Szervesszén térképezést segítő módszertani kutatásként vizsgáltuk egy szántóföldi művelés alatt álló terület, 3 mélységből származó mintáinak MIR reflektanciáját, illetve szervesszén tartalmát (Walkley-Black). Ezt követően a spektroszkópia mérések eredményeit használtuk a talaj szervesszén-mennyiségének (TOC %) becslésére. Tettük ezt 3 mintakijelölési módszer (Kennard-Stone Sampling - KSS, K-means Sampling - KMS, Latin Hypercube Sampling - LHS) bevonásával, az így kijelölt kalibrációs mintákkal a PLSR modell segítségével becslést végeztünk az adathalmaz további értékeire. Annak érdekében, hogy tábla szintű szervesszén meghatározás során teszteljük becslési pontosságukat, a modellek reprezentativitását – különböző validációs/kalibrációs arány esetén – statisztikai mutatókkal (R2, RMSE) ellenőriztük.
Az eredményekben részleteiben vizsgáltuk a különböző becslési modellek reakcióját eltérő arányú kalibráció és validáció esetén. A modellek R2 és RMSE értékei alapján kijelöltük, hogy mely modellek működtek pontosan még alacsony kalibráció esetén is, illetve abszolút értelemben véve melyik modell volt leghatékonyabb.
Az összehasonlítás eredményeként kijelenthető, hogy az általunk vizsgált talajkörülmények között a 30% alatti, valamint a 70% feletti mintaszámú kalibráció a mintakijelölési módszerek megbízhatóságának ingadozását eredményezte. Az összes minta 30%-val történő kalibráció esetén legjobb eredményt a KSS adta, így ez tekinthető a leggazdaságosabb módszernek. Az abszolút értékben vett legkisebb hibát a K-means sampling eredményezte, a minták 90%-val történő kalibrációt követően.
Kijelenthető, hogy az alkalmazott módszertan esetünkben alkalmas volt – a reprezentativitás megtartása mellett – a szükséges minták számának, ergo a táblaszintű szervesszén-felmérés költségeinek csökkentésére. Továbbá a mintakijelölési módszerek becslési hatékonyságának összehasonlítására is megfelelt az általunk alkalmazott statisztikai vizsgálat. A módszertan a jövőben kiinduló alapja lehet hasonló jellegű kutatásoknak, valamint tábla szintű szervesszéntérképek elkészítésének. A szélesebb körű alkalmazást megelőzően a modelleket nagyobb varianciájú adathalmazok esetén is tesztelni szükséges.
Within the framework of the present research, we mapped the organic carbon content of an arable area, during which we measured the MIR reflectance and organic carbon content (Walkley-Black) of the soil samples collected from the area at three different depths. Subsequently, the results of spectroscopic measurements were used to improve the estimation of the soil organic carbon content (TOC %). Three sample selection models were involved (KSS, KMS, LHS), and with the selected calibration samples, we estimated the additional values of the data set using the PLSR model. In order to test the accuracy of estimation for a table-level organic carbon determination, the representativeness level of the models was checked with statistical indicators (R2, RMSE) at different validation / calibration ratios.
In the results, we thoroughly examined the response of different estimation models with different ratios of calibration and validation. Based on the R2 and RMSE values of the models, we determined which models worked precisely even at low calibration, and in absolute terms, which model was the most efficient.
As a result of the comparison, it can be stated that under the soil conditions we examined, calibration with a sample number below 30% and above 70% caused significant fluctuations in the reliability of the sampling methods. Kennard-Stone sampling (KSS) gave the most precise results for calibration with 30% of all samples, thus it is considered the most economical method. The smallest error overall was given by K-means sampling after calibration of 90% of the samples.
It can be stated that the methodology used in this study was suitable to reduce the samples required for analysis - while maintaining representativeness - therefore reducing the costs of the field-level organic carbon survey.
Furthermore, the statistical analysis we used to compare the estimation efficiency of the sampling methods was also appropriate. The methodology we use may be the basis for similar research in the future, as well as for the production of table-level organic carbon maps. Prior to wider application, models also need to be tested for higher variance datasets.
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