TY - JOUR

T1 - Analyses of the Impact of Soil Conditions and Soil Degradation on Vegetation Vitality and Crop Productivity Based on Airborne Hyperspectral VNIR–SWIR–TIR Data in a Semi-Arid Rainfed Agricultural Area (Camarena, Central Spain)

AU - Milewski, Robert

AU - Schmid, Thomas

AU - Chabrillat, Sabine

AU - Jiménez, Marcos

AU - Escribano, Paula

AU - Pelayo, Marta

AU - Ben-Dor, Eyal

N1 - Funding Information: The authors thank the EUFAR program for supporting and promoting this work under EU-FP7 EUFAR (European Facility for Airborne Research). We would like to sincerely thank the INTA flight operators and remote sensing group for the support and efficient management of the hyperspectral AHS and CASI data acquisition and pre-processing. Further thanks go to Veronica Sobejano Paz, Andrés Reyes (R.I.P.), and Natalia Ramírez for support with the field work and Daniel Berger for his help with field data handling. We thank the EnMAP science program funded by the German Federal Ministry of Economics and Technology and institutional support by the GFZ Potsdam and CIEMAT for further promoting this work. Acknowledgment: The authors acknowledge the grant (No 312609) obtained for the 2017 field and airborne campaign from the EUropean Facility for Airborne Research (EUFAR) Transnational Access program based on the project MASOMED—Mapping SOil variability within rainfed MEDiterranean agroecosystems using hyperspectral data.

PY - 2022/10

Y1 - 2022/10

N2 - Soils are an essential factor contributing to the agricultural production of rainfed crops such as barley and triticale cereals. Changing environmental conditions and inadequate land management are endangering soil quality and productivity and, in turn, crop quality and productivity are affected. Advances in hyperspectral remote sensing are of great use for the spatial characterization and monitoring of the soil degradation status, as well as its impact on crop growth and agricultural productivity. In this study, hyperspectral airborne data covering the visible, near-infrared, short-wave infrared, and thermal infrared (VNIR–SWIR–TIR, 0.4–12 µm) were acquired in a Mediterranean agricultural area of central Spain and used to analyze the spatial differences in vegetation vitality and grain yield in relation to the soil degradation status. Specifically, leaf area index (LAI), crop water stress index (CWSI), and the biomass of the crop yield are derived from the remote sensing data and discussed regarding their spatial differences and relationship to a classification of erosion and accumulation stages (SEAS) based on previous remote sensing analyses during bare soil conditions. LAI and harvested crop biomass yield could be well estimated by PLS regression based on the hyperspectral and in situ reference data (R2 of 0.83, r of 0.91, and an RMSE of 0.2 m2 m−2 for LAI and an R2 of 0.85, r of 0.92, and an RMSE of 0.48 t ha−1 for grain yield). In addition, the soil erosion and accumulation stages (SEAS) were successfully predicted based on the canopy spectral signal of vegetated crop fields using a random forest machine learning approach. Overall accuracy was achieved above 71% by combining the VNIR–SWIR–TIR canopy reflectance and emissivity of the growing season with topographic information after reducing the redundancy in the spectral dataset. The results show that the estimated crop traits are spatially related to the soil’s degradation status, with shallow and highly eroded soils, as well as sandy accumulation zones being associated with areas of low LAI, crop yield, and high crop water stress. Overall, the results of this study illustrate the enormous potential of imaging spectroscopy for a combined analysis of the plant-soil system in the frame of land and soil degradation monitoring.

AB - Soils are an essential factor contributing to the agricultural production of rainfed crops such as barley and triticale cereals. Changing environmental conditions and inadequate land management are endangering soil quality and productivity and, in turn, crop quality and productivity are affected. Advances in hyperspectral remote sensing are of great use for the spatial characterization and monitoring of the soil degradation status, as well as its impact on crop growth and agricultural productivity. In this study, hyperspectral airborne data covering the visible, near-infrared, short-wave infrared, and thermal infrared (VNIR–SWIR–TIR, 0.4–12 µm) were acquired in a Mediterranean agricultural area of central Spain and used to analyze the spatial differences in vegetation vitality and grain yield in relation to the soil degradation status. Specifically, leaf area index (LAI), crop water stress index (CWSI), and the biomass of the crop yield are derived from the remote sensing data and discussed regarding their spatial differences and relationship to a classification of erosion and accumulation stages (SEAS) based on previous remote sensing analyses during bare soil conditions. LAI and harvested crop biomass yield could be well estimated by PLS regression based on the hyperspectral and in situ reference data (R2 of 0.83, r of 0.91, and an RMSE of 0.2 m2 m−2 for LAI and an R2 of 0.85, r of 0.92, and an RMSE of 0.48 t ha−1 for grain yield). In addition, the soil erosion and accumulation stages (SEAS) were successfully predicted based on the canopy spectral signal of vegetated crop fields using a random forest machine learning approach. Overall accuracy was achieved above 71% by combining the VNIR–SWIR–TIR canopy reflectance and emissivity of the growing season with topographic information after reducing the redundancy in the spectral dataset. The results show that the estimated crop traits are spatially related to the soil’s degradation status, with shallow and highly eroded soils, as well as sandy accumulation zones being associated with areas of low LAI, crop yield, and high crop water stress. Overall, the results of this study illustrate the enormous potential of imaging spectroscopy for a combined analysis of the plant-soil system in the frame of land and soil degradation monitoring.

KW - crop productivity

KW - hyperspectral imagery

KW - LAI

KW - Mediterranean

KW - soil degradation

KW - vegetation traits

KW - water stress

UR - http://www.scopus.com/inward/record.url?scp=85140963219&partnerID=8YFLogxK

U2 - 10.3390/rs14205131

DO - 10.3390/rs14205131

M3 - Article

AN - SCOPUS:85140963219

VL - 14

JO - Remote sensing

JF - Remote sensing

SN - 2072-4292

IS - 20

M1 - 5131

ER -