Sains Malaysiana 55(10)(2026): 1690-1702

http://doi.org/10.17576/jsm-2026-5510-08

 

Predictive Reliability of Denoised QM-Corrected Satellite-Derived Photosynthetically Active Radiation (PAR) under Humid Tropical Conditions

(Kebolehpercayaan Ramalan bagi Sinaran Aktif Fotosintesis (PAR) Terbitan Satelit yang Telah Dibuang Hingar dan Dibetulkan secara QM di bawah Keadaan Tropika Lembap)

 

ROHAIZA DAUD^1,2,*, FARRAH MELISSA MUHARAM¹, RADHWANE DERRAZ³, NGAI PAING TAN⁴ & NOR AZURA HUSIN⁵

 

¹Department of Agriculture Technology, Faculty of Agriculture, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia

²Centre for Pre-University Studies, Universiti Malaysia Sarawak (UNIMAS), 94300 Kota Samarahan, Sarawak, Malaysia

³School of Computing, College of Arts and Sciences, Universiti Utara Malaysia, 06010 Sintok, Kedah, Malaysia

⁴Department of Land Management, Faculty of Agriculture, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia

⁵Department of Computer Science, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia

 

Diserahkan: 12 Jun 2026/Diterima: 20 September 2026

 

*Pengarang untuk surat-menyurat; email: drohaiza@unimas.my

 

Abstract

Satellite-derived photosynthetically active radiation (PAR) products provide valuable information for environmental monitoring and agricultural applications, but residual fluctuations may remain even after bias correction, potentially affecting predictive reliability. This study evaluated the influence of multiple denoising approaches, including decomposition-, filtering-, smoothing-, and learning-based methods, on the predictive performance of quantile mapping (QM)-corrected satellite-derived PAR under humid tropical conditions in Peninsular Malaysia. MODIS MCD18A2 Version 061 PAR data were obtained for 14 meteorological stations between 2015 and 2023 and corrected using QM prior to denoising. Predictive performance was assessed using five regression models. Most denoising approaches improved predictive performance relative to the baseline QM-corrected dataset, although the magnitude of improvement varied considerably among methods. Median filtering paired with Random Forest produced the highest mean R² (0.748), with a mean MSE of 0.293. Although LSTM yielded a lower numerical MSE (0.004), its lower mean R² (0.542) and nearly flat reconstruction indicated variance compression rather than faithful preservation of PAR dynamics. Median filtering was therefore selected as the most reliable overall method. In contrast, Empirical Mode Decomposition (EMD) exhibited inconsistent performance across stations and substantially larger prediction errors. The findings demonstrate that denoising can substantially enhance the predictive reliability of satellite-derived PAR. More importantly, the results suggest that predictive reliability is influenced more by effective signal preservation than by algorithmic complexity alone. Under humid tropical conditions, relatively simple denoising approaches may provide more robust improvements than computationally intensive alternatives for PAR-based environmental modelling applications.

Keywords: Denoising; machine learning; MODIS MCD18A2; photosynthetically active radiation; quantile mapping

 

Abstrak

Produk sinaran aktif fotosintesis (PAR) berasaskan satelit menyediakan maklumat penting untuk pemantauan alam sekitar dan aplikasi pertanian, namun lebihan turun naik masih boleh wujud walaupun selepas pembetulan bias, sekali gus berpotensi menjejaskan kebolehpercayaan ramalan. Kajian ini menilai pengaruh pelbagai pendekatan penyahhingaran, termasuk kaedah berasaskan penguraian, penapisan, pelicinan dan pembelajaran, terhadap prestasi ramalan PAR berasaskan satelit yang telah diperbetulkan menggunakan pemetaan kuantil (QM) di bawah keadaan tropika lembap di Semenanjung Malaysia. Data PAR MODIS MCD18A2 Versi 061 diperoleh bagi 14 stesen meteorologi untuk tempoh 2015 hingga 2023 dan diperbetulkan menggunakan QM sebelum proses penyahhingaran dijalankan. Prestasi ramalan dinilai menggunakan lima model regresi. Kebanyakan kaedah penyahhingaran meningkatkan prestasi ramalan berbanding set data asas yang telah diperbetulkan menggunakan QM, walaupun tahap peningkatan berbeza antara kaedah. Penapisan median yang digandingkan dengan hutan rawak menghasilkan purata R² tertinggi (0.748) dan purata MSE sebanyak 0.293. Walaupun LSTM menghasilkan MSE yang lebih rendah (0.004), purata R² yang lebih rendah (0.542) dan pembinaan semula hampir mendatar menunjukkan pemampatan varians, bukannya pengekalan dinamik PAR. Oleh itu, penapisan median dipilih sebagai kaedah yang paling boleh dipercayai secara keseluruhan. Sebaliknya, Penguraian Mod Empirik (EMD) menunjukkan prestasi yang tidak tekal antara stesen serta menghasilkan ralat ramalan yang lebih tinggi. Keputusan kajian menunjukkan bahawa penyahhingaran dapat meningkatkan kebolehpercayaan ramalan PAR berasaskan satelit. Yang lebih penting, kebolehpercayaan ramalan didapati lebih dipengaruhi oleh pemeliharaan maklumat temporal yang bermakna berbanding kerumitan algoritma semata-mata. Di bawah keadaan tropika lembap, pendekatan penyahhingaran yang lebih ringkas berpotensi memberikan peningkatan yang lebih mantap bagi aplikasi pemodelan alam sekitar berasaskan PAR.

Kata kunci: MODIS MCD18A2; pembelajaran mesin; pemetaan kuantil; penyahhingaran; sinaran aktif fotosintesis

 

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