistent negative response of US crops to high temperatures in observations and crop models. Nature Communications 8, 13931.
Tenreiro, T.R., García-Vila, M., Gómez, J.A., Jiménez-Berni, J.A., Fereres, E., 2021. Using NDVI for the assessment of canopy cover in agricultural crops within modelling research. Computers and Electronics in Agriculture 182, 106038.
Tibshirani, R., 1996. Regression Shrinkage and Selection via the lasso. Journal of the Royal Statistical Society. Series B (methodological), 58(1), 267–88.
Tucker C.J., 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing and Environment 8, 127–150.
Witek, T., 1993. Waloryzacja rolniczej przestrzeni produkcyjnej Polski według gmin. IUNG Puławy, Seria (A) 56. (in Polish).
Zhao, Q., Yu, L., Li, X., Peng, D., Zhang, Y., Gong, P., 2021. Progress and trends in the application of Google Earth and Google Earth Engine. Remote Sensing 13, 3778.
Journals System - logo
Scroll to top