{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:Q3L3Y63WKDXCHTQY3PYRCPDH2M","short_pith_number":"pith:Q3L3Y63W","schema_version":"1.0","canonical_sha256":"86d7bc7b7650ee23ce18dbf1113c67d310a10d3b5cd9997f56a62b37017cb156","source":{"kind":"arxiv","id":"1501.07683","version":1},"attestation_state":"computed","paper":{"title":"Downscaling Microwave Brightness Temperatures Using Self Regularized Regressive Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anand Rangarajan, Jasmeet Judge, Sanjay Ranka, Subit Chakrabarti","submitted_at":"2015-01-30T07:24:44Z","abstract_excerpt":"A novel algorithm is proposed to downscale microwave brightness temperatures ($\\mathrm{T_B}$), at scales of 10-40 km such as those from the Soil Moisture Active Passive mission to a resolution meaningful for hydrological and agricultural applications. This algorithm, called Self-Regularized Regressive Models (SRRM), uses auxiliary variables correlated to $\\mathrm{T_B}$ along-with a limited set of \\textit{in-situ} SM observations, which are converted to high resolution $\\mathrm{T_B}$ observations using biophysical models. It includes an information-theoretic clustering step based on all auxilia"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1501.07683","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-01-30T07:24:44Z","cross_cats_sorted":[],"title_canon_sha256":"e62d803edc3e50581d52d89f430b8594810cccb31b81100bcfb65080612b689c","abstract_canon_sha256":"aeeb140d7a7daa565e56353c419aa47e343e90224fd30b16a8848c80f4c21178"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:28:14.409940Z","signature_b64":"HtYOhvRsctdWbOc5cAhmURztqiJUibCsHbeB7yfG+u5TDPz+SQOEV2/wAmSBdkaewYccz3339O6oabGrD+nnDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"86d7bc7b7650ee23ce18dbf1113c67d310a10d3b5cd9997f56a62b37017cb156","last_reissued_at":"2026-05-18T02:28:14.409025Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:28:14.409025Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Downscaling Microwave Brightness Temperatures Using Self Regularized Regressive Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anand Rangarajan, Jasmeet Judge, Sanjay Ranka, Subit Chakrabarti","submitted_at":"2015-01-30T07:24:44Z","abstract_excerpt":"A novel algorithm is proposed to downscale microwave brightness temperatures ($\\mathrm{T_B}$), at scales of 10-40 km such as those from the Soil Moisture Active Passive mission to a resolution meaningful for hydrological and agricultural applications. This algorithm, called Self-Regularized Regressive Models (SRRM), uses auxiliary variables correlated to $\\mathrm{T_B}$ along-with a limited set of \\textit{in-situ} SM observations, which are converted to high resolution $\\mathrm{T_B}$ observations using biophysical models. It includes an information-theoretic clustering step based on all auxilia"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1501.07683","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1501.07683","created_at":"2026-05-18T02:28:14.409183+00:00"},{"alias_kind":"arxiv_version","alias_value":"1501.07683v1","created_at":"2026-05-18T02:28:14.409183+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1501.07683","created_at":"2026-05-18T02:28:14.409183+00:00"},{"alias_kind":"pith_short_12","alias_value":"Q3L3Y63WKDXC","created_at":"2026-05-18T12:29:37.295048+00:00"},{"alias_kind":"pith_short_16","alias_value":"Q3L3Y63WKDXCHTQY","created_at":"2026-05-18T12:29:37.295048+00:00"},{"alias_kind":"pith_short_8","alias_value":"Q3L3Y63W","created_at":"2026-05-18T12:29:37.295048+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Q3L3Y63WKDXCHTQY3PYRCPDH2M","json":"https://pith.science/pith/Q3L3Y63WKDXCHTQY3PYRCPDH2M.json","graph_json":"https://pith.science/api/pith-number/Q3L3Y63WKDXCHTQY3PYRCPDH2M/graph.json","events_json":"https://pith.science/api/pith-number/Q3L3Y63WKDXCHTQY3PYRCPDH2M/events.json","paper":"https://pith.science/paper/Q3L3Y63W"},"agent_actions":{"view_html":"https://pith.science/pith/Q3L3Y63WKDXCHTQY3PYRCPDH2M","download_json":"https://pith.science/pith/Q3L3Y63WKDXCHTQY3PYRCPDH2M.json","view_paper":"https://pith.science/paper/Q3L3Y63W","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1501.07683&json=true","fetch_graph":"https://pith.science/api/pith-number/Q3L3Y63WKDXCHTQY3PYRCPDH2M/graph.json","fetch_events":"https://pith.science/api/pith-number/Q3L3Y63WKDXCHTQY3PYRCPDH2M/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Q3L3Y63WKDXCHTQY3PYRCPDH2M/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Q3L3Y63WKDXCHTQY3PYRCPDH2M/action/storage_attestation","attest_author":"https://pith.science/pith/Q3L3Y63WKDXCHTQY3PYRCPDH2M/action/author_attestation","sign_citation":"https://pith.science/pith/Q3L3Y63WKDXCHTQY3PYRCPDH2M/action/citation_signature","submit_replication":"https://pith.science/pith/Q3L3Y63WKDXCHTQY3PYRCPDH2M/action/replication_record"}},"created_at":"2026-05-18T02:28:14.409183+00:00","updated_at":"2026-05-18T02:28:14.409183+00:00"}