{"query_echo":{"q":"","filters":{"analysis_type":["spectroscopic analysis"]},"warnings":[{"code":"facet_value_not_in_vocabulary","message":"Value 'spectroscopic analysis' for facet 'analysis_type' is not in the controlled vocabulary."}]},"total":{"value":1,"relation":"eq"},"page":1,"page_size":20,"self":"https://climateinsightsdirectory.org/api/v1/search?analysis_type=spectroscopic+analysis","stability":"beta","ai_marking":{"digital_source_type":{"uri":"http://cv.iptc.org/newscodes/digitalsourcetype/trainedAlgorithmicMedia","name":"Created using Generative AI"},"notice":"Summary and key insights are generated using artificial intelligence."},"results":[{"id":"iai-000079","url":"https://climateinsightsdirectory.org/document/iai-000079","title":"Spectroscopic determination of leaf water content using continuous wavelet analysis","publisher":{"name":"Inter-American Institute for Global Change Research","organization":"inter-american-institute-for-global-change-research"},"document_type":"research paper","language":"en","date_published":"2025-01-17","source_url":"https://iai.int/es/pub_acad/spectroscopic-determination-of-leaf-water-content-using-continuous-wavelet-analysis/","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","analysis_type"],"summary":{"text":"The study demonstrates that continuous wavelet analysis of reflectance spectra (350–2500 nm) can more accurately estimate leaf gravimetric water content (GWC) than established spectral indices, particularly by identifying features in the shortwave infrared region related to dry matter absorption.","ai_generated":true,"unit":"iai-000079#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Eight wavelet features identified between 1300 and 2500 nm showed strong correlations with leaf water content expressed as a function of dry mass (LWCD), whereas established spectral indices showed poor correlations.","ai_generated":true,"unit":"iai-000079#insight-1","date_modified":"2026-10-23"},{"text":"The wavelet features captured both amplitude variations in the overall reflectance spectra and specific variations in the shape and depth of dry matter absorptions, such as cellulose, lignin, and protein, centered around 1730 and 2100 nm.","ai_generated":true,"unit":"iai-000079#insight-2","date_modified":"2026-10-23"},{"text":"Predictive models using a combination of wavelet features improved the accuracy of estimating leaf water content, achieving root mean square errors (RMSEs) of 26.04% (R2 = 0.71) for LWCD and 4.34% (R2 = 0.75) for LWCF.","ai_generated":true,"unit":"iai-000079#insight-3","date_modified":"2026-10-23"}],"climate_tags":[],"geography_discussed":[],"frame_perspective":[],"analysis_type":["spectroscopic analysis","predictive modeling"]}]}