Remote Sensing for Soil Organic Carbon Mapping and Monitoring

Wesemael, Bas van; Chabrillat, Sabine; Dias, Ana Carolina Lucena; Berger, Michael; Szantoi, Zoltan

doi:10.3390/rs15143464

Cited by 8 publications

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A comparison of the average CV% distributions shows that CMAC performed better at the lowest reflectance levels. CMAC provides higher precision for the low reflectance of the red band and for the moderately high levels of NIR, both forming the indices for assessing vegetation performance for precision agriculture, measuring carbon cycling [25,26] and potentially for AI feature extraction [27,28]. The slightly higher dispersion (~1-2%) observed in the central portions of the CMAC-corrected visible band distributions may result from the relatively cursory L8/9 calibration.…”

Section: Resultsmentioning

confidence: 99%

An Algorithm Developed for Smallsats Accurately Retrieves Landsat Surface Reflectance Using Scene Statistics

Groeneveld,

Ruggles

2023

Applied Sciences

View full text Add to dashboard Cite

Closed-form Method for Atmospheric Correction (CMAC) is software that overcomes radiative transfer method problems for smallsat surface reflectance retrieval: unknown sensor radiance responses because onboard monitors are omitted to conserve size/weight, and ancillary data availability that delays processing by days. CMAC requires neither and retrieves surface reflectance in near real time, first mapping the atmospheric effect across the image as an index (Atm-I) from scene statistics, then reversing these effects with a closed-form linear model that has precedence in the literature. Five consistent-reflectance area-of-interest targets on thirty-one low-to-moderate Atm-I images were processed by CMAC and LaSRC. CMAC retrievals accurately matched LaSRC with nearly identical error profiles. CMAC and LaSRC output for paired images of low and high Atm-I were then compared for three additional consistent-reflectance area-of-interest targets. Three indices were calculated from the extracted reflectance: NDVI calculated with red (standard) and substitutions with blue and green. A null hypothesis for competent retrieval would show no difference. The pooled error for the three indices (n = 9) was 0–3% for CMAC, 6–20% for LaSRC, and 13–38% for uncorrected top-of-atmosphere results, thus demonstrating both the value of atmospheric correction and, especially, the stability of CMAC for machine analysis and AI application under increasing Atm-I from climate change-driven wildfires.

show abstract